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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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This role combines expertise in LLM reasoning and agentic AI with applied impact in life sciences. You will work on how large language models reason plan and act in complex scientific domains while applying domain knowledge to ensure models produce scientifically valid outputs. The problems span multiple fronts: • How do you build LLM-based agentic systems that correctly reason over clinical protocols regulatory standards and complex multi-step operational workflows? • How do you evaluate agent reliability and faithfulness rigorously enough to trust in high-stakes clinical settings? • How do you develop model customization methods (fine-tuning retrieval augmentation domain adaptation) that let customers get strong results from foundation models on their own data? You will focus on clinical trial operations (agentic automation structured reasoning evaluation domain adaptation) with opportunities to contribute across drug discovery (protein engineering antibody design) as the portfolio grows. You will own end-to-end scientific solutions from research through production and your work will directly shape the tools that scientists use daily. Key job responsibilities • Design train fine-tune and evaluate LLM-based agentic systems that reason over clinical protocols regulatory standards and operational workflows • Build rigorous evaluation harnesses and benchmarks to measure agent reliability faithfulness and failure modes in high-stakes domains • Develop model customization methods (fine-tuning RLHF retrieval augmentation domain adaptation) that help customers get better outputs on their own data with less effort • Contribute to graph-based and causal modeling approaches for clinical trial operations • Partner with Life Sciences domain experts product and engineering to translate scientific challenges into shipped capabilities • Own experiments end to end: problem framing implementation evaluation iteration and hand-off to production • Publish at top-tier venues where the work supports it • Contribute to drug discovery efforts (protein engineering antibody design) as opportunities arise A day in the life • Design and run an experiment to validate a new agentic reasoning or fine-tuning method then ship it as a capability customers can use • Diagnose why a model is failing on a new class of inputs and implement a fix to unblock a delivery milestone • Build or extend an evaluation benchmark to measure how faithfully an agent reasons over clinical logic • Meet with domain experts to scope what the next model release needs to do • Review results with a senior scientist sharpen the approach and get it over the finish line • Prototype a new idea that could become the next capability in the product About the team Amazon values diverse experiences. 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Data Scientist III, Amazon 1P Credito, Payments
BR, SP, Sao Paulo
Do you feel the challenge and the adrenaline kick when a huge data-set stares you in the face and you know that somewhere inside are hidden very important business insights that can fundamentally alter the way top business leaders think and act? Do you enjoy presenting strong data backed insights to business leaders; insights that can topple their long held beliefs and compel them to change their direction completely? If yes, then you are the one we are looking for. We are looking to invite passionate leaders, with expertise in generate power business insights from very large datasets, on a journey where the primary aim would be to enable needle moving business impacts through statistical analysis. We are looking for leaders who can envision the design and development of analytical infrastructure which can support strategic and tactical decision-making. Those who join this high visibility team would have to navigate through significant ambiguity in defining business problems and converting them to analytical problems. This role requires additional exposure and experience to Machine Learning. 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, development, evaluate and deploy innovative and highly scalable ml models such as risk scorecards, income models, fraud models for predictive learning in credit risk applications • Research and implement novel machine learning and statistical approaches • Work closely with software engineering teams to drive real-time model implementations and new feature creations • Work closely with business owners and operations staff to optimize various business operations • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation • Mentor other scientists and engineers in the use of ML techniques • Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions • Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment to delight Amazon customers • Collaborate with cross-functional teams to develop comprehensive ML/statistical models that can scale to millions of customers to multiple countries Understand the credit risk data and evaluate the best ml model/ solution for dynamic business problems. About the team Brazil Payments is part of the International Emerging Stores Payments team and focuses on supporting the launch of new payment and financial products to our customers in Brazil.
Data Scientist II, Amazon 1P Credito, Payments
BR, SP, Sao Paulo
Do you feel the challenge and the adrenaline kick when a huge data-set stares you in the face and you know that somewhere inside are hidden very important business insights that can fundamentally alter the way top business leaders think and act? Do you enjoy presenting strong data backed insights to business leaders; insights that can topple their long held beliefs and compel them to change their direction completely? If yes, then you are the one we are looking for. We are looking to invite passionate leaders, with expertise in generate power business insights from very large datasets, on a journey where the primary aim would be to enable needle moving business impacts through statistical analysis. We are looking for leaders who can envision the design and development of analytical infrastructure which can support strategic and tactical decision-making. Those who join this high visibility team would have to navigate through significant ambiguity in defining business problems and converting them to analytical problems. This role requires additional exposure and experience to Machine Learning. 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, development, evaluate and deploy innovative and highly scalable models for predictive learning • Research and implement novel machine learning and statistical approaches • Work closely with software engineering teams to drive real-time model implementations and new feature creations • Work closely with business owners and operations staff to optimize various business operations • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation • Mentor other scientists and engineers in the use of ML techniques • Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions • Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment to delight Amazon customers • Collaborate with cross-functional teams to develop comprehensive ML/statistical models that can scale to millions of customers to multiple countries About the team Brazil Payments is part of the International Emerging Stores Payments team and focuses on supporting the launch of new payment and financial products to our customers in Brazil.
Data Scientist III, Amazon 1P Credito, Payments
BR, SP, Sao Paulo
Do you feel the challenge and the adrenaline kick when a huge data-set stares you in the face and you know that somewhere inside are hidden very important business insights that can fundamentally alter the way top business leaders think and act? Do you enjoy presenting strong data backed insights to business leaders; insights that can topple their long held beliefs and compel them to change their direction completely? If yes, then you are the one we are looking for. We are looking to invite passionate leaders, with expertise in generate power business insights from very large datasets, on a journey where the primary aim would be to enable needle moving business impacts through statistical analysis. We are looking for leaders who can envision the design and development of analytical infrastructure which can support strategic and tactical decision-making. Those who join this high visibility team would have to navigate through significant ambiguity in defining business problems and converting them to analytical problems. This role requires additional exposure and experience to Machine Learning. 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, development, evaluate and deploy innovative and highly scalable ml models such as risk scorecards, income models, fraud models for predictive learning in credit risk applications • Research and implement novel machine learning and statistical approaches • Work closely with software engineering teams to drive real-time model implementations and new feature creations • Work closely with business owners and operations staff to optimize various business operations • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation • Mentor other scientists and engineers in the use of ML techniques • Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions • Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment to delight Amazon customers • Collaborate with cross-functional teams to develop comprehensive ML/statistical models that can scale to millions of customers to multiple countries Understand the credit risk data and evaluate the best ml model/ solution for dynamic business problems. About the team Brazil Payments is part of the International Emerging Stores Payments team and focuses on supporting the launch of new payment and financial products to our customers in Brazil.
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