Top row, left to right, Ruomeng Cui, Christos Faloutsos, Nicholas Kullman, and Niklas Karlsson; bottom row, left to right, Joan Feigenbaum, Hugo Krawczyk, Aaditya Ramdas, and Aaron Roth.
Top row, left to right, Ruomeng Cui, Christos Faloutsos, Nicholas Kullman, and Niklas Karlsson; bottom row, left to right, Joan Feigenbaum, Hugo Krawczyk, Aaditya Ramdas, and Aaron Roth.

Recent honors and awards for Amazon scientists

Researchers honored for their contributions to the scientific community in 2023.

Ruomeng Cui won Management Science best paper award

Ruomeng Cui, an Amazon Visiting Academic with Amazon’s Supply Chain Optimization Technologies (SCOT) team, won the 2023 Management Science Best Paper Award in Operations Management.

Cui, who is on leave from her role as an associate professor in the department of Information System and Operations Management at the Goizueta Business School, Emory University, won the award along with her co-authors Jun Li and Dennis Zhang for their 2020 paper, “Reducing discrimination with reviews in the sharing economy: Evidence from field experiments on Airbnb.”

Their paper explored ways to reduce “widespread discrimination by hosts against guests of certain races in online marketplaces” by using peer-generated online reviews. Their work has influenced sharing platforms’ strategies to fight discrimination.

The award is given “to the manuscript judged to be most deserving for its contribution to the theory and practice of operations management among all operations papers published in the past 3 years at Management Science.”

Cui earned her PhD in operations management from the Kellogg School of Management, Northwestern University in 2014. In June 2022, she joined Amazon as a Visiting Academic. In that role, she is building and implementing cutting-edge causal inference, machine learning, optimization, and economic models to make supply chain decisions.

Christos Faloutsos won Donald G. Fink Overview Paper Award

Christos Faloutsos, an Amazon Scholar and the Fredkin Professor of Computer Science at Carnegie Mellon University, was part of a team that received the 2023 IEEE Signal Processing Society Donald G. Fink Overview Paper Award by the IEE Signal Processing Society for "Tensor Decomposition for Signal Processing and Machine Learning."

In their 2016 overview paper, Faloutsos and his coauthors — Nicholas D. Sidiropoulos, Lieven De Lathauwer, Xiao Fu, Kejun Huang, and Evangelos E. Papalexakis — noted that while tensors, which are a higher-dimensional analogue of a matrix, already had “a rich history, stretching over almost a century, and touching upon numerous disciplines” their usage had only then “become ubiquitous in signal and data analytics at the confluence of signal processing, statistics, data mining and machine learning." Their overview aimed “to provide a good starting point for researchers and practitioners interested in learning about and working with tensors.”

The IEEE Signal Processing Society Overview Paper Award honors the authors “of a journal article of broad interest that has had substantial impact over several years on a subject related to the Society’s technical scope.”

Faloutsos said he believes the paper’s impact can be attributed to the fact that tensors are powerful tools. “They can handle static graphs, time evolving graphs, knowledge graphs which consist of triplets such as subject, verb, object, e.g., who plays in what team, who lives in, what city, who is friends with whom.”

Faloutsos, who joined Amazon as a Scholar in 2018, researches large-scale data mining with emphasis on graphs and time sequences, anomaly detection, tensors, and fractals.

Nicholas Kullman won 2023 Transportation Science Journal Paper of the Year

Nicholas Kullman, a senior research scientist with Amazon Line Haul, won the 2023 Transportation Science Journal Paper of the Year. Kullman and his coauthors — Martin Cousineau, Justin C. Goodson, and Jorge E. Mendoza — were awarded for their 2021 paper, “Dynamic Ride-Hailing with Electric Vehicles”.

In the paper, the authors “consider the problem of an operator controlling a fleet of electric vehicles for use in a ride-hailing service. The operator, seeking to maximize profit, must assign vehicles to requests as they arise as well as recharge and reposition vehicles in anticipation of future requests.”

“As autonomous vehicles become more common, fleets of taxis may become more centrally coordinated,” Kullman explained. “We wanted to consider this case where there's a central authority that controls whether or not requests are accepted or rejected.

“We wanted to look at good policies for figuring out which vehicles should serve which requests and what do you do with your vehicles when they're not serving requests so that they are better positioned to be able to serve future requests — a sort of dynamic stochastic vehicle routing problem.”

The team utilized deep reinforcement learning to develop new policies. Those policies were compared “against some more classical operations research approaches” and “and against dual bounds on the value of an optimal policy.”

“I think one of the other reasons why the paper was well received was that we had dual bounds,” Kullman explained. “We built out a benchmark where we knew we could not have done better than that standard. Basically, if you're the taxi authority and you know exactly where and when these requests are going to pop up, what would you do?”

The team found its “best policy trained with deep reinforcement learning outperforms the reoptimization approach.” Kullman, who joined Amazon in 2021, earned a PhD in operations research from Université de Tours. At Amazon, he researches optimization of middle-mile linehaul operations.

Niklas Karlsson named IEEE Fellow

Niklas Karlsson, a senior principal research scientist in Amazon Advertising Engineering, was recently named an IEEE Fellow for “technical leadership to vSLAM and online advertising.” The designation took effect on Jan. 1. Karlsson leads a team within Amazon DSP (ADSP) engineering, where he oversees research pertaining to ADSP bidding and optimization.

Karlsson earned a master’s in engineering physics from Lund University and then earned both a master’s in statistics and applied probability and a PhD in control, dynamic systems, and robotics, from UC Santa Barbara. After graduating he joined Evolution Robotics as senior navigation and control engineer. While there, he and his colleagues developed and patented vSLAM (visual simultaneous localization and mapping), an odometry- and vision-based SLAM system.

In 2005, Karlsson transitioned to a role as principal control engineer with There he leveraged his expertise in feedback control and systems engineering to develop a next generation of scalable and adaptive bidding solutions for ad campaign optimization. By way of acquisitions and mergers, he ended up with Yahoo, where, after 17 years in online advertising, he departed as the chief scientist and vice president of research and development for Yahoo’s Demand Side Platform.

The IEEE Fellow designation is conferred by the IEEE board of directors upon individuals with outstanding records of accomplishment in any of the IEEE fields of interest. The total number selected in any one year cannot exceed 0.1% of the total voting membership. IEEE Fellow is the highest grade of membership and is recognized by the technical community as a prestigious honor and an important career achievement.

Joan Feigenbaum named IEEE Fellow

Joan Feigenbaum, an Amazon Scholar and the Grace Murray Hopper professor of computer science at Yale University, will be elevated to IEEE Fellow grade in 2024. The grade of IEEE Fellow “recognizes exceptional distinction in the engineering profession.”

Feigenbaum, who works in the AWS Cryptographic Algorithms group on privacy-preserving computation, was awarded “for contributions to trust-management systems and Internet algorithmics.”

Hugo Krawczyk named IACR Distinguished speaker

Hugo Krawczyk, senior principal scientist, Amazon Web Services, was selected to present the 2023 IACR Distinguished Lecture.

The International Association for Cryptologic Research (IACR) Distinguished Lectures are awarded “to people who have made important contributions to cryptology research.”

Krawczyk, who is also an IACR Fellow, has made fundamental contributions to the cryptographic design of Internet standards like IPsec, IKE, and TLS. He also co-invented numerous cryptographic algorithms including the HMAC message authentication algorithm.

Prior to joining Amazon in July 2023, he was a principal researcher at the Algorand Foundation and part of its founding team. Prior to that, he was an IBM Fellow and Distinguished Research Staff Member at the IBM T.J. Watson Research Center as a member of the Cryptography Research group from 1992 to 1997, and again from 2004 to 2019. He was an associate professor at the Department of Electrical Engineering at the Technion in Israel from 1997 until 2004.

Aaditya Ramdas won Peter Gavin Hall IMS Early Career Prize

Aaditya Ramdas, an Amazon Visiting Academic who is also an assistant professor of statistics and machine learning at Carnegie Mellon University (CMU), won the Peter Gavin Hall Institute of Mathematical Statistics (IMS) Early Career Prize. Ramdas was recognized “for significant contributions in the areas of reproducibility in science and technology; active, sequential decision-making; and assumption-light uncertainty quantification.”

The prize “recognizes one researcher annually who is within the first eight years of completing their doctoral degree.” Ramdas has a bacehlor’s degree in computer science and engineering from IIT-Bombay and earned both a master’s and a PhD in statistics and machine learning from CMU.

Ramdas researches selective and simultaneous inference, game-theoretic statistics, and black-box predictive inference. His areas of applied interest include neuroscience, genetics and auditing.

Aaron Roth named CyLab's 2023 Distinguished Alumni Award winner

Aaron Roth, an Amazon Scholar who is the Henry Salvatori Professor of Computer and Cognitive Science at the University of Pennsylvania, was named Distinguished Alumni Award winner by CyLab, Carnegie Mellon University's security and privacy research institute. The award recognizes “Roth's excellence in algorithms and machine learning, leadership in the field, and commitment to his students.”

Roth, who joined Amazon as a Scholar in 2020, researches the algorithmic foundations of data privacy, algorithmic fairness, game theory, learning theory, and machine learning.

Related content

US, WA, Seattle
The Artificial General Intelligent team (AGI) seeks a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP) and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. As part of this team, you will collaborate with talented peers to create scalable solutions for an innovative conversational assistant, aiming to revolutionize user experiences for millions of Alexa customers. The ideal candidate possesses a solid understanding of machine learning fundamentals and a passion for pushing boundaries in the field. They thrive in fast-paced environments, possess the drive to tackle complex challenges, and excel at swiftly delivering impactful solutions while iterating based on user feedback. Join us in our mission to redefine industry standards and provide unparalleled experiences for our customers. Key job responsibilities . You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. . You will work on core LLM technologies, including developing best-in-class modeling, prompt optimization algorithms to enable Conversation AI use cases · Build and measure novel online & offline metrics for personal digital assistants and customer scenarios, on diverse devices and endpoints · Create, innovate and deliver deep learning, policy-based learning, and/or machine learning based algorithms to deliver customer-impacting results · Perform model/data analysis and monitor metrics through online A/B testing · Research and implement novel machine learning and deep learning algorithms and models. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA | Seattle, WA, USA
US, WA, Redmond
Project Kuiper is an initiative to increase global broadband access through a constellation of 3,236 satellites in low Earth orbit (LEO). Its mission is to bring fast, affordable broadband to unserved and underserved communities around the world. Project Kuiper will help close the digital divide by delivering fast, affordable broadband to a wide range of customers, including consumers, businesses, government agencies, and other organizations operating in places without reliable connectivity. As an Applied Scientist on the team you will responsible for building out and maintaining the algorithms and software services behind one of the world’s largest satellite constellations. You will be responsible for developing algorithms and applications that provide mission critical information derived from past and predicted satellite orbits to other systems and organizations rapidly, reliably, and at scale. You will be focused on contributing to the design and analysis of software systems responsible across a broad range of areas required for automated management of the Kuiper constellation. You will apply knowledge of mathematical modeling, optimization algorithms, astrodynamics, state estimation, space systems, and software engineering across a wide variety of problems to enable space operations at an unprecedented scale. You will develop features for systems to interface with internal and external teams, predict and plan communication opportunities, manage satellite orbits determination and prediction systems, develop analysis and infrastructure to monitor and support systems performance. Your work will interface with various subsystems within Project Kuiper and Amazon, as well as with external organizations, to enable engineers to safely and efficiently manage the satellite constellation. The ideal candidate will be detail oriented, strong organizational skills, able to work independently, juggle multiple tasks at once, and maintain professionalism under pressure. You should have proven knowledge of mathematical modeling and optimization along with strong software engineering skills. You should be able to independently understand customer requirements, and use data-driven approaches to identify possible solutions, select the best approach, and deliver high-quality applications. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. About the team The Constellation Management & Space Safety team maintains and builds the software services responsible for maintaining situational awareness of Kuiper satellites through their entire lifecycle in space. We coordinate with internal and external organizations to maintain the nominal operational state of the constellation. We build automated systems that use satellite telemetry and other relevant data to predict future orbits, plan maneuvers to avoid high risk close approaches with other objects in space, keep satellites in the desired locations, and exchange data with external organizations. We provide visibility information that is used to predict and establish communication channels for Kuiper satellites. We are open to hiring candidates to work out of one of the following locations: Redmond, WA, USA
US, WA, Seattle
Join us in the evolution of Amazon’s Seller business! The Selling Partner Recruitment and Success organization is the growth and development engine for our Store. Partnering with business, product, and engineering, we catalyze SP growth with comprehensive and accurate data, unique insights, and actionable recommendations and collaborate with WW SP facing teams to drive adoption and create feedback loops. We strongly believe that any motivated SP should be able to grow their businesses and reach their full potential by using our scaled, automated, and self-service tools. We aim to accelerate the growth of Sellers by providing tools and insights that enable them to make better and faster decisions at each step of selection management. To accomplish this, we offer intelligent insights that are both detailed and actionable, allowing Sellers to introduce new products and engage with customers effectively. We leverage extensive structured and unstructured data to generate science-based insights about their business. Furthermore, we provide personalized recommendations tailored to individual Sellers' business objectives in a user-friendly format. These insights and recommendations are integrated into our products, including Amazon Brand Analytics (ABA), Product Opportunity Explorer (OX), and Manage Your Growth (MYG). We are looking for a talented and passionate Sr. Research Scientist to lead our research endeavors and develop world-class statistical and machine learning models. The successful candidate will work closely with Product Managers (PM), User Experience (UX) designers, engineering teams, and Seller Growth Consulting teams to provide actionable insights that drive improvements in Seller businesses. Key job responsibilities You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions. About the team The Seller Growth science team aims to provide data and science solutions to drive Seller growth and create better Seller experiences. We structure our science domain with three key themes and two horizontal components. We discover the opportunity space by identifying opportunities with unrealized potential, then generate actionable analytics to identify high value actions (HVAs) that unlock the opportunity space, and finally, empower Sellers with personalized Growth Plans and differentiated treatment that help them realize their potential. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
GB, London
Amazon Advertising is looking for a Senior Applied Scientist to join its brand new initiative that powers Amazon’s contextual advertising product. Advertising at Amazon is a fast-growing multi-billion dollar business that spans across desktop, mobile and connected devices; encompasses ads on Amazon and a vast network of hundreds of thousands of third party publishers; and extends across US, EU and an increasing number of international geographies. We are looking for a dynamic, innovative and accomplished Senior Applied Scientist to work on machine learning and data science initiatives for contextual data processing and classification that power our contextual advertising solutions. Are you excited by the prospect of analyzing terabytes of data and leveraging state-of-the-art data science and machine learning techniques to solve real world problems? Do you like to own business problems/metrics of high ambiguity where yo get to define the path forward for success of a new initiative? As an applied scientist, you will invent ML and Artificial General Intelligence based solutions to power our contextual classification technology. As this is a new initiative, you will get an opportunity to act as a thought leader, work backwards from the customer needs, dive deep into data to understand the issues, conceptualize and build algorithms and collaborate with multiple cross-functional teams. Key job responsibilities * Design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both analysis and business judgment. * Collaborate with software engineering teams to integrate successful experiments into large-scale, highly complex Amazon production systems. * Promote the culture of experimentation and applied science at Amazon. * Demonstrated ability to meet deadlines while managing multiple projects. * Excellent communication and presentation skills working with multiple peer groups and different levels of management * Influence and continuously improve a sustainable team culture that exemplifies Amazon’s leadership principles. About the team The Supply Quality organization has the charter to solve optimization problems for ad-programs in Amazon and ensure high-quality ad-impressions. We develop advanced algorithms and infrastructure systems to optimize performance for our advertisers and publishers. We are focused on solving a wide variety of problems in computational advertising like Contextual data processing and classification, traffic quality prediction (robot and fraud detection), Security forensics and research, Viewability prediction, Brand Safety and experimentation. Our team includes experts in the areas of distributed computing, machine learning, statistics, optimization, text mining, information theory and big data systems. We are open to hiring candidates to work out of one of the following locations: London, GBR
ES, M, Madrid
At Amazon, we are committed to being the Earth’s most customer-centric company. The International Technology group (InTech) owns the enhancement and delivery of Amazon’s cutting-edge 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 Tools and Machine learning (Tamale) team. As part of InTech, Tamale strives to solve complex catalog quality problems using challenging machine learning and data analysis solutions. You will be exposed to cutting edge big data and machine learning technologies, along to all Amazon catalog technology stack, and you'll be part of a key effort to improve our customers experience by tackling and preventing defects in items in Amazon's catalog. We are looking for a passionate, talented, and inventive Scientist with a strong machine learning background to help build industry-leading machine learning 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 with work closely with your business partners to identify opportunities for innovation. You will apply machine learning solutions to automate manual processes, to scale existing systems and to improve catalog data quality, to name just a few. 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 services. You will be part of team of 5 scientists and 13 engineers working on solving data quality issues 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 models, including image to text, LLMs and GenAI. Your work will improve the experience of millions of daily customers using Amazon in Europe and in other regions. 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! This position will be based in Madrid, Spain We are open to hiring candidates to work out of one of the following locations: Madrid, M, ESP
IN, KA, Bangalore
Appstore Quality tech team builds tools, using AI and engineering techniques to provide the best quality apps to Amazon Appstore users. We are a team of highly-motivated, engaged, and responsive professionals who enable the core testing and quality infrastructure of Amazon Appstore. Come join our team and be a part of history as we deliver results for our customers. Appstore Quality team's mission is to automate all types of functional, non functional, and compliance checks on apps submitted by appstore app developers to enable north star vision of publishing apps in under 5 hours. Our team uses various ML/AI/Generative AI techniques to automatically detect violations in images and text metadata submitted by developers. We are working on ambitious project AI projects such as building LLM, auto navigate a mobile app to detect inside app issues and violations. We are seeking an innovative and technically strong data scientist with a background in optimization, machine learning, and statistical modeling/analysis. This role requires a team member to have strong quantitative modeling skills and the ability to apply optimization/statistical/machine learning methods to complex decision-making problems, with data coming from various data sources. The candidate should have strong communication skills, be able to work closely with stakeholders and translate data-driven findings into actionable insights. The successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and ability to work in a fast-paced and ever-changing environment. This role involves working closely with Sr Data Scientist, Principal engineer, and engineering team to build ML and AL based solutions in meeting our north start vision. Key job responsibilities • Implement statistical methods to solve specific business problems utilizing code (Python, Scala, etc.). • Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters. • Collaborate with program management, product management, software developers, data engineering, and business leaders to provide science support, and communicate feedback; develop, test and deploy a wide range of statistical, econometric, and machine learning models. • Build customer-facing reporting tools to provide insights and metrics which track model performance and explain variance. • Communicate verbally and in writing to business customers with various levels of technical knowledge, educating them about our solutions, as well as sharing insights and recommendations. • Earn the trust of your customers by continuing to constantly obsess over their needs and helping them solve their problems by leveraging technology • Excellent prompt engineering skillset with a deep knowledge of LLMs, embeddings, transformer models. • Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers About the team In Appstore, “We entertain, and delight, hundreds of millions of people across devices with a vast selection of relevant apps, games, and services by making it trivially easy for developers to deliver”. Appstore team enables the customer and developer flywheel on devices by enabling developers to seamlessly launch and manage their apps/ in-app content on Amazon. It helps customers discover, buy and engage with these apps on Fire TV, Fire Tablets and mobile devices. The technologies we build on vary from device software, to high scale services, to efficient tools for developers. We are open to hiring candidates to work out of one of the following locations: Bangalore, KA, IND
US, WA, Bellevue
Want to be part of the team whose mission is to expand Alexa to new countries, languages, devices and cultures? The Alexa International team makes it happen. Our customers are very diverse in where they live, the languages they speak to Alexa, the devices they use and the content that matters most. In turn, our problems are diverse and need innovative solutions. We are seeking a Senior Applied Science Manager who will play a key role in the next generation of AI powered Conversational Assistants. Key job responsibilities Lead and manage a team of applied and research scientists responsible for building multilingual experiences Collaborate with cross-functional teams to ensure that Amazon’s AI models are aligned with human preferences. Identify and prioritize research opportunities that have the potential to significantly impact our AI systems. Mentor and guide team members to achieve their career goals and objectives. Communicate research findings and progress to senior leadership and stakeholders. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a highly-skilled Senior Applied Scientist, to lead the development and implementation of cutting-edge algorithms and push the boundaries of efficient inference for Generative Artificial Intelligence (GenAI) models. As a Senior Applied Scientist, you will play a critical role in driving the development of GenAI technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Design and execute experiments to evaluate the performance of different decoding algorithms and models, and iterate quickly to improve results - Develop deep learning models for compression, system optimization, and inference - Collaborate with cross-functional teams of engineers and scientists to identify and solve complex problems in GenAI - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA | New York, NY, USA | Sunnyvale, CA, USA
US, NJ, Newark
Employer: Audible, Inc. Title: Data Scientist II Location: One Washington Park, Newark, NJ, 07102 Duties: Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing RedShift, and S3 / edX storage systems. Build relationships with stakeholders and counterparts, and communicate model outputs, observations, and key performance indicators (KPIs) to the management to develop sustainable and consumable products. Explore and analyze data by inspecting univariate distributions and multivariate interactions, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build production-ready models using statistical modeling, mathematical modeling, econometric modeling, machine learning algorithms, network modeling, social network modeling, natural language processing, or genetic algorithms. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production. Position reports into Newark, NJ office; however, telecommuting from a home office may be allowed. Requirements: Requires a Master’s in Statistics, Computer Science, Data Science, Machine Learning, Applied Math, Operations Research, Economics, or a related field plus two (2) years of experience as a Data Scientist, Data Engineer, or other occupation/position/job title involving research and data analysis. Experience may be gained concurrently and must include one (1) year in each of the following: - Building statistical models and machine learning models using large datasets from multiple resources - Working with Customer, Content, or Product data modeling and extraction - Using database technologies such as SQL or ETL - Applying specialized modelling software including Python, R, SAS, MATLAB, or Stata. Alternatively, will accept a Bachelor's and four (4) years of experience. Multiple positions. Apply online: Job Code: ADBL157. We are open to hiring candidates to work out of one of the following locations: Newark, NJ, USA
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a highly-skilled Senior Applied Scientist, to lead the development and implementation of cutting-edge algorithms and push the boundaries of efficient inference for Generative Artificial Intelligence (GenAI) models. As a Senior Applied Scientist, you will play a critical role in driving the development of GenAI technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Design and execute experiments to evaluate the performance of different decoding algorithms and models, and iterate quickly to improve results - Develop deep learning models for compression, system optimization, and inference - Collaborate with cross-functional teams of engineers and scientists to identify and solve complex problems in GenAI - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA | New York, NY, USA | Sunnyvale, CA, USA