Headshots of female Amazon scientists participating in the Grace Hopper Conference.
Amazon scientists (from top left) Kristine Brown, Laura De Lorenzo, Yang Liu, Hannah Marlowe, Nina Mishra, Candace Thille, and Chao Wang provide their perspectives on what it will take to attract more women to pursue STEM careers.
Credit: Stacy Reilly

Seeds of inspiration

Given the recent death of US Supreme Court Justice Ruth Bader Ginsburg, and with the Grace Hopper Celebration taking place this week, we asked Amazon women scientists what it will take to attract more women to pursue STEM careers.

The AnitaB.org Grace Hopper Celebration, an event honoring Grace Hopper’s legacy by inspiring future generations of women to pursue careers in technology, takes place this week, as it has every year since 1994. Amazon is a Diamond sponsor of this year’s event.

Unlike previous years, though, this year’s celebration, which AnitaB.org produces in partnership with the Association for Computing Machinery (ACM), will be held virtually given restrictions related to COVID-19.  What hasn’t changed is the vision of AnitaB.org: a future “where the people who imagine and build technology mirror the people and societies for whom they build it.”

Based on the latest statistics from the National Center for Women & Information Technology, that future is still on the horizon. While 57 percent of US professional jobs were held by women in 2019, just 26% of professional computing jobs were occupied by women. Among the 26% of women occupying professional computing jobs, 7% were Asian women, 3% Black women, and 2% Hispanic women.

Elizabeth Nieto, Amazon’s head of global diversity and inclusion, says the company’s vision is to create a culture where the best builders, including women from all backgrounds, want to work and stay at Amazon “because they are drawn to our mission, our culture, and our leaders. We are focused on being globally inclusive and creating a culture at Amazon where everyone can reach their full potential.”

At last year’s event, Brenda Darden Wilkerson, president and CEO of AnitaB.org, told nearly 25,000 attendees, “I want our daughters to say, ‘I heard back in the day there was this problem that there weren’t enough women in tech.  What was that like?’”

In advance of this week's conference, Amazon Science asked some of the company’s women scientists when they think the industry will reach that goal, what it will take to get there, and who or what most inspired them to pursue their science careers.  Below are their responses.

Kristine Brown is a principal economist within Amazon’s human resources organization. She obtained her PhD in economics from the University of California, Berkeley.

Kristine Brown
Kristine Brown

Q. When do you think we'll reach that day that Brenda Wilkerson talked about last year?

At Amazon, I learned the importance of continuous inspection to identify opportunities for improvement, and to adapt to a shifting environment. I think the same applies here; the task of deliberately creating opportunities for others, and removing barriers to shape a more equitable and inclusive workplace will evolve over time, but it doesn’t have an end date.

Q. What will it take to get there?

The demand for science and tech talent is increasing in the traditional technology sector and in other industries that are leveraging new technologies and data to provide better services and products. The door is wide open, but you can’t walk in if you don’t know it exists, or how to get there. For me, early exposure and encouragement to explore science and math were critical. I discovered a passion for physics and that interest pushed me to develop my math and science skills. I was lucky to have this opportunity. Casting a wider net to provide early, low stakes opportunities to engage in science and tech activities, develop STEM skills, and learn about the diversity of work in this space, will help demystify the technology industry. It will also allow kids and young adults to learn whether it matches their interests and whether they have a knack for it.

Q. Who or what inspired you most to pursue your STEM career?

My fascination with the natural world was fueled by observing wildlife, peering through an observatory telescope at distant planets, and nature magazines with beautiful photos. The mind-bending questions of space and time were especially irresistible; I wanted the answers to the universe, and physics and math were the key to finding them. Later, as I became interested in understanding human behavior (which I’d argue is no less mysterious) and how government policies could improve lives, I found economics came with a familiar toolkit of mathematical modeling and scientific testing to answer these questions. I saw a career in economics as an opportunity to leverage my strengths to drive positive change.

Laura De Lorenzo is a quantum computing research scientist within the Amazon Web Services organization. She earned her PhD in applied physics from the California Institute of Technology (CalTech).

Laura De Lorenzo
Laura De Lorenzo

Q. When do you think we'll reach that day that Brenda Wilkerson talked about last year?

To be honest, I'm so uncertain as to be unwilling to hazard a guess, but I do think it is a long way off. In some STEM fields, such as medicine, the gender gap has nearly, or completely, closed within the past 50 years. In other fields, the percentage of women (measured by employment or educational degree) remains far below 50% and doesn't appear to be changing significantly year over year. The amount of progress in some fields is encouraging, but it's difficult to understand why fields like physics and computer science lag behind.  

Q. What will it take to get there?

This issue is clearly challenging and multi-faceted, so I cannot offer a single simple solution. However, I think one important aspect is a focus on young women, in the middle school to high school age group. For example, women are already underrepresented in the high school AP physics examinations. By the time students reach the undergraduate level, only about 20% of physics majors are female. I think it is essential to understand why young women make these choices. Is it a lack of role models, or self-doubt about their ability to perform well in science, or peer pressure, or something else entirely?  In the meantime, I think it is important to offer encouragement and support to young students because once women drop out of the STEM fields, it is more difficult for them to return at a later age.

Q. Who or what inspired you most to pursue your STEM career?

From a young age, my parents were always supportive of my interests in science and math, and of my career in general. My mother went to medical school in the late ‘70s, when women represented only about 20% of medical students in the US.  I always saw her as strong, hard-working, and independent, and she was a great example for me to follow. Both of my parents had high expectations for me and would never allow me to perform at less than my best. I definitely owe the largest debt of gratitude to them. However, programs such as Science Olympiad and the Pennsylvania Governor's School for Science (a five-week program for rising high school seniors), also helped me by introducing me to a peer group with similar interests, and to a larger group of role models and mentors who could help me navigate the next step.

Yang Liu is a principal scientist within the Alexa AI organization. She earned her PhD in electrical and computer engineering from Purdue University.

Yang Liu
Yang Liu

Q. When do you think we'll reach that day that Brenda Wilkerson talked about last year?

Maybe in another generation. My daughter is in first grade now. I’m hopeful we can reach that day when she finishes high school, and is choosing a college major or planning a career in STEM or the technology industry.

Q. What will it take to get there?

It will require effort from everyone in society, including educators, students, parents, and policy makers. Starting from kindergarten through high school, young girls and women need support and encouragement from parents and teachers to realize their potential and get excited by STEM careers; educators need to nurture girls’ interest in STEM and create an environment to help them do well in these subjects; and policy makers need to provide appropriate and adequate resources for teachers and students. As Hillary Clinton has written and said, it will take a village for society to address existing biases and prejudices. But with everyone’s effort, I’m confident we can get there by the time my daughter is entering the workforce.

Q. Who or what most inspired you to pursue your STEM career?

Mostly just people around me — my family, teachers from elementary schools all the way up to universities, and an overall supportive environment, including friends and peers. I grew up in China. My mom was a math teacher, and I did well in math starting in elementary school. All I got from everyone around me was support, respect, and encouragement to continue to excel in this subject. I never encountered an attitude like “girls are not good at math (or other science subjects) or don’t need to do well in math”. I made many friends (girls and boys) in schools, and was never left out because I did better than others in science. Reflecting on this, there’s no doubt I benefited from that supportive environment, leading to my future career in STEM. I don’t know for sure if there is a difference between China and US; I don’t have enough sample to draw a conclusion. I’m not even sure if there’s been a generational change within China. What I can say is that I would encourage girls and young women to pursue STEM careers.  The subjects themselves are fascinating. Right now I’m working within the Alexa organization on making computers and other devices “intelligent” by recognizing speech and understanding human language. The work is challenging, interesting, and it’s great to see how Alexa can have a positive impact on the lives of our customers. 

Hannah Marlowe is a senior data scientist within the AWS Worldwide Public Sector Professional Services Data and Machine Learning team. She earned her PhD in physics from the University of Iowa, specializing in the study of astronomical X-ray sources and space-borne instrumentation development.

Hannah Marlowe
Hannah Marlowe

Q. When do you think we'll reach that day that Brenda Wilkerson talked about last year?

The university building where I completed my PhD was an interesting time-capsule to observe some of the progress of women in physics and astronomy. The eight-level physics building, built in the ‘60s, originally featured only men’s restrooms apart from one. The lone women’s restroom was located across the hall from the administration office and included an attached kitchen (still there today), presumably so that secretaries working in the office could prepare meals during the work day. In years since, they have thankfully adjusted the restroom situation, but the basement where my team’s lab was located still only had a men’s room and it was always an interesting reminder of that past.

Today, the thought of designing a building with facilities only for men (much less a public university building) seems completely ridiculous, but it wasn’t so long ago that it apparently made practical sense. We are standing on the shoulders of giants like Ruth Bader Ginsburg and other advocates of gender equality who paved the way for the participation of women in traditionally male-dominated fields and shifted public perception of what women can and should do. It is my hope that we continue to build on the work they championed, but it will take a concerted effort. I don’t have a good answer for when I think we will get to the point that gender disparity in STEM fields is a distant memory. However, I have seen positive changes and witnessed shifts over my own career (not limited to restroom design choices) that make me optimistic that we can get there eventually.

Q. What will it take to get there?

I don’t believe there is any one right answer, but one of the most important things is making it clear to young girls and women that they belong and add value in STEM. I think people tend to gravitate to careers and roles that they have exposure to, and where they see role models that look like themselves. The other piece is not just encouraging girls and women to explore STEM, but expecting it and treating it like a normal career path versus an exceptional one. That is not to say we should be pushing girls to pursue something they aren’t interested in, but I hope that we get to a point where girls pursuing STEM seems completely boring and commonplace. That gets easier as more women enter STEM fields, and I think there is probably a tipping point where women and girls just naturally begin to gravitate in larger numbers to these fields. As a practical matter, we should also be equipping girls with all of the skills and tools that will make them successful in these fields from a young age. Anyone who isn’t exposed to math and science early is going to have to play catch-up later on, and may question their own abilities when they compare themselves to peers who have been in advanced math and science tracks throughout grade school.

Q. Who or what most inspired you to pursue your STEM career?

I feel extremely fortunate that I have mainly been able to follow my interests and what I found to be fun and personally challenging throughout school and my career so far. I also had many great influences and mentors in my life that helped me along my path. From an early age my father used to point out constellations in the sky and took my sister and me to observe comets and space shuttle launches. Once I got to high school, I had a wonderful retired NASA engineer as a physics teacher who introduced me to physics and to Carl Sagan and helped us start the first astronomy club at our school. For my undergraduate education, I chose a small women’s liberal arts college, Agnes Scott College, that had its own observatory and offered an astrophysics degree. At Agnes, I had excellent professors and the unique experience of having all of my STEM peers be women. I think that experience especially helped inoculate me for the future where I’ve more often found myself the only women in large lab groups, collaborations, and professional teams.

The last thing I would like to mention here, because I think it is really important and something I have often struggled with, is the issue of self-doubt. Self-doubt and imposter syndrome are definitely not limited to women in STEM fields, but I think being the only one around who looks like you can contribute to those feelings, and can push people away who have wonderful things to add to these fields. I have so often questioned myself and my worthiness, intelligence, and value (did I really earn that award/fellowship/job offer or was I selected just because I am a women/was in the right place at the right time/completely by mistake?). It was really important for me to know that I was not alone in doubting myself and my capabilities and I am grateful to colleagues and mentors, men and women alike, who shared their own experiences with self-doubt and imposter syndrome along the way. I’ll always remember my wonderful, brilliant, and inspiring undergraduate professor telling me about her own struggles in graduate school, and that one of the reasons she became a professor was to show us that “if she could do it, any of us could.”

Nina Mishra is a principal scientist Amazon’s Health and Wellness organization. She earned her PhD in computer science from the University of Illinois at Urbana-Champaign.

Nina Mishra
Nina Mishra

Q. When do you think we’ll reach that day that Brenda Wilkerson talked about last year?

While computer science has had a gender gap since its inception, I was convinced early on that a trifling matter like gender difference would self-correct. I was wrong. According to a 2019 Taulbee survey, 80% of PhDs are awarded to men and 20% to women. Back in 2001, the split was 78%/22% -- essentially unchanged after 18 years. The problem is not likely to improve in the next five years since the 80/20 gap persists in 2019 at the computer science bachelor’s degree level. Beyond gender gap, there is a gaping wide race gap. In 2019, less than 1% of PhDs were awarded to Black or African-American students; in 2001 this number was 1.3% -- again, essentially the same.  This gap persists early in the education pipeline.  For example, while Atlanta’s population is more than 50% black, only 3 Black students are enrolled in advanced placement computer science courses in local public high schools -- that is 3 out of 528,000! Narrowing this gap is critical for the technology industry. Companies do not want the lack of diversity in their workforce to perpetuate into their products. When will we reach that day? When we change the computer-science culture to welcome and embrace differences. 

My hope, adapting the words of others, is that the arc of social justice is long, but bends towards equality.
Nina Mishra

Q. What will it take to get there?

We cannot reach parity until we overturn the presumption that women hold different roles than men. Until we eliminate the idea that there are ‘girls’ disciplines’ and ‘boys’ disciplines’, and slights such as asking a woman in a meeting if she’s a secretary, or if she can get water for the meeting, it will be difficult to make progress.  Derogatory comments like these contribute to the ‘million cuts’ that women experience and can ultimately lead people to pursue careers where they are more wanted. I’m surprised that people are still hung up on these role associations, but the concern is real and people like Ruth Bader Ginsberg fought their entire career to overturn them. My hope, adapting the words of others, is that the arc of social justice is long, but bends towards equality.

Beyond reaching parity, underrepresented groups need to be seen and more prominently heard. All people have amazing ideas, but I have repeatedly seen ideas from underrepresented groups diminished and even discarded. When such ideas later resurface with the ownership transferred to someone in an overrepresented group, the process is demoralizing and influences people to find alternate careers. These injustices need to be reported and escalated to higher levels. The problem can only be fixed if we have an active dialogue starting from a young age.

Accessibility of resources is a consideration in some parts of the country. There are still households where students do not have a computer and others where a single computer is shared among many family members. There are households that do not have internet access. And, there are parts of the country where computer science classes and teachers aren’t available to students. People cannot choose a computer science career if they are missing these simple, starter ingredients.

Outreach is another area where we can do more. Students may wonder, `What will I do if I have a career in STEM?’. Everyone knows what a medical degree or a law degree leads to career-wise, but what does a computer science degree lead to? The common misperception is of macho geeks cranking out tons of code. For me, it is about finding ways to use data collected about some people to help millions more. It is about the amazing predictions that machine learning can make. The way that smartwatches can detect heart arrhythmias and search engines connect people to information is rooted in data and machine learning. Writing code is a means to that end. Novel and crazy ideas are what push the field forward. A more concerted effort is needed to communicate this to young students.

Q. Who or what most inspired you to pursue your STEM career?

My mother played a huge role early in life. She has a gift for explaining mathematical concepts. She taught math at a community college and also a prison. Later on, my high school math teacher played a large role. She forced students to walk to the board and write/explain their solutions. It was an early peek into the clarity one achieves by teaching their solution to others. Both taught me the precision and beauty of math. Both insisted on exacting standards for the highest quality of work. My father taught me to be bold. He has a PhD in inorganic chemistry and emphasized scientific innovation. To this day, he shares articles with the latest and greatest scientific findings, always pushing me to aim higher.

Candace Thille is director of learning science within Amazon’s Global Learning and Development organization. She obtained her master’s degree in computer science from Carnegie Mellon University and earned her PhD in education from the University of Pennsylvania.

Candace Thille
Candace Thille

Q. When do you think we'll reach that day that Brenda Wilkerson talked about last year?

I am going to change the question to respond to what I wish Brenda Darden Wilkerson had said: “I want our sons to say ‘I heard back in the day there was this problem that there weren’t enough women in tech. What was that like?” I do not mean to imply that the quote needs to be changed because the problem is only important if it is acknowledged by our sons, but rather that the problem will only be corrected when the problem, and the responsibility for correcting it, is owned by our sons too, not just our daughters.  When will we reach that day?  When gender is no longer seen as a feature of an individual that is relevant for encouraging, allocating, or selecting roles and responsibilities.

Q. What will it take to get there? 

First, an acknowledgement that the current systems and structures in STEM fields are grounded in the idea that gender and race are features of an individual that are relevant for encouraging, allocating, or selecting roles and responsibilities. Second, a commitment to ongoing inspection of those systems and structures for biases in order to change them. People would sometimes ask Ruth Bader Ginsberg “When will there be enough women on the court” and she would reply, “When there are nine”.  She would say then that “People are shocked, but there’d been nine men, and nobody’s ever raised a question about that”.  

Q. Who or what most inspired you to pursue your STEM career?

I have always been fascinated with how things work, both for the joy of understanding and to figure out how to make things work better. I have been awed by the discoveries that come from good research, and from the positive impact of using the results from research to make the world better. Both as an academic researcher and as a research scientist at Amazon, I situate my work in Pasteur's quadrant and work on projects that seek fundamental understanding of scientific problems, while also having immediate use for society.

Chao Wang is a senior applied science manager within the Alexa organization. She earned her PhD in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT).

Chao Wang
Chao Wang

Q. When do you think we'll reach that day that Brenda Wilkerson talked about last year?

I’m reminded of the Bill Gates quote, “We always overestimate the change that will occur in the next two years, and underestimate the change that will occur in the next ten. Don’t let yourself be lulled into inaction.” I’d like to think we could reach that state within the next 10 years, but it will probably take another generation of change. So I think closer to 2050.

Q. What will it take to get there?

I’ll share a very different perspective. I grew up in China and the education system back then made everyone decide their major in sophomore year of high school. That system channeled students to different college entrance exams depending on the choice (so your career paths are largely determined very early on). It was a 5:2 split ratio for STEM and non-STEM (probably matching the college admission ratio), and naturally only students who were really interested in a non-STEM career path self-selected into that track. The majority chose STEM. At the time, I did notice that more female students chose the non-STEM track, but plenty of us ended up in the STEM track, too (strength in numbers). I have observed that in the US, if you are ambivalent about STEM, then the gender stereotype works against young women pursuing STEM careers. I contrast that with the early days of computing in the US, when computer programmer was considered a female job, and you had a lot of female programmers in an otherwise male dominant technology industry and computing pioneers like Dr. Grace Hopper. It all changed (for the worse) within a generation, and we can change it back with the right societal mental shift.

Q. Who or what most inspired you to pursue your STEM career?

Growing up in China I never felt that STEM was somehow an unusual choice for a young woman. Math and physics were always my favorite subjects, and no one ever discouraged me from pursuing those interests. I enjoyed the problem solving of math and physics much more than courses requiring writing or memorization. I opted for the STEM track in high school and was admitted into a top engineering school in China for my undergraduate studies. My career path was more or less decided from that point in time.

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Have you ever wondered what it takes to transform millions of manual network planning decisions into AI-powered precision? Network Planning Solutions is looking for scientific innovators obsessed with building the AI/ML intelligence that makes orchestrating complex global operations feel effortless. Here, you'll do more than just build models; you'll create 'delight' by discovering and deploying the science that delivers exactly what our customers need, right when they need it. If you're ready to transform complex data patterns into breakthrough AI capabilities that power intuitive human experiences, you've found your team. Network Planning Solutions architects and orchestrates Amazon's customer service network of the future. By building AI-native solutions that continuously learn, predict and optimize, we deliver seamless customer experiences and empower associates with high-value work—driving measurable business impact at a global scale. As a Sr. Manager, Applied Science, you will own the scientific innovation and research initiatives that make this vision possible. You will lead a team of applied scientists and collaborate with cross-functional partners to develop and implement breakthrough scientific solutions that redefine our global network. Key job responsibilities Lead AI/ML Innovation for Network Planning Solutions: - Develop and deploy production-ready demand forecasting algorithms that continuously sense and predict customer demand using real-time signals - Build network optimization algorithms that automatically adjust staffing as conditions evolve across the service network - Architect scalable AI/ML infrastructure supporting automated forecasting and network optimization capabilities across the system Drive Scientific Excellence: - Build and mentor a team of applied scientists to deliver breakthrough AI/ML solutions - Design rigorous experiments to validate hypotheses and quantify business impact - Establish scientific excellence mechanisms including evaluation metrics and peer review processes Enable Strategic Transformation: - Drive scientific innovation from research to production - Design and validate next-generation AI-native models while ensuring robust performance, explainability, and seamless integration with existing systems. - Partner with Engineering, Product, and Operations teams to translate AI/ML capabilities into measurable business outcomes - Navigate ambiguity through experimentation while balancing innovation with operational constraints - Influence senior leadership through scientific rigor, translating complex algorithms into clear business value A day in the life Your day will be a dynamic blend of scientific innovation and strategic problem-solving. You'll collaborate with cross-functional teams, design AI algorithms, and translate complex data patterns into intuitive solutions that drive meaningful business impact. About the team We are Network Planning Solutions, a team of scientific innovators dedicated to reshaping how global service networks operate. Our mission is to create AI-native solutions that continuously learn, predict, and optimize customer experiences. We empower our associates to tackle high-value challenges and drive transformative change at a global scale.
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X team within Amazon Advertising time aims to democratize access to high-quality creatives (audio, images, videos, text) by building AI-driven solutions for advertisers. To accomplish this, we are investing in understanding how best users can leverage Generative AI methods such as latent-diffusion models, large language models (LLM), generative audio (music and speech synthesis), computer vision (CV), reinforced learning (RL) and related. As an Applied Scientist you will be part of a close-knit team of other applied scientists and product managers, UX and engineers who are highly collaborative and at the top of their respective fields. We are looking for talented Applied Scientists who are adept at a variety of skills, especially at the development and use of multi-modal Generative AI and can use state-of-the-art generative music and audio, computer vision, latent diffusion or related foundational models that will accelerate our plans to generate high-quality creatives on behalf of advertisers. Every member of the team is expected to build customer (advertiser) facing features, contribute to the collaborative spirit within the team, publish, patent, and bring SOTA research to raise the bar within the team. As an Applied Scientist on this team, you will: - Drive the invention and development of novel multi-modal agentic architectures and models for the use of Generative AI methods in advertising. - Work closely and integrate end-to-end proof-of-concept Machine Learning projects that have a high degree of ambiguity, scale and complexity. - Build interface-oriented systems that use Machine Learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Curate relevant multi-modal datasets. - Perform hands-on analysis and modeling of experiments with human-in-the-loop that eg increase traffic monetization and merchandise sales, without compromising the shopper experience. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Mentor and help recruit Applied Scientists to the team. - Present results and explain methods to senior leadership. - Willingness to publish research at internal and external top scientific venues. - Write and pursue IP submissions. Key job responsibilities This role is focused on developing new multi-modal Generative AI methods to augment generative imagery and videos. You will develop new multi-modal paradigms, models, datasets and agentic architectures that will be at the core of advertising-facing tools that we are launching. You may also work on development of ML and GenAI models suitable for advertising. You will conduct literature reviews to stay on the SOTA of the field. You will regularly engage with product managers, UX designers and engineers who will partner with you to productize your work. For reference see our products: Enhanced Video Generator, Creative Agent and Creative Studio. A day in the life On a day-to-day basis, you will be doing your independent research and work to develop models, you will participate in sprint planning, collaborative sessions with your peers, and demo new models and share results with peers, other partner teams and leadership. About the team The team is a dynamic team of applied scientists, UX researchers, engineers and product leaders. We reside in the Creative X organization, which focuses on creating products for advertisers that will improve the quality of the creatives within Amazon Ads. We are open to hiring candidates to work out of one of the following locations: UK (London), USA (Seattle).
US, WA, Bellevue
The Amazon Fulfillment Technologies (AFT) Science team is seeking an exceptional Applied Scientist with strong operations research and optimization expertise to develop production solutions for one of the most complex systems in the world: Amazon's Fulfillment Network. At AFT Science, we design, build, and deploy optimization, statistics, machine learning, and GenAI/LLM solutions that power production systems running across Amazon Fulfillment Centers worldwide. We tackle a wide range of challenges throughout the network, including labor planning and staffing, pick scheduling, stow guidance, and capacity risk management. Our mission is to develop innovative, scalable, and reliable science-driven production solutions that exceed the published state of the art, enabling systems to run optimally and continuously (from every few minutes to every few hours) across our large-scale network. Key job responsibilities As an Applied Scientist, you will collaborate with scientists, software engineers, product managers, and operations leaders to develop optimization-driven solutions that directly impact process efficiency and associate experience in the fulfillment network. Your key responsibilities include: - Develop deep understanding and domain knowledge of operational processes, system architecture, and business requirements - Dive deep into data and code to identify opportunities for continuous improvement and disruptive new approaches - Design and develop scalable mathematical models for production systems to derive optimal or near-optimal solutions for existing and emerging challenges - Create prototypes and simulations for agile experimentation of proposed solutions - Advocate for technical solutions with business stakeholders, engineering teams, and senior leadership - Partner with software engineers to integrate prototypes into production systems - Design and execute experiments to test new or incremental solutions launched in production - Build and monitor metrics to track solution performance and business impact About the team Amazon Fulfillment Technology (AFT) designs, develops, and operates end-to-end fulfillment technology solutions for all Amazon Fulfillment Centers (FCs). We harmonize the physical and virtual worlds so Amazon customers can get what they want, when they want it. The AFT Science team brings expertise in operations research, optimization, statistics, machine learning, and GenAI/LLM, combined with deep domain knowledge of operational processes within FCs and their unique challenges. We prioritize advancements that support AFT tech teams and focus areas rather than specific fields of research or individual business partners. We influence each stage of innovation from inception to deployment, which includes both developing novel solutions and improving existing approaches. Our production systems rely on a diverse set of technologies, and our teams invest in multiple specialties as the needs of each focus area evolve.
US, WA, Seattle
We are looking for an exceptional applied scientist to join the AWS Applied AI Life Sciences organization. You will invent, implement, and deploy state of the art machine learning algorithms and intelligent AI systems to solve complex problems in healthcare and life sciences area, making a meaningful impact on patient lives. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. Key job responsibilities - Design, develop, and deploy novel Agentic systems and ML solutions for complex healthcare and life sciences challenges - Navigate ambiguity and create clarity in early-stage product development - Collaborate with product managers, engineers, and domain experts to transform research into production-quality features - Mentor junior scientists and participate in tactical and strategic planning A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, design simulations and experiments, and develop statistical and ML models. About the team We are a multidisciplinary team of product managers, engineers, scientists, and domain experts working at the intersection of AI/ML and healthcare. We leverage AWS's expertise in secure, scalable cloud computing and applied AI to solve complex challenges in healthcare and life sciences. Our team values customer obsession, technical excellence, innovation, and a commitment to improving patient outcomes through technology.
US, WA, Seattle
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Demand Utilization team with Sponsored Products and Brands owns finding the appropriate ads to surface to customers when they search for products on Amazon. We strive to understand our customers’ intent and identify relevant ads which enable them to discover new and alternate products. This also enables sellers on Amazon to showcase their products to customers, which may at times be buried deeper in the search results. Our systems and algorithms operate on one of the world's largest product catalogs, matching shoppers with products - with a high relevance bar and strict latency constraints. We are a team of machine learning scientists and software engineers working on complex solutions to understand the customer intent and present them with ads that are not only relevant to their actual shopping experience, but also non-obtrusive. This area is of strategic importance to Amazon Retail and Marketplace business, driving long term-growth. We are looking for an Applied Scientist II, with a strong background in Machine Learning and Generative AI to optimize serving ads on billions of product pages. The solutions you create would drive step increases in coverage of sponsored ads across the retail website and ensure relevant ads are served to Amazon's customers. You will directly impact our customers' shopping experience while helping our sellers get the maximum ROI from advertising on Amazon. You will be expected to demonstrate strong ownership and should be curious to learn and leverage the rich textual, image, and other contextual signals. This role will challenge you to utilize innovative machine learning techniques in the domain of predictive modeling, natural language processing (NLP), deep learning, reinforcement learning, query understanding, vector search, image recognition, and multi-modal AI to deliver significant impact for the business. In addition, you will be at the forefront of leveraging Generative AI (GenAI) technologies, including Large Language Models (LLMs) and foundation models, to drive advanced language understanding, creative ad content generation, and retrieval-augmented generation (RAG). You will also design and build agentic AI systems capable of autonomous, multi-step reasoning, tool use, and chain-of-thought decision-making, while applying techniques such as prompt engineering, fine-tuning, RLHF (Reinforcement Learning from Human Feedback), and embedding-based retrieval to develop scalable, production-grade solutions. Ideal candidates will have hands-on experience fine-tuning, evaluating, and deploying LLMs at scale, along with a strong understanding of emerging GenAI paradigms including agentic workflows and responsible AI practices. You should be able to work cross-functionally across multiple stakeholders, synthesize the science needs of our business partners, develop models to solve business needs, and implement solutions in production. In addition to being a strongly motivated IC, you will also be responsible for mentoring junior scientists, guiding them to deliver high-impact products and services for Amazon customers and sellers, and fostering a culture of innovation around the latest advancements in Generative AI and LLM technologies. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video https://youtu.be/zD_6Lzw8raE Key job responsibilities As an Applied Scientist II on this team, you will: - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in deploying your ML models. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Research new and innovative machine learning approaches.
US, CA, Sunnyvale
Amazon Industrial Robotics is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As an Applied Scientist, you will develop and improve machine learning systems that help robots perceive, reason, and act in real-world environments. You will leverage state-of-the-art models (open source and internal research), evaluate them on representative tasks, and adapt/optimize them to meet robustness, safety, and performance needs. You will invent new algorithms where gaps exist. You’ll collaborate closely with research, controls, hardware, and product-facing teams, and your outputs will be used by downstream teams to further customize and deploy on specific robot embodiments. Key job responsibilities - Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization. - Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments. - Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes. - Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes. - Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies.
US, NY, New York
Advertising at Amazon is growing incredibly fast and we are responsible for defining and delivering a collection of advertising products that drive discovery and sales. Amazon Business Ads is equally growing fast ($XXXMs to $XBs) and owns engineering and science for the AB WW ad experience. We build business-to-business (“B2B”) specific ad solutions distributed across retail and ad systems for shopper and advertiser experiences. Some include new ad placements or widgets, creatives, sourcing techniques, ad campaign management capabilities and much more! We consider unique AB qualities which are differentiated from the consumer experience such as varying shopper role types, purchasing complexities based on business size and industry (eg education vs healthcare), AB specific features (eg business discounts, buying policies to restrict and prefer products), and AB buyer behaviors (eg buying in bulk). We are seeking a scientific leader who can drive innovation in complex problem areas and new business initiatives. The ideal candidate will: Technical & Research Requirements: * Demonstrate fluency in Python, R, Matlab or other statistical languages and familiarity with deep learning frameworks like PyTorch, TensorFlow * Lead end-to-end solution development from research to prototyping and experimentation * Write and deploy significant parts of scientifically novel software solutions into production Leadership & Influence: * Drive team's scientific agenda by proposing new initiatives and securing management buy-in including PM, SDM * Mentor colleagues and contribute to their professional development * Build consensus on large projects and influence decisions across different teams in Ads Key Leadership Principles: * Dive Deep: Uncover non-obvious insights in data * Deliver Results: Create solutions aligned with customer and product needs * Learn and Be Curious: Demonstrate self-driven desire to explore new research areas * Earn Trust: Build relationships with stakeholders through understanding business needs