This year's Day One Amazon Robotics Fellows are, top row, left to right: Omoruyi Atheka, Camille Anne Chungyoun, Asbel Fontanez, Zakar Handricken, Abubakarr Jaye, Christopher LeBlanc, and Janeth Meraz; bottom row, left to right: Jessie Mindel, Naana Obeng-Marnu, Kimberly Llajaruna Peralta, Priscila Rubio, Antonio Sanchez, Augustus ‘ Gus’ Teran, and Walter Williams.
This year's Day One Amazon Robotics Fellows are, top row, left to right: Omoruyi Atheka, Camille Anne Chungyoun, Asbel Fontanez, Zakar Handricken, Abubakarr Jaye, Christopher LeBlanc, and Janeth Meraz; bottom row, left to right: Jessie Mindel, Naana Obeng-Marnu, Kimberly Llajaruna Peralta, Priscila Rubio, Antonio Sanchez, Augustus ‘ Gus’ Teran, and Walter Williams.

Amazon Robotics expands Day One Fellowship Program and selects 14 recipients for 2022

Program empowers Black, Latinx, and Native American students to become industry leaders through scholarship, research, and career opportunities.

Amazon Robotics recently announced fourteen new recipients of the Amazon Robotics Day One Fellowship, a program established to support exceptionally talented students from diverse technical and multicultural backgrounds who are pursuing master of science degrees. The program was developed to support emerging leaders in science from backgrounds underrepresented in STEM, awarding scholarships, mentorship, and career opportunities.

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The fellowships are aimed at helping students from underrepresented backgrounds establish careers in robotics, engineering, computer science, and related fields.

The fellowship program was launched last year with an inaugural class of six recipients across three universities. The program has expanded to support fourteen fellows across seven universities, including Brown University, Boston University, Harvard University, Massachusetts Institute of Technology, Northeastern University, Stanford University, and Worcester Polytechnic Institute.

Recipients receive fully funded fellowships in robotics, engineering, computer science, and related fields that will cover tuition, living expenses, and other costs.

Fellowship recipients also have the opportunity to participate in Amazon Robotics’ internship program. During their summer at Amazon Robotics, the Fellows connect with and receive mentorship from industry experts and members of leadership to gain hands-on experience in their chosen field. Fellows seeking full time industry positions also have the opportunity to join Amazon at the conclusion of their graduate studies.

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Three of Amazon’s leading roboticists — Sidd Srinivasa, Tye Brady, and Philipp Michel — discuss the challenges of building robotic systems that interact with human beings in real-world settings.

“We have selected and invested in another outstanding class of future scientists and engineers to pursue some of the hardest problems in our field at some of the best academic institutions on the planet. We are excited to be a part of their journey to greatness,” said Tye Brady, chief technologist, Amazon Robotics.

The fourteen recipients of the 2022 Day One Amazon Robotics Fellowships are:

Omoruyi Atheka, Stanford University: Atheka will pursue his master's in mechanical engineering at Stanford University where he hopes to become an expert in robotics and develop his problem-solving skills and research independence. He will receive his bachelor’s at MIT in mechanical engineering with a concentration in optics, with a minor in design and political science. During his time at MIT, he has gained extensive knowledge relating to mechanical engineering, technology, and design.

Camille Anne Chungyoun, Stanford University: Chungyoun will pursue a master’s in robotics at Stanford University, where she hopes to conduct research in robotic locomotion and bio-inspired robotics, ultimately allowing her to work in an R&D industry position where she can use robotic locomotion to advance human health and well-being. She is currently finishing her bachelor’s in mechanical engineering, with a concentration in mechatronics, at the University of Washington.

Asbel Fontanez, Boston University: Fontanez is pursuing a master’s in robotics and autonomous systems at Boston University where he earned his bachelor’s in electrical engineering with a concentration in machine learning. In addition to spending more than 10 years participating in robotics competitions, he has also worked with engineers from Florida Power & Light, Motorola Solutions, and SpaceX. His experiences have given him a greater foundational understanding in many areas of engineering, including mechanical, electrical, and computer engineering.

Zakar Handricken, Northeastern University: Handricken is pursuing a master's in computer science at Northeastern in the Khoury College of Computer Sciences, Institute for Experiential Robotics, led by Taskin Padir. He earned his bachelor’s in computer science from Bridgewater State University where he participated in undergraduate research and worked as an intern at AcadiaSoft. He later joined Fidelity Investments as a software engineer, where he worked on projects under Fidelity's Center for Applied Technology and Data Warehouse. At Fidelity, he began his independent study of artificial intelligence to understand and research its application in robotics, data systems, and more within multidisciplinary areas.

Abubakarr Jaye, Brown: Jaye is pursuing a master’s of engineering with an emphasis on machine learning (ML) at Brown University. He received his bachelor’s in computer science and economics at the University of Illinois Urbana-Champaign. It was there he learned of ML through a friend who demonstrated an animal image neural net classifier built from scratch. His focus is currently on the application of machine learning in finance and economics.

Christopher LeBlanc, Northeastern University: LeBlanc will pursue a master's in artificial intelligence with a specialization in robotics and agent-based systems. He interned for the Louisiana Material Design Alliance, a group concerned with the innovation of novel manufacturing methods. LeBlanc obtained his bachelor's from Louisiana State University in computer science with a minor in chemistry. He decided to follow a career in artificial intelligence to satisfy his long-held curiosity about how a machine could learn. His research interests include robotics, reinforcement learning, and their applications in the automation of industrial systems.

Janeth Meraz, Brown: Meraz is pursuing a master’s degree in computer science at Brown. She earned dual bachelor's degrees in computer science and mathematics from the University of Texas at El Paso. She worked as a researcher studying the optimization of neural network weight-initialization in Diego Aguirre's Applied Intelligence Research Lab, and is a member of the Association for Computing Machinery. Meraz has also served as a mentor in the Computing Alliance for Hispanic Serving Institutions Allyship Program.

Jessie Mindel, MIT: Mindel will earn her bachelor’s in electrical engineering and computer science with an emphasis on new media at University of California, Berkeley. At the core of her work lies storytelling, placemaking, and community-centered design. She seeks to build embodied, empathetic, and narrative technologies that help people better understand themselves, more meaningfully connect with others, and more creatively explore their worlds.

Naana Obeng-Marnu, MIT: Obeng-Marnu will pursue a master’s in media arts and sciences at the MIT Media Lab under the Center for Constructive Communication. She graduated with honors from Brown University with a degree in English, nonfiction writing. She was a premier partner experience operations associate at Meta where she built frameworks and automated processes to better support creators and publishers. As secretary of the board of directors for Brown Broadcasting Service she works alongside industry leaders in new media to support and mentor Brown University students interested in media, design, and tech careers.

Kimberly Llajaruna Peralta, Harvard: Peralta will pursue a master’s degree in data science at Harvard University. She earned her bachelors from the University of Rochester in mechanical engineering and studio arts, where she also worked at Corning as a mechanical process engineer. She developed an interest in data driven decision making during her time at the Corning lens manufacturing facility while working on projects to determine optimal tolerances for manufacturing tools and to design tools that improve the precision of coaters.

Priscila Rubio, Boston University: Rubio will pursue a master’s of science in robotics and autonomous systems at Boston University. She previously interned at the National Institutes of Health, where she investigated the activation mechanism of A3 adenosine receptors. She later interned at Northrop Grumman where she worked to help design mechanical ground support equipment for the Minotaur rocket. She received her bachelors in mechanical engineering at the University of Maryland. There, she worked at US Medical Innovations and used her mechatronics knowledge to extend the capabilities of surgical instruments.

Antonio Sanchez, Worcester Polytechnic Institute: Sanchez will pursue a master’s in either soft robotics or human/robot interaction in the WPI Soft Robotics lab. He will receive his bachelor’s at Texas A&M in mechatronics, where, throughout his undergraduate career, he held several engineering internships. He is interested in embedded electronic systems, machine learning, and computer science.

Augustus ‘ Gus’ Teran, Worcester Polytechnic Institute: Teran is pursuing a master’s in robotics engineering with a focus on multi-robot systems at WPI, where he earned dual bachelor’s degrees in computer science and engineering. He was one of the authors of “Air-Releasable Soft Robots for Explosive Ordnance Disposal” which explored using soft robotics to assist in the de-mining of land mines. The paper was accepted by the IEEE International Conference on Soft Robotics.

Walter Williams, Harvard: Williams will pursue a master’s of engineering in computational science and engineering at Harvard University. He is currently finishing his bachelor's degree in computer science at University of Memphis. There he worked at the Cybersecurity Lab of the Center for Information Assurance, focusing on machine learning based applications in cybersecurity. He has also competed in machine learning competitions, finishing in the top 11% of Kaggle's 2020 Plant Pathology competition.

Read about the teams that are creating the next robotics innovations at Amazon, see job opportunities, and find out more about Amazon's participation at ICRA.

Research areas

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US, VA, Arlington
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The architectural decisions about how scientific methods get encoded into software, the engineering quality bar for the code that implements them, and the reliability of the pipelines that other teams depend on are yours to own. You will work alongside Senior and Principal Research Scientists, an Amazon Scholar, Product Management, and a Senior Applied Scientist who bring deep expertise in behavioral science, psychometrics, and causal inference, and you will be the driving force behind turning that expertise into working, deployable systems for our Amazon executives. The problems you will be building for are genuinely hard and largely unsolved. Scoring a simulation-based leadership assessment with an LLM requires both measurement rigor and a production system that behaves consistently at scale. Estimating the effect of a talent program on leader outcomes requires both a defensible identification strategy and an analytical pipeline someone else can run and trust. Building a self-serve tool that lets a PXT team evaluate a new feature without calling a scientist requires both sound methodology and software that is robust enough to operate without expert supervision. If you want to do work that is technically demanding, scientifically cutting edge, and consequential for real leaders in a large organization, this is that role. Key job responsibilities • Own the production implementation of the team's scientific systems from end to end. 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US, NY, New York
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US, CA, San Francisco
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US, NY, New York
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IN, KA, Bengaluru
Every product a customer returns is a moment where Amazon either recovers value or writes it off — and India's ReCommerce business is on a multi-million-dollar mission to recover more of it, more intelligently, at scale. Machine learning is the core lever: predicting whether a returned unit is sellable without a human touching it, detecting damage and fraud inside sealed packaging from images, routing each unit to its highest-value disposition, and pricing recovered inventory dynamically. India's returns network is large, fast-growing, and structurally different from other geographies — a rich, high-impact environment for an Applied Scientist to build models that move real financial and customer-experience metrics. We are hiring an Applied Scientist to build and adapt the ML that powers India ReCommerce. You will work at the intersection of two mandates: building India-first models for problems unique to our market, and adapting proven Worldwide models to India's data, catalog, and operational reality — recalibrating them where distribution, language, and process differ. You will own problems end-to-end, from framing and data through modeling, evaluation, and production deployment, partnering closely with engineering, product, and operations. Key job responsibilities Build ML models for automated returns grading — predicting the salability of returned units from structured and unstructured signals so units can be evaluated with zero or minimal human touch, improving speed, accuracy, and recovery value. Develop computer-vision models for defect detection, condition assessment, and anomaly/fraud identification (including inside sealed packaging), and for establishing chain-of-custody and damage attribution across the returns journey. Build disposition-prediction and routing models that direct each unit to its highest-value recovery path (resale, repair, liquidation, donation, recycle) as early as possible in the network. Develop pricing and recovery-optimization models for liquidation and resale, moving from flat rates toward dynamic, grade- and condition-aware pricing. Adapt Worldwide ML models to India — retraining, recalibrating, and re-evaluating for India's return distribution, catalog, languages, and operational constraints, and closing the gaps that prevent a direct lift-and-shift. Own the full model lifecycle — problem framing, data pipelines, feature engineering, training, offline/online evaluation, monitoring, and retraining — with rigorous attention to calibration, drift, and business-metric impact. Partner cross-functionally with engineering (to productionize), product (to frame problems and measure impact), and operations (to ground models in how the network actually runs), and use modern GenAI/LLM tooling to accelerate research and delivery. A day in the life You start by reviewing the performance of a grading model in production — checking calibration and drift against last week's returns, and confirming the recovery-value lift is holding. Mid-morning, you dig into a computer-vision problem: improving detection of a damage type that's driving write-offs, using images captured across the returns journey. In the afternoon you work with a Worldwide science team to bring one of their models to India — scoping what retraining and recalibration India's data requires — then pair with an engineer to move your latest model toward production behind a clean evaluation gate. You close by framing a new problem with a product partner: quantifying the opportunity, defining the label and success metric, and sketching the modeling approach. About the team India ReCommerce owns the systems and science that turn returned and unsellable inventory into recovered value and a better customer experience. You will join a team building an increasingly automated, ML-driven returns network — leveraging Worldwide platforms where they fit and building India-first capabilities where they don't. It is a high-ownership environment with a direct line from your models to measurable business and customer outcomes.
US, WA, Seattle
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IN, MH, Mumbai
Amazon Science gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work. Please visit https://www.amazon.science for more information. About Amazon Prime Video “Many of the problems we face have no textbook solution, and so we-happily-invent new ones.” – Jeff Bezos
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US, WA, Seattle
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CN, 31, Shanghai
Worldwide Global Selling has been helping individuals and businesses increase sales and reach new customers around the globe. Today, more than 50% of Amazon's total unit sales come from third-party selection. The Global Selling team in China is responsible for recruiting local businesses to sell on Amazon's 19+ overseas marketplaces and supporting local Sellers' success and growth on Amazon. Our vision is to be the first choice for all types of Chinese business to go globally. The Worldwide Global Selling Analytics, Intelligence, and Technology (WWGS-AIT) team serves as the research, automation, and insight arm of the International Seller Service data hub, enabling rapid delivery of growth insights through strategic investments in regional data foundations, self-service business intelligence solutions, and artificial intelligence tools. The WWGS-AIT team is positioned to establish AI-ready foundational capabilities across the WWGS organization while maintaining excellence in business insight generation, and self-service BI/AI application development. WWGS-AIT is looking for a Data Scientist to design and build seller-facing AI agents that turn our AI-ready data foundation into intelligent, conversational experiences for Amazon's global sellers. You will own the intelligence layer of these agents end-to-end, from modeling and retrieval to evaluation and launch, working alongside applied scientists, data engineers, and the Seller Assistant platform team to put trustworthy AI directly into sellers' hands. Key job responsibilities - Design, build, and iterate seller-facing AI agents (LLM-powered) that help Chinese sellers grow globally, reasoning over WWGS-AIT's AI-ready data foundation and knowledge base. - Develop the intelligence layer of agents: retrieval-augmented generation (RAG) over our knowledge management system, tool-use / function-calling orchestration, prompt engineering, and model fine-tuning or adaptation where needed. - Ground agent responses in standardized metrics and unified seller profiles to guarantee consistency and accuracy across agents; design and enforce guardrails that prevent hallucination and protect sensitive, compliance-restricted data. - Build rigorous evaluation frameworks (golden datasets, offline evaluation, and online experimentation) to measure and continuously improve agent quality, safety, and seller impact. - Develop seller-intelligence models (segmentation, entity resolution / One-ID, ranking and recommendation) that power personalized agent experiences. - Partner with WWGS Tech and the Seller Assistant platform team to productionize agents and tools (e.g., via MCP), defining the model and intelligence contract while engineering operates the runtime. - Collaborate with business, product, and cross-functional partners to translate seller pain points into agent capabilities and measurable business outcomes. - Stay current with advances in GenAI and agentic systems, and bring applied research into production.