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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“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.

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US, MA, Boston
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US, CA, San Francisco
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US, NY, New York
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US, CA, San Francisco
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CA, BC, Vancouver
The Alexa Daily Essentials team delivers experiences critical to how customers interact with Alexa as part of daily life. Alexa users engage with our products across experiences connected to Timers, Alarms, Calendars, Food, and News. Our experiences include critical time saving techniques, ad-supported news audio and video, and in-depth kitchen guidance aimed at serving the needs of the family from sunset to sundown. As a Data Scientist on our team, you'll work with complex data, develop statistical methodologies, and provide critical product insights that shape how we build and optimize our solutions. You will work closely with your Analytics and Applied Science teammates. You will build frameworks and mechanisms to scale data solutions across our organization. If you are passionate about redefining how AI can improves everyone's daily life, we’d love to hear from you. Key job responsibilities Problem-Solving - Analyze complex data to identify patterns, inform product decisions, and understand root causes of anomalies. - Develop analysis and modeling approaches to drive product and engineering actions to identify patterns, insights, and understand root causes of anomalies. Your solutions directly improve the customer experience. - Independently work with product partners to identify problems and opportunities. Apply a range of data science techniques and tools to solve these problems. Use data driven insights to inform product development. Work with cross-disciplinary teams to mechanize your solution into scalable and automated frameworks. Data Infrastructure - Build data pipelines, and identify novel data sources to leverage in analytical work - both from within Alexa and from cross Amazon - Acquire data by building the necessary SQL / ETL queries Communication - Excel at communicating complex ideas to technical and non-technical audiences. - Build relationships with stakeholders and counterparts. Work with stakeholders to translate causal insights into actionable recommendations - Force multiply the work of the team with data visualizations, presentations, and/or dashboards to drive awareness and adoption of data assets and product insights - Collaborate with cross-functional teams. Mentor teammates to foster a culture of continuous learning and development
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The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As a Senior Applied Scientist on the team, you will be at the forefront of innovation, developing measurement solutions end-to-end from inception to production. You will set the technical vision and innovate on behalf of our customers. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. You will partner with engineering to deploy these solutions into production. You will work with key stakeholders from various business teams to enable advertisers to act upon those metrics. Key job responsibilities * Lead the development of ad measurement models and solutions that address the full spectrum of an advertiser's investment, focusing on scalable and efficient methodologies. * Collaborate closely with cross-functional teams including engineering, product management, and business teams to define and implement measurement solutions. * Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models that measure the impact of ad spend across multiple platforms and timescales. * Drive experimentation and the continuous improvement of ML models through iterative development, testing, and optimization. * Translate complex scientific challenges into clear and impactful solutions for business stakeholders. * Mentor and guide junior scientists, fostering a collaborative and high-performing team culture. * Foster collaborations between scientists to move faster, with broader impact. * Regularly engage with the broader scientific community with presentations, publications, and patents. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate business 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 advertising organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Team video https://advertising.amazon.com/help/G4LNN5YWHP6SM9TJ About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.
CA, ON, Toronto
The Brand Registry team is seeking an Applied Scientist to tackle complex, high-impact problems that directly affect millions of brands, selling partners, and customers on Amazon. You will design, develop, and deploy AI solutions—leveraging large language models (LLMs) and agentic AI frameworks—to power intelligent automation that augments human decision-making and drives autonomous outcomes at scale. What You'll Do -Build agent-based AI systems that reason, plan, and act like domain experts progressing from decision-support tools to fully autonomous solutions -Own the end-to-end ML lifecycle, from problem formulation and data analysis through experimentation, model development, and production deployment -Work backwards from data insights and customer feedback to identify the highest-value science opportunities and translate them into scalable machine learning solutions -Partner closely with product managers and engineering teams to define requirements, iterate rapidly, and launch solutions that deliver measurable business impact -Collaborate with domain experts across Amazon to pioneer innovative approaches to unsolved problems in brand protection and seller experience What We're Looking For -Technical depth: Extensive hands-on experience in Machine Learning, with a strong focus on Generative AI and LLM-based applications (e.g., fine-tuning, prompt engineering, retrieval-augmented generation, multi-agent orchestration) -End-to-end delivery: Proven track record of driving large-scale ML initiatives from conception through production launch in fast-paced, ambiguous environments -Scientific rigor: Strong foundation in experimental design, statistical analysis, and the ability to translate research into production-grade systems -Customer obsession: A bias toward working backwards from real-world problems and customer pain points rather than technology for its own sake -Entrepreneurial mindset: Comfort with ambiguity, a bias for action, and the tenacity to break down complex problems into actionable solutions -Communication skills: Ability to articulate technical concepts clearly to both technical and non-technical stakeholders About the team Brand Registry's mission is bold and unambiguous: protect 100% of the brands in the Amazon catalog. We are the team that stands between authentic brands and the forces that threaten their integrity — counterfeit products, catalog abuse, unauthorized sellers, and inaccurate brand representation. We do this by building the tools, systems, and experiences that empower brand owners to establish, protect, and grow their presence on Amazon with confidence. Achieving this mission requires deep collaboration across science, engineering, legal, and selling partner experience teams — all working in concert to deliver a seamless, trustworthy brand ownership experience at global scale.
US, CA, San Francisco
The Amazon AGI SF Lab is focused on developing new foundational capabilities for enabling useful AI agents that can take actions in the digital and physical worlds. We’re enabling practical AI that can actually do things for us and make our customers more productive, empowered, and fulfilled. The lab is designed to empower AI researchers and engineers to make major breakthroughs with speed and focus toward this goal. Our philosophy combines the agility of a startup with the resources of Amazon. By keeping the team lean, we’re able to maximize the amount of compute per person. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. In this role, you will work closely with research teams to design, build, and maintain systems for training and evaluating state-of-the-art agent models. Our team works inside the Amazon AGI SF Lab, an environment designed to empower AI researchers and engineers to work with speed and focus. Our philosophy combines the agility of a startup with the resources of Amazon. Key job responsibilities * Develop training infrastructure to ensure large-scale reinforcement learning on LLMs runs highly efficient and robust. * Work across the entire technology stack, including low level ML system, job orchestration and data management. * Analyze, troubleshoot and profiling complex ML systems, identify and address performance bottlenecks. * Work closely with researchers, conduct MLSys research to create new techniques, infrastructure, and tooling around emerging research capabilities.
US, WA, Seattle
At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities Key job responsibilities include: * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research
US, WA, Seattle
Amazon is seeking exceptional science talent to develop AI and machine learning systems that will enable the next generation of advanced manufacturing capabilities at unprecedented scale. We're building revolutionary software infrastructure that combines cutting-edge AI, large-scale optimization, and advanced manufacturing processes to create adaptive production control systems. As a Senior Research Scientist, you will develop and improve machine learning systems that enable real-time manufacturing flow decisions. You will leverage state-of-the-art optimization and ML techniques, evaluate them against representative manufacturing scenarios, and adapt them to meet the robustness, reliability, and performance needs of production environments. You will invent new algorithms where gaps exist. You'll collaborate closely with software engineering, manufacturing engineering, robotics simulation, and operations teams, and your outputs will directly power the systems that determine what to build next, where to allocate resources, and how to maximize throughput. The ideal candidate brings deep expertise in optimization and machine learning, with a proven track record of delivering scientifically complex solutions into production. You are hands-on, writing significant portions of critical-path scientific code while driving your team's scientific agenda. If you're passionate about inventing the intelligent manufacturing systems of tomorrow rather than optimizing those of today, this role offers the chance to make a lasting impact on the future of automation. Key job responsibilities - Identify and devise new scientific approaches for constraint identification, dispatch optimization, WIP release control, and predictive flow intelligence when the problem is ill-defined and new methodologies need to be invented - Lead the design, implementation, and successful delivery of scientifically complex solutions for real-time manufacturing flow optimization in production - Design and build ML models and optimization algorithms including constraint prediction, starvation risk forecasting, and dispatch optimization - Write a significant portion of critical-path scientific code with solutions that are inventive, maintainable, scalable, and extensible - Execute rapid, rigorous experimentation with reproducible results, closing the gap between simulation and real manufacturing environments - Build evaluation benchmarks that measure model performance against manufacturing outcomes including constraint utilization and throughput rather than traditional ML metrics alone - Influence your team's science and business strategy through insightful contributions to roadmaps, goals, and priorities - Partner with manufacturing engineering, robotics simulation, and applied intelligence teams to ensure scientific approaches are grounded in operational reality - Drive your team's scientific agenda and role model publishing of research results at peer-reviewed venues when appropriate and not precluded by business considerations - Actively participate in hiring and mentor other scientists, improving their skills and ability to deliver - Write clear narratives and documentation describing scientific solutions and design choices