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This is an image with six separate photos, one of an Amazon fulfillment center, another of female scientist Belinda Zend, a third of a Pi Day billboard in Times Square with an image of Marie Curie, a fourth with an image of Josh Miele at a computer, a fifth of an Alexa Echo device, and a sixth of a Formula 1 race car.
Images from some of the stories that captivated our readers in the first half of 2022, including Belinda Zeng (top row, middle), head of applied science and engineering, Amazon Search Science and AI, who earlier this year shared her thoughts on what it takes to succeed as a scientist at Amazon, and MacArthur Fellow Josh Miele (lower left), who has merged a lifelong passion for science with a mission to make the world more accessible for people with disabilities.

Ten stories from the first half of 2022 that captivated readers

From Josh Miele's passion for making the world more accessible to improving forecasting by learning quantile functions, these stories resonated with our audience.

By Staff writer
July 4, 2022
5 min read
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  1. Josh Miele: Amazon’s resident MacArthur Fellow
    Josh Miele, in a purple dress shirt, sits at a desk in an office, he is typing and looking at a computer screen, there are chairs and desks in the background
    Josh Miele, an Amazon principal accessibility researcher, was selected a 2021 MacArthur Foundation Fellow. He has spent his career developing tools to make the world more accessible for people who are blind and visually impaired.
    Meg Coyle / Amazon

    In September 2021, when Josh Miele, an Amazon principal accessibility researcher, got a text from someone at the MacArthur Foundation requesting a phone call, his heart leapt. For anyone in the arts and sciences, a MacArthur Fellowship, known as the “genius” grant, is akin to winning the lottery.

    For Miele, who is blind and has spent his career developing tools to make the world more accessible for people who are blind and visually impaired, a MacArthur grant had long been a fantastical dream. Learn how he has merged a lifelong passion for science with a mission to make the world more accessible for people with disabilities.

  2. How to build a successful career as a scientist at Amazon
    Belinda Zeng, the head of applied science and engineering at Amazon Search Science and AI, is seen standing outside in Costa Rica on a sunny day, a wire fence is just behind her in the foreground, and a valley and mountains are seen in the background
    Belinda Zeng is the head of applied science and engineering at Amazon Search Science and AI.
    Courtesy of Belinda Zeng

    Belinda Zeng, head of applied science and engineering at Amazon Search Science and AI, has participated in hundreds of interviews for science roles across the company.

    Earlier this year, she shared her thoughts on what it takes to succeed as a scientist at Amazon — including the lessons she learned as a Bar Raiser: experienced interviewers who help to raise the Amazon recruiting standard.

    Learn what the hiring team looks for and which three Leadership Principles stand out for scientists.

  3. Amazon releases 51-language dataset for language understanding
    Multilingual Alexa.png
    The MASSIVE dataset is a step toward the creation of multilingual natural-language-understanding models that can generalize easily to new languages.

    Amazon researchers released a new dataset called MASSIVE, which is composed of one million labeled utterances spanning 51 languages, along with open-source code.

    The release provides examples of how to perform massively multilingual NLU modeling and allows practitioners to re-create baseline results for intent classification and slot filling.

  4. The science behind the 2022 FORMULA 1 car
    Image shows the 2022 F1 car sitting in profile on a racetrack with viewing stands in the background
    The F1 engineering team collaborated with AWS to explore the science of how cars interact when racing in close proximity.
    F1

    When the 2022 FORMULA 1 (F1) racing season started in March, teams will took to the track with newly designed cars engineered to give fans — and drivers — more of the wheel-to-wheel action they’ve been seeking.

    Learn how the F1 engineering team collaborated with AWS to develop new design specifications to help make races more competitive.

  5. How AWS uses graph neural networks to meet customer needs
    Protein graphs.png
    Examples of graph representations of proteins.

    At Amazon Web Services, the use of machine learning to make the information encoded in graphs more useful to customers has been a major research focus.

    In this post, AWS researchers showcased a variety of graph ML applications that customers have developed in collaboration with AWS scientists, from malicious-account detection and automated document processing to knowledge-graph-assisted drug discovery and protein property prediction.

  6. Improving forecasting by learning quantile functions
    Quantile function animation.gif
    The quantile function is simply the inverse of the cumulative distribution function (if it exists). Its graph can be produced by flipping the cumulative distribution function's graph over.

    The quantile function is a mathematical function that takes a quantile (a percentage of a distribution, from 0 to 1) as input and outputs the value of a variable. It can answer questions like, “If I want to guarantee that 95% of my customers receive their orders within 24 hours, how much inventory do I need to keep on hand?” As such, the quantile function is commonly used in the context of forecasting questions.

    In practical cases, however, we rarely have a tidy formula for computing the quantile function. Instead, statisticians usually use regression analysis to approximate it for a single quantile level at a time. That means that if you decide you want to compute it for a different quantile, you have to build a new regression model — which, today, often means retraining a neural network.

    In a pair of papers presented at this year’s International Conference on Artificial Intelligence and Statistics (AISTATS), Amazon researchers describe an approach to learning an approximation of the entire quantile function at once, rather than simply approximating it for each quantile level.

  7. How pioneering deep learning is reducing Amazon’s packaging waste
    An overhead shot inside an Amazon fulfillment center shows hundreds of boxes on conveyor belts along with people monitoring the flow of those packages
    Amazon's scale makes picking the right package for each product a challenge. Fortunately, machine learning approaches — particularly deep learning — thrive on big data and massive scale. These tools have helped Amazon reduce per-shipment packaging weight by 36% and eliminate more than a million tons of packaging.

    Finding the right amount of packaging to ship an item can be challenging — and at Amazon, an ever-changing catalog of hundreds of millions of products makes it an ongoing challenge.

    Fortunately, machine learning approaches — particularly deep learning — thrive on big data and massive scale, and a pioneering combination of natural language processing and computer vision is enabling Amazon to hone in on using the right amount of packaging. Learn how these tools have helped Amazon drive change over the past six years, reducing per-shipment packaging weight by 36% and eliminating more than a million tons of packaging, equivalent to more than 2 billion shipping boxes.

  8. How Prime Video uses machine learning to ensure video quality
    Block Corruption Detection.gif
    The initial version of Amazon Prime Video's block corruption detector uses a residual neural network to produce a map indicating the probability of corruption at particular image locations, binarizes that map, and computes the ratio between the corrupted area and the total image area.

    Streaming video can suffer from defects introduced during recording, encoding, packaging, or transmission, so most subscription video services — such as Amazon Prime Video — continually assess the quality of the content they stream.

    Manual content review — known as eyes-on-glass testing — doesn’t scale well, and it presents its own challenges, such as variance in reviewers’ perceptions of quality. More common in the industry is the use of digital signal processing to detect anomalies in the video signal that frequently correlate with defects.

    Three years ago, the Video Quality Analysis (VQA) group in Prime Video started using machine learning to identify defects in captured content from devices, such as gaming consoles, TVs, and set-top boxes, to validate new application releases or offline changes to encoding profiles. Learn how they've been applying the same techniques to problems such as real-time quality monitoring of thousands of channels and live events and to analyzing new catalogue content at scale.

  9. How Amazon Music's recommender hits the right notes
    A screen grab of the Amazon Music website
    Since 2018, Amazon Music customers in the US have been able to converse with the Alexa voice assistant. Progress in machine learning has recently made the Alexa music recommender experience even more successful and satisfying for customers.

    Since 2018, Amazon Music customers in the US who aren’t sure what to choose have been able to converse with the Alexa voice assistant. The idea is that Alexa gathers the crucial missing information to help the customer arrive at the right recommendation for that moment. The technical complexity of this challenge is hard to overstate, but progress in machine learning (ML) at Amazon has recently made the Alexa music recommender experience even more successful and satisfying for customers.

    Learn how the Amazon Music Conversations team is using pioneering machine learning to make Alexa's discernment better than ever.

  10. Amazon Science celebrated Pi Day in Times Square
    Amazon Science celebrates Pi Day

    To mark Pi Day this year, Amazon Science utilized a Times Square billboard to honor scientists, engineers, and mathematicians past, present, and future.

    The billboard display ran from midnight to 8 a.m. and again — for 3 hours and 14 minutes — from 3:14 p.m. to 6:28 p.m. The display began by honoring Marie Curie, the first woman to be awarded a Nobel Prize in 1903 for her contributions to physics. It was Curie who once famously said, “Nothing in life is to be feared, it is only to be understood. Now is the time to understand more, so that we may fear less.”

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Applied Scientist I, International Machine Learning
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Quantum Research Engineer, Quantum Computing
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Quantum Research Engineer, Quantum Computing
US, CA, Pasadena
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2026 Applied Scientist Intern, Amazon University Talent Acquisition
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Are you a MS or PhD student interested in a 2026 internship in the field of machine learning, deep learning, generative AI, large language models, speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal intern must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, South Africa, Spain, Sweden, UAE, and UK). Please note these are not remote internships.
Applied Scientist, Pricing and Promotion Optimization
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Amazon's Pricing & Promotions Science is seeking a driven Applied Scientist to harness planet scale multi-modal datasets, and navigate a continuously evolving competitor landscape, in order to regularly generate fresh customer-relevant prices on billions of Amazon and Third Party Seller products worldwide. We are looking for a talented, organized, and customer-focused applied researchers to join our Pricing and Promotions Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon. This role requires an individual with exceptional machine learning and reinforcement learning modeling expertise, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator, who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment. Key job responsibilities - See the big picture. Understand and influence the long term vision for Amazon's science-based competitive, perception-preserving pricing techniques - Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale - Stay informed. Establish mechanisms to stay up to date on latest scientific advancements in machine learning, neural networks, natural language processing, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems - Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery. - Successfully execute & deliver. Apply your exceptional technical machine learning expertise to incrementally move the needle on some of our hardest pricing problems. A day in the life We are hiring an applied scientist to drive our pricing optimization initiatives. The Price Optimization science team drives cross-domain and cross-system improvements through: - invent and deliver price optimization, simulation, and competitiveness tools for Sellers. - shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs. - Promotion optimization initiatives exploring CX, discount amount, and cross-product optimization opportunities. - Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) Price is a highly relevant input into many partner-team architectures, and is highly relevant to the customer, therefore this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication. About the team About the team: the Pricing Discovery and Optimization team within P2 Science owns price quality, discovery and discount optimization initiatives, including criteria for internal price matching, price discovery into search, p13N and SP, pricing bandits, and Promotion type optimization. We leverage planet scale data on billions of Amazon and external competitor products to build advanced optimization models for pricing, elasticity estimation, product substitutability, and optimization. We preserve long term customer trust by ensuring Amazon's prices are always competitive and error free.
Control Stack Manager, Center for Quantum Computing
US, CA, Pasadena
The Amazon Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. We are looking to hire a Control Stack Manager to join our growing software group. You will lead a team of interdisciplinary scientists and software engineers, focused on developing research software and infrastructure to support the development and operation of scalable fault-tolerant quantum computers. You will interface directly with our experimental physics and control hardware teams to develop and drive a vision for the experimental quantum computing software-hardware interface. The ideal candidate will (1) have strong technical breadth across low-level programming, scientific instrumentation, and computer architecture, (2) have excellent communication skills and a proven track record of collaborating with scientists and hardware engineers, and (3) be excited about empowering and growing a team of scientists and software engineers. Inclusive Team Culture Here at Amazon, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Export Control Requirement Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility. Key job responsibilities - Develop a technical vision for the quantum software-hardware interface in collaboration w/ senior engineers - Collaborate effectively with science and hardware teams to derive software needs and priorities - Own resource allocation and planning activities for your team to meet the needs of (internal) customers - Be comfortable “getting your hands dirty” (i.e. diving deep into architecture, metrics, and implementation) - Regularly provide technical evaluation and feedback to your reports (i.e. via code review, design docs, etc.) - Drive hiring activities for your team — develop growth plans, source candidates, and design interview loops - Coach and empower your employees to become better engineers, scientists, and communicators We are looking for candidates with strong engineering principles, a bias for action, superior problem-solving, and excellent communication skills. Thriving in ambiguity and leading with empathy are essential. As a manager embedded in a broader research science organization, you will have the opportunity to work on new ideas and stay abreast of the field of experimental quantum computation. A day in the life The majority of your time will be spent orchestrating, coaching, and growing the control stack team at the Center for Quantum Computing. This requires collaborating with other science and software teams and working backwards from the needs of our science staff in the context of our larger experimental roadmap. You will translate science needs and priorities into software project proposals and resource allocations. Once project proposals have been accepted, you will support and empower your team to deliver these projects on time while maintaining high standards of engineering excellence. Because many high-level experimental goals have cross-cutting requirements, you’ll need to stay in sync with partner science and software teams. About the team You will be joining the software group within the Center of Quantum Computing. Our team is comprised of scientists and software engineers who are building scalable software that enables quantum computing technologies.
Applied Scientist , Prime Video - Personalization and Discovery Science
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! We are looking for a self-motivated, passionate and resourceful Applied Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities - Develop AI solutions for various Prime Video recommendation systems using Deep learning, GenAI, Reinforcement Learning, and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - 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 the field; - Publish your research findings in top conferences and journals. About the team Prime Video Recommendation Science team owns science solution to power personalized experience on various devices, from sourcing, relevance, ranking, to name a few. We work closely with the engineering teams to launch our solutions in production.
Applied Scientist, Prime Video - Content Understanding, Localization & Enrichment Team
US, WA, Seattle
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 subscriptions such as Apple TV+, HBO Max, Peacock, 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 team member, 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 We are looking for passionate, hard-working, and talented individuals to help us push the envelope of content localization. We work on a broad array of research areas and applications, including but not limited to multimodal machine translation, speech synthesis, speech analysis, and asset quality assessment. Candidates should be prepared to help drive innovation in one or more areas of machine learning, audio processing, and natural language understanding. The ideal candidate would have experience in audio processing, natural language understanding and machine learning. Familiarity with machine translation, foundational models, and speech synthesis will be a plus. As an Applied Scientist, you should be a strong communicator, able to describe scientifically rigorous work to business stakeholders of varying levels of technical sophistication. You will closely partner with the solution development teams, and should be intensely curious about how the research is moving the needle for business. Strong inter-personal and mentoring skills to develop applied science talent in the team is another important requirement.
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