Amazon Physical Science Fellowship winners announced

Award recognizes three individuals who have shown the skills necessary to bridge the gap between fundamental scientific results in the physical sciences and the development of impactful technologies.

The Amazon Physical Science Fellowship was developed to foster collaboration between Amazon and the physics community for the purpose of accelerating the time from fundamental discovery to real-world application. More than 2,000 physics professors from around the world were invited to identify game-changing discoveries from the past two decades that could lead to products and services that will positively impact future generations.

These three selected fellows demonstrated an ability to identify scientific results from across the physical sciences with the potential to provide broad, positive impacts to society.

The winners are listed below.

Xiwen Gong

Xiwen Gong, PhD, is an assistant professor of chemical engineering at the University of Michigan, where she focuses on developing the next generation of soft electronic materials and devices by utilizing a transdisciplinary approach that unites physics, chemistry, and engineering.

Xiwen Gong
Xiwen Gong

Before joining the University of Michigan, Gong — who is also by courtesy an assistant professor of electrical and computer engineering, materials science and engineering, macromolecular science and engineering, and applied physics — worked as a post-doctoral fellow with Zhenan Bao, the K. K. Lee Professor of Chemical Engineering, at Stanford University’s Department of Chemical Engineering. At Stanford, Gong focused on developing soft and stretchable semiconductors and devices for wearable electronics (inSPIREd Talk). In 2018, Gong earned her PhD in electrical and computer engineering with Edward Sargent, University Professor of electrical and computer engineering, at the University of Toronto. During her PhD studies, Gong focused on the design of novel materials for solar energy harvesting, light emitting, and sensing. Her work has been published in Nature, Nature Materials, Nature Photonics, and other leading science publications. Gong received the Extraordinary Potential Prize and the “Rising Stars in EECS 2017” (Stanford University). In 2018, she was selected as one of the fourteen inaugural Schmidt Science Fellows.

Eric Ma

Eric Y. Ma PhD, is an assistant professor in physics and electrical engineering and computer science and the Georgia Lee Chair in Physics at the University of California, Berkeley. His research focuses on electromagnetic-matter interaction in uncommon regimes.

Eric Ma
Eric Ma

On the one hand, he develops new instruments that use microwave and light to probe the fundamental properties of quantum materials. On the other hand, he creates new devices and structures that use unconventional materials and inverse design to generate, manipulate, and detect electromagnetic fields. His research interests are expansive, though he is particularly excited about beyond-von–Neumann computing and human-computer interface.

Before joining UC Berkeley, Ma earned his PhD in applied physics at Stanford University, where he also conducted postdoc studies in applied physics and electrical engineering. He was also briefly a senior scientist at Apple. Ma is passionate about advancing access to undergraduate research and broadening collaborations between physics and engineering.

Tomas Martin

Tomas Martin, PhD, is senior lecturer in materials physics within the School of Physics at the University of Bristol, and director of the university’s Master of Science in Nuclear Science and Engineering program.

Tomas Martin
Tomas Martin

After earning a PhD at Bristol investigating the electronic properties of diamond surfaces, Martin worked in the renewable energy industry as a bank’s engineer on wind and solar power projects around the world, followed by four years as David Cockayne Junior Research Fellow in Materials at the University of Oxford. Martin is editor-in-chief of the scientific journal Materials Today Communications and is a published science fiction author.'

Martin’s research uses advanced microstructural characterization techniques to understand the structure and chemistry of materials for nuclear power plants, semiconductor devices and aerospace. His work aims to take a holistic approach to materials characterization using a combination of experimental techniques and computer modeling to understand the mechanisms behind materials behavior across the length scales, from individual atomic defects to large-scale stresses and chemistry changes in engineering components.

Martin is part of the core academic team running the University of Bristol’s Interface Analysis Centre microscope facility. His research group uses techniques including atom probe tomography, focused ion beam and electron microscopy, complemented by computational modeling, to understand materials degradation challenges such as corrosion, creep and radiation damage. He works with collaborators in many fields of academic research, as well as with industrial partners including EDF Energy, NNL, Rolls Royce and UKAEA.

Below are the Review Board of the Amazon Physical Science Fellowship, a distinguished group from academia and industry.

Review Board members

Philip Kim.jpg
Philip Kim

Philip Kim - Professor Philip Kim received his B.S in physics at Seoul National University in 1990 and received his Ph.D. in Applied Physics from Harvard University in 1999. He was Miller Postdoctoral Fellow in Physics from University of California, Berkeley during 1999-2001. He then joined the Department of Physics at Columbia University as a faculty member from 2002-2014. In 2014, he moved to Harvard University, where he is Professor of Physics and Professor of Applied Physics.

The focus of Prof. Kim’s group research is the mesoscopic investigation of transport phenomena, particularly, electric, thermal and thermoelectrical properties of low dimensional nanoscale materials. These materials include carbon nanotubes, organic and inorganic nanowires, 2-dimensional mesoscopic single crystals, and single organic molecules.

Professor Kim also received numerous honors and award including Tomassoni-Chisesi Prizes (2018); Vannevar Bush Faculty Fellowship (2018); Oliver E. Buckley Prize, American Physical Society (2014); Dresden Barkhausen Award (2012); IBM Faculty Award (2009); and Ho-Am Science Prize (2008). He is Elected member of the American Academy of Arts and Science (2020) and American Physical Society Fellow (2007). He graduated 21 PhD students and trained 32 postdoctoral fellows.

Young-Kee Kim - Young-Kee Kim is the Louis Block Distinguished Service Professor of Physics and Senior Advisor to the Provost for Global Scientific Initiatives at the University of Chicago. She is an experimental particle physicist, and devotes much of her research to understanding the origin of mass for fundamental particles.

Young-Kee Kim copy.jpg
Young-Kee Kim

Between 2004 and 2006, she co-led the CDF experiment at Fermilab and was Deputy Director of Fermilab between 2006 and 2013. She is currently working on the ATLAS particle physics experiment at the Large Hadron Collider at CERN as well as on accelerator physics research. Prior to Chicago, Young-Kee Kim was Professor of Physics at University of California, Berkeley. She was born in South Korea, and earned her BS and MS in Physics from Korea University, in 1984 and 1986, respectively, and her Ph.D. in Physics from the University of Rochester in 1990.

She conducted her postdoctoral research at Lawrence Berkeley National Laboratory. Young-Kee is a Fellow of the National Academy of Sciences, the American Academy of Arts and Sciences, the American Physical Society, the American Association for the Advancement of Science, and the Sloan Foundation. She received the Ho-Am Prize, the Women in Science Leadership Award from the Chicago Council of Science and Technology, the University of Rochester’s Distinguished Scholar Medal, and Korea University’s Alumni Award.

Hideo Mabuchi image.jpg
Hideo Mabuchi

Hideo Mabuchi - Hideo Mabuchi received an AB in Physics from Princeton and a PhD in Physics from Caltech. He served as Chair of the Department of Applied Physics at Stanford from 2010-2016.

His early scientific research was focused on understanding open quantum systems, quantum measurement, and the quantum-to-classical transition. In recent years his research group has turned towards fundamental issues of quantum engineering, such as quantum nonlinear dynamics, quantum feedback control and quantum model reduction. Along the way his group has also worked substantially on single-molecule biophysics, quantum information science, and quantum materials.

Major awards include the inaugural Mohammed Dahleh Distinguished Lectureship (UCSB) and a Fellowship from the John D. and Catherine T. MacArthur Foundation.

Matt McIlwain - Managing Director, Madrona Venture Group - Madrona is a venture capital firm based in Seattle, investing in mainly seed and Series A technology-based companies. For over two decades, the firm has been helping technology entrepreneurs launch and grow world-class companies

Matt McIlwain.jpg
Matt McIlwain

At Madrona, Matt invests in a broad range of software and data driven companies with a focus on cloud computing, dataware, intelligent applications and the intersections of innovation (where life science and data science intersect).

He believes in the Learning Loop for entrepreneurs who journey from curiosity to triangulation and decision making. This leads to positive outcomes and ongoing learnings. Matt has been named several times to the Forbes Midas List and list of Top 100 Venture Capitalists by CB Insights and The New York Times.

He was named Emerging Company Director of the year by the Puget Sound Business Journal. In 2011, he received the Washington Policy Center’s Champion of Freedom Award. Matt is a board member (and previous chair) of Fred Hutchinson Cancer Research Center and a board member of Washington Policy Center.

Matt enjoys going on adventures with his family, discussing public policy issues and trying out new technologies. Matt is a graduate of Dartmouth College and holds an MBA from Harvard Business School and a Master’s in Public Policy from Harvard’s Kennedy School of Government.

José Onuchic - José Onuchic is the Harry C & Olga K Wiess Professor of Physics and Astronomy, Chemistry and Biosciences at Rice University and the co-Director of the NSF-sponsored Center for Theoretical Biological Physics. His research looks at theoretical methods for molecular biophysics and gene networks.

Jose Onuchic.jpg
José Onuchic

He introduced the concept of protein folding funnels. Energy landscape theory and the funnel concept provide the framework needed to pose and to address the questions of protein folding and function mechanisms. He developed the tunneling pathways concept for electron transfer in proteins. He is also interested in stochastic effects in genetic networks with applications to bacteria decision-making and cancer. Further expanding his ideas coming from energy landscapes for protein folding, his group is now exploring chromatin folding and function and therefore modeling the 3D structure of the genome. He has received much recognition for his achievements. He was elected to the National Academy of Sciences in 2006.

He received the ICTP Prize in honor of Heisenberg in Trieste, Italy (1989) and the Beckman Young Investigator Award (1992). He is a fellow of the American Physical Society (1995), the American Academy of Arts and Sciences (2009), the Brazilian Academy of Sciences (2009), the Biophysical Society (2012) and the American Association for the Advancement of Science (2017). He received the Einstein Professorship by the Chinese Academy of Sciences (2011).

In 2014 he received the Diaspora Prize from the Ministry of Foreign Affairs and the Ministry of Industrial Development and Foreign Trade from Brazil. In 2015 he received The International Union of Biochemistry and Molecular Biology Medal. In 2018 he received National Order of Scientific Merit by the Brazilian National Council in Science and Technology. He received the 2019 American Physical Society’s Max Delbruck Prize in Biological Physics and was elected to Pontifical Academy of Sciences in 2020.

Babak Parviz - Vice President | Amazon — Babak is a Vice President at Amazon, and has led the launch of products/services such as Amazon Care, Amazon Comprehend Medical, Echo Frames, and Amazon Explore.

BabakParviz.jpg
Babak Parviz

Prior to joining Amazon in 2014, Babak was with Google as a Distinguished Engineer and Director at Google [x] where he built Google Glass and founded the robotic surgery and the active contact lens programs.

Babak received his BA in Literature (University of Washington), BS in Electronics (Sharif University of Technology), MS in Physics and MS and PhD in Electrical Engineering (University of Michigan), and completed his postdoctoral fellowship in Chemistry and Chemical Biology at Harvard University. He has received numerous recognitions including NSF Career Award, MIT Technology Review 35, University of Michigan Bicentennial Alumni Award, Time magazine’s best invention of the year, and IEEE CAS Industrial Pioneer Award.

Simone Severini - Simone Severini is a Professor of Physics of Information at University College London and is the Director of Quantum Computing at AWS.

Simone Severini.jpg
Simone Severini

As Director, Simone contributed to grow the initiatives of AWS in Quantum Technologies, including the Amazon Braket service and the AWS Center for Quantum Computing in partnership with Caltech.

During his academic career, Simone served as a grant reviewer for EPSRC (UK), NSF (US), NSFC (China), European Commission, Research Council of Norway, National Science Center (Poland), Dutch Research Council, Israel Science Foundation, MITACS (Canada), NSERC (Computer Science Evaluation Group), The Royal Society (International Exchanges Committee).

Our inspiration

Building on the formulation of Maxwell’s equations in 1865, Heinrich Hertz demonstrated in 1888 that radio waves can be generated, transmitted, and detected in a laboratory setting. Though Hertz doubted this discovery would lead to any practical application, it provided the game-changing experimental results that inspired Guglielmo Marconi to develop a viable radio system, transmitting the first signals across the Atlantic Ocean in 1902.

It took fourteen years from an important experimental observation by Hertz until the radio became widespread. Now, we are interested in identifying key scientific findings since the year 2000 and working with big thinkers who can help foster their development into engineered products and services at a much faster pace. We aim to identify the modern-day equivalents of the Hertz experiment.

Related content

ES, B, Barcelona
Are you interested in defining the science strategy that enables Amazon to market to millions of customers based on their lifecycle needs rather than one-size-fits-all campaigns? We are seeking a Applied Scientist to lead the science strategy for our Lifecycle Marketing Experimentation roadmap within the PRIMAS (Prime & Marketing analytics and science) team. The position is open to candidates in Amsterdam and Barcelona. In this role, you will own the end-to-end science approach that enables EU marketing to shift from broad, generic campaigns to targeted, cohort-based marketing that changes customer behavior. This is a high-ambiguity, high-impact role where you will define what problems are worth solving, build the science foundation from scratch, and influence senior business leaders on marketing strategy. You will work directly with Business Directors and channel leaders to solve critical business problems: how do we win back customers lost to competitors, convert Young Adults to Prime, and optimize marketing spend by de-averaging across customer cohorts. Key job responsibilities Science Strategy & Leadership: 1. Own the end-to-end science strategy for lifecycle marketing, defining the roadmap across audience targeting, behavioral modeling, and measurement 2. Navigate high ambiguity in defining customer journey frameworks and behavioral models – our most challenging science problem with no established playbook 3. Lead strategic discussions with business leaders translating business needs into science solutions and building trust across business and tech partners 4. Mentor and guide a team of 2-3 scientists and BIEs on technical execution while contributing hands-on to the hardest problems Advanced Customer Behavior Modeling: 1. Build sophisticated propensity models identifying customer cohorts based on lifecycle stage and complex behavioral patterns (e.g., Bargain hunters, Young adults Prime prospects) 2. Define customer journey frameworks using advanced techniques (Hidden Markov Models, sequential decision-making) to model how customers transition across lifecycle stages 3. Identify which customer behaviors and triggers drive lifecycle progression and what messaging/levers are most effective for each cohort 4. Integrate 1P behavioral data with 2P survey insights to create rich, actionable audience definitions Measurement & Cross-Workstream Integration: 1. Partner with measurement scientist to design experiments (RCTs) that isolate audience targeting effects from creative effects 2. Ensure audience definitions, journey models, and measurement frameworks work coherently across Meta, LiveRamp, and owned channels 3. Establish feedback loops connecting measurement insights back to model improvements About the team The PRIMAS (Prime & Marketing Analytics and Science) is the team that support the science & analytics needs of the EU Prime and Marketing organization, an org that supports the Prime and Marketing programs in European marketplaces and comprises 250-300 employees. The PRIMAS team, is part of a larger tech tech team of 100+ people called WIMSI (WW Integrated Marketing Systems and Intelligence). WIMSI core mission is to accelerate marketing technology capabilities that enable de-averaged customer experiences across the marketing funnel: awareness, consideration, and conversion.
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major 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
ES, M, Madrid
At Amazon, we are committed to being the Earth's most customer-centric company. The European International Technology group (EU INTech) owns the enhancement and delivery of Amazon's engineering to all the varied customers and cultures of the world. We do this through a combination of partnerships with other Amazon technical teams and our own innovative new projects. You will be joining the Tamale team to work on Haul. As part of EU INTech and Haul, Tamale strives to create a discovery-driven shopping experience using challenging machine learning and ranking solutions. You will be exposed to large-scale recommendation systems, multi-objective optimization, and state-of-the-art deep learning architectures, and you'll be part of a key effort to improve our customers' browsing experience by building next-generation ranking models for Amazon Haul's endless scroll experience. We are looking for a passionate, talented, and inventive Scientist with a strong machine learning background to help build industry-leading ranking solutions. We strongly value your hard work and obsession to solve complex problems on behalf of Amazon customers. Key job responsibilities We look for applied scientists who possess a wide variety of skills. As the successful applicant for this role, you will work closely with your business partners to identify opportunities for innovation. You will apply machine learning solutions to optimize multi-objective ranking, improve discovery engagement through contextual signals, and scale ranking systems across multiple marketplaces. You will work with business leaders, scientists, and product managers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed ranking services. You will be part of a team of scientists and engineers working on solving ranking and personalization challenges at scale. You will be able to influence the scientific roadmap of the team, setting the standards for scientific excellence. You will be working with state-of-the-art architectures and real-time feature serving systems. Your work will improve the experience of millions of daily customers using Amazon Haul worldwide. You will have the chance to have great customer impact and continue growing in one of the most innovative companies in the world. You will learn a huge amount - and have a lot of fun - in the process!
IN, HR, Gurugram
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced ML systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real-world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning team for International Emerging Stores (IES). Machine Learning, Big Data and related quantitative sciences have been strategic to Amazon from the early years. Amazon has been a pioneer in areas such as recommendation engines, ecommerce fraud detection and large-scale optimization of fulfillment center operations. As Amazon has rapidly grown and diversified, the opportunity for applying machine learning has exploded. We have a very broad collection of practical problems where machine learning systems can dramatically improve the customer experience, reduce cost, and drive speed and automation. These include product bundle recommendations for millions of products, safeguarding financial transactions across by building the risk models, improving catalog quality via extracting product attribute values from structured/unstructured data for millions of products, enhancing address quality by powering customer suggestions We are developing state-of-the-art machine learning solutions to accelerate the Amazon India growth story. Amazon is an exciting place to be at for a machine learning practitioner. We have the eagerness of a fresh startup to absorb machine learning solutions, and the scale of a mature firm to help support their development at the same time. As part of the International Machine Learning team, you will get to work alongside brilliant minds motivated to solve real-world machine learning problems that make a difference to millions of our customers. We encourage thought leadership and blue ocean thinking in ML. Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions across International Emerging Store (India, MENA, Far-East, LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, WA, Bellevue
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.