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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Work on ML teams building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. As an Applied Scientist II, you will set scientific direction, mentor applied scientists, and partner with engineering and product leaders to deliver production-grade ML solutions at massive scale. Key job responsibilities 1. Lead and grow a high-performing team of Applied Scientists, providing technical guidance, mentorship, and career development. 2. Define and own the scientific vision and roadmap for ML solutions powering large-scale transportation planning and execution. 3. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 4. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 5. Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 6. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing. A day in the life Your day includes reviewing model performance and business metrics, guiding technical design and experimentation, mentoring scientists, and driving roadmap execution. You’ll balance near-term delivery with long-term innovation while ensuring solutions are robust, interpretable, and scalable. Ultimately, your work helps improve delivery reliability, reduce costs, and enhance the customer experience at massive scale.
US, WA, Seattle
Amazon Customer Service (CS) Data Intelligence builds the data and Artificial Intelligence (AI) foundations for CS to ensure Amazon delivers the best customer service possible. CS Economics sits within CS DI and contributes to the CS knowledge base and decision frameworks. CS Economics seeks economists to apply economic methods to solve business problems. The ideal candidate will work with engineers and applied scientists to design models that leverage large scale and unstructured data, design scalable agents for non-tech CS partners to understand the impact of their actions, and propose mechanism designs to robustly match customers to our services. CS Economics is looking for optimistic critical-thinkers who combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates enjoy working in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities - Design and conduct rigorous evaluations of CS actions - Develop experiments to evaluate product launches - Communicate complex findings to business stakeholders in clear, actionable terms - Work with engineering teams to develop scalable tools that automate and streamline evaluation processes A day in the life Work with teammates to apply economic methods to business problems, e.g., identify the appropriate research question and identification strategy, write code to estimate heterogeneous treatment effects or conduct experiment analysis, write and present a document with findings to business leaders. We collaborate with partner teams within and outside of CS throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team Amazon Customer Service (CS) Economics provides estimates and measures of the causal impact of CS actions on costs and benefits. We build agents and guide leadership to establish processes to scale valid experimentation, causal inference, and mechanism design.
US, CA, Sunnyvale
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities Develop foundation models for content understanding using state-of-the-art deep learning and multimodal learning techniques to analyze video and text Build time sequence foundation models to understand and predict customer behavior patterns and viewing trajectories Work closely with engineers and product managers to design, implement and launch solutions end-to-end across various Prime Video experiences Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses Effectively communicate technical and non-technical ideas with teammates and stakeholders Stay up-to-date with advancements and the latest modeling techniques in foundation models, multimodal learning, and time series analysis Publish your research findings in top conferences and journals A day in the life We're using advanced approaches such as foundation models to connect information about our videos and customers from a variety of information sources, acquiring and processing data sets on a scale that only a few companies in the world can match. This will enable us to recommend titles effectively, even when we don't have a large behavioral signal (to tackle the cold-start title problem). It will also allow us to find our customer's niche interests, helping them discover groups of titles that they didn't even know existed. We are looking for creative & customer obsessed machine learning scientists who can apply the latest research, state of the art algorithms and ML to build highly scalable page personalization solutions. You'll be a research leader in the space and a hands-on ML practitioner, guiding and collaborating with talented teams of engineers and scientists and senior leaders in the Prime Video organization. You will also have the opportunity to publish your research at internal and external conferences. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various Prime Video surfaces and devices. We work closely with the engineering teams to launch our solutions in production.
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the limits. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. As a Senior Applied Scientist on our team, you will focus on building state-of-the-art ML models for healthcare. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the forefront of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams. This role offers a unique opportunity to work on projects that could fundamentally transform healthcare outcomes. Key job responsibilities In this role, you will: • Design and implement novel AI/ML solutions for complex healthcare challenges • Drive advancements in machine learning and data science • Balance theoretical knowledge with practical implementation • Work closely with customers and partners to understand their requirements • Navigate ambiguity and create clarity in early-stage product development • Collaborate with cross-functional teams while fostering innovation in a collaborative work environment to deliver impactful solutions • Establish best practices for ML experimentation, evaluation, development and deployment • Partner with leadership to define roadmap and strategic initiatives You’ll need a strong background in AI/ML, proven leadership skills, and the ability to translate complex concepts into actionable plans. You’ll also need to effectively translate research findings into practical solutions. 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. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the Special Projects organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. About the team We represent Amazon's ambitious vision to solve the world's most pressing challenges. We are exploring new approaches to enhance research practices in the healthcare space, leveraging Amazon's scale and technological expertise. We operate with the agility of a startup while backed by Amazon's resources and operational excellence. We're looking for builders who are excited about working on ambitious, undefined problems and are comfortable with ambiguity.
US, CA, Palo Alto
About Sponsored Products and Brands The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through 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. About our team SPB Ad Response Prediction team is your choice, if you want to join a highly motivated, collaborative, and fun-loving team with a strong entrepreneurial spirit and bias for action. We are seeking an experienced and motivated Applied Scientist with machine learning engineering background who loves to innovate at the intersection of customer experience, deep learning, and high-scale machine learning systems. We are looking for a talented Applied Scientist with a strong background in machine learning engineering to join our team and help us grow the business. In this role, you will partner with a team of engineers and scientists to build advanced machine learning models and infrastructure, from training to inference, including emerging LLM-based systems, that deliver highly relevant ads to shoppers across all Amazon platforms and surfaces worldwide. Key job responsibilities As an Applied Scientist, you will: * Develop scalable and effective machine learning models and optimization strategies to solve business problems. * Conduct research on new machine learning modeling to optimize all aspects of Sponsored Products business. * Enhance the scalability, automation, and efficiency of large-scale training and real-time inference systems. * Pioneer the development of LLM inference infrastructure to support next-generation GenAI workloads at Amazon Ads scale.
US, CA, Sunnyvale
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Amazon Music - Search Science team is seeking an experienced Applied Scientist who will join a team of experts in the field of machine learning, and work together to break new ground in the world of understanding and classifying different forms of music, and creating interactive experiences to help users find the music they are in the mood for. We work on machine learning problems for music classification, recommender systems, dialogue systems, NLP, and music information retrieval. You'll work in a collaborative environment where you can pursue applied research, with many peta-bytes of data, work on problems that haven’t been solved before, quickly implement and deploy your algorithmic ideas at scale, understand whether they succeed via statistically relevant experiments across millions of customers, and publish your research. You'll see the work you do directly improve the experience of Amazon Music customers on Alexa/Echo, mobile, and web. Key job responsibilities - Use machine learning, deep learning, LLMs and Agentic AI techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key processes - Design, development and evaluation of AI models for predictive learning - Work closely with software engineering teams to drive model implementations and new feature creations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Research and implement novel machine learning and statistical approaches About the team Everyone on our team has a meaningful impact on product features, new directions in music streaming, and customer engagement. We are looking for new team members across a variety of job functions including software engineering/development, marketing, design, ops and more. Come join us as we make history by launching exciting new projects in the coming year.Our team is focused on building a personalized, curated, and seamless music experience. We want to help our customers discover up-and-coming artists, while also having access to their favorite established musicians. We build systems that are distributed on a large scale, spanning our music apps, web player, and voice-forward audio engagement on mobile and Amazon Echo devices, powered by Alexa to support our customer base. Amazon Music offerings are available in countries around the world, and our applications support our mission of delivering music to customers in new and exciting ways that enhance their day-to-day lives.
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced ML systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real-world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning team for India Consumer Businesses. Machine Learning, Big Data and related quantitative sciences have been strategic to Amazon from the early years. Amazon has been a pioneer in areas such as recommendation engines, ecommerce fraud detection and large-scale optimization of fulfillment center operations. As Amazon has rapidly grown and diversified, the opportunity for applying machine learning has exploded. We have a very broad collection of practical problems where machine learning systems can dramatically improve the customer experience, reduce cost, and drive speed and automation. These include product bundle recommendations for millions of products, safeguarding financial transactions across by building the risk models, improving catalog quality via extracting product attribute values from structured/unstructured data for millions of products, enhancing address quality by powering customer suggestions We are developing state-of-the-art machine learning solutions to accelerate the Amazon India growth story. Amazon India is an exciting place to be at for a machine learning practitioner. We have the eagerness of a fresh startup to absorb machine learning solutions, and the scale of a mature firm to help support their development at the same time. As part of the India Machine Learning team, you will get to work alongside brilliant minds motivated to solve real-world machine learning problems that make a difference to millions of our customers. We encourage thought leadership and blue ocean thinking in ML. Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions that attack India first (and other Emerging Markets across MENA and LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide