AI for Information Security, Agentic AI, Amazon 2030, Amazon Security, Build on Trainium: Accelerating Post-Training, Build on Trainium: Kernels for ML Acceleration, and Robotics is sponsored by Amazon Web Services (AWS). This call for proposals covers seven research areas, including AI for Information Security and Agentic AI, which are relevant to machine learning research.

Build on Trainium: Kernels for ML Acceleration call for proposals — Spring 2026 - Amazon Science Information and knowledge management Operations research and optimization Security, privacy, and abuse prevention Our scientific contributions Research from our scientists and collaborators. Our experts present and discuss cutting-edge research at scientific meetings globally.

Information and knowledge management Operations research and optimization Security, privacy, and abuse prevention Our scientific contributions Research from our scientists and collaborators. Our experts present and discuss cutting-edge research at scientific meetings globally. The latest from Amazon researchers Technical deep-dives and perspectives from our scientists.

Research milestones and recent achievements. The latest from Amazon researchers Technical deep-dives and perspectives from our scientists. Research milestones and recent achievements.

Carnegie Mellon University Tennessee State University University of California, Los Angeles University of Illinois Urbana-Champaign University of Southern California University of Texas at Austin Carnegie Mellon University Tennessee State University University of California, Los Angeles University of Illinois Urbana-Champaign University of Southern California University of Texas at Austin Meet the team building useful AI agents.

Try Amazon’s frontier foundation models. Meet the team building useful AI agents. Try Amazon’s frontier foundation models.

Faculty research opportunities on industry-scale technical challenges. Postdoctoral Science Program Early-career research opportunities alongside experienced industry scientists. Faculty research opportunities on industry-scale technical challenges.

Postdoctoral Science Program Early-career research opportunities alongside experienced industry scientists. Build on Trainium: Kernels for ML Acceleration call for proposals — Spring 2026 Building the future of AI with AWS Trainium By Amazon Research Awards team What is Build on Trainium?

Build on Trainium is a $110MM credit program focused on AI research and university education to support the next generation of innovation and development on AWS Trainium . AWS Trainium chips are purpose-built for high-performance deep learning (DL) training of generative AI models, including large language models (LLMs) and latent diffusion models.

Build on Trainium provides compute credits to novel AI research on Trainium, investing in leading academic teams to build innovations in critical areas including new model architectures, ML libraries, optimizations, large-scale distributed systems, and more.

This multi-year initiative lays the foundation for the future of AI by inspiring the academic community to utilize, invest in, and contribute to the open-source community around Trainium. Combining these benefits with Neuron software development kit (SDK) and recent launch of the Neuron Kernel Interface (NKI) , AI researchers can innovate at scale in the cloud. What are AWS Trainium and Neuron?

AWS Trainium is an AI chip developed by AWS for accelerating building and deploying machine learning models. Built on a specialized architecture designed for deep learning, Trainium accelerates the training and inference of complex models with high output and scalability, making it ideal for academic researchers looking to optimize performance and costs.

This architecture also emphasizes sustainability through energy-efficient design, reducing environmental impact. Amazon has established a dedicated Trainium research cluster featuring up to 40,000 Trainium chips, accessible via Amazon EC2 Trn1 instances. These instances are connected through a non-blocking, petabit-scale network using Amazon EC2 UltraClusters, enabling seamless high-performance ML training.

The Trn1 instance family is optimized to deliver substantial compute power for cutting-edge AI research and development. This unique offering not only enhances the efficiency and affordability of model training but also presents academic researchers with opportunities to publish new papers on underrepresented compute architectures, thus advancing the field.

Focus on Kernels for ML Acceleration GenAI for Kernel Development : As kernel development becomes increasingly complex and specialized for accelerators like Trainium, generative AI offers opportunities to automate and optimize the kernel development lifecycle.

We seek proposals that leverage GenAI to accelerate kernel creation, optimization, and maintenance on Trainium, including: Automated Kernel Generation : Methods for using GenAI to generate high-performance NKI kernels from high-level specifications, including natural language descriptions, mathematical formulations, or reference implementations in other frameworks.

This includes agentic workflows leveraging reinforcement learning, inference-time compute scaling, and multi-agent systems with iterative refinement where agents execute kernels, observe performance metrics, and progressively improve implementations through feedback loops.

Kernel Optimization and Tuning : Techniques for using GenAI to automatically optimize existing kernels, including instruction scheduling, memory access patterns, tile size selection, and register allocation strategies.

This encompasses knowledge distillation and memory systems that capture, distill, and organize optimization insights into structured knowledge bases of architecture-specific heuristics, enabling continuous learning from both successful and failed optimization attempts.

Performance Debugging and Analysis : AI-assisted tools for identifying performance bottlenecks, suggesting optimizations, and explaining performance characteristics of NKI kernels.

This includes methods for correctness verification and robustness testing that rigorously verify functional correctness beyond standard test cases, detecting subtle bugs and reward hacking behaviors where kernels achieve favorable metrics while producing incorrect outputs.

Code Completion and Synthesis : Methods for intelligent code completion, pattern recognition, and synthesis of common kernel idioms specific to NKI and Trainium architecture.

This includes transfer learning and domain adaptation techniques for adapting kernel generation across different hardware generations or compiler versions with minimal training data, as well as explain ability methods that make AI-generated kernels interpretable and maintainable through documentation generation and collaborative human-AI development workflows.

Benchmark Construction and Evaluation : Development of comprehensive, representative benchmark suites for evaluating kernel generation and optimization techniques on Trainium, including systematic methodologies for creating diverse kernel collections spanning operator types, tensor shapes, data layouts, and compute/memory-bound characteristics representative of real-world model workloads.

Developer Tools and Profiling: Effective kernel development requires sophisticated tooling for understanding performance, debugging behavior, and iterating designs.

We seek proposals that advance the NKI developer experience on Trainium, including: Novel Profiling Visualizations and Human-Computer Interaction : Innovative visualization techniques that blend performance analysis with HCI research to make complex kernel behavior intuitive and actionable, including interactive 3D performance landscapes, temporal execution flow visualizations, comparative visual analytics across kernel variants, attention-driven bottleneck highlighting, and multi-dimensional performance space exploration tools that enable developers to quickly identify optimization opportunities through visual pattern recognition.

Performance Modeling and Estimation : Advanced methods for predicting kernel performance before execution, including analytical roofline models extended for Trainium architecture, learned performance predictors using neural networks trained on kernel characteristics, hybrid symbolic-numeric performance models, static analysis techniques for estimating memory bandwidth and compute utilization, and probabilistic performance bounds that account for hardware variability and dynamic effects.

Debugging and Verification Tools : Methods for validating kernel correctness, detecting numerical issues, and debugging complex kernel behaviors, including symbolic execution and formal verification approaches.

Interactive Development Environments : Enhanced IDE support for NKI development, including syntax highlighting, type checking, inline performance hints derived from real-time estimation models, and integration with existing development workflows. Kernel Porting and Cross-Framework Translation: The ecosystem of kernel languages continues to fragment, creating barriers to adoption and limiting code reuse.

We seek proposals that enable seamless translation between kernel frameworks while preserving high performance on Trainium, including: Automated Kernel Translation : Methods for automatically porting kernels from other frameworks (Triton, CUDA,CuTe, Pallas) to NKI specifically while maintaining or improving performance, including semantic-preserving transformations and architecture-specific optimizations Cross-Framework Optimization : Methods for leveraging optimization techniques across different kernel languages, including pattern matching, optimization transfer learning, and unified intermediate representations.

Performance Portability : Approaches for ensuring translated kernels achieve competitive performance with hand-written implementations, including auto-tuning, architecture-aware code generation, and performance validation frameworks. Kernel Language Design and Abstractions: The design of kernel languages fundamentally shapes developer productivity and achievable performance.

We seek proposals that explore novel language representations, APIs, and abstractions for NKI on Trainium, including: Alternative Language Representations : Novel representations for expressing kernel computations, including tensor comprehensions, polyhedral models, and domain-specific languages that improve expressiveness or enable better optimization.

API Design and Primitives : Improved APIs and primitive operations for kernels including higher-level abstractions that maintain performance while improving usability, composability, and maintainability. Abstraction Layers : Methods for building layered abstractions that allow developers to work at different levels of detail, from high-level operations to low-level hardware control, with smooth transitions between levels.

Submission period: March 25 — May 6, 2026 (11:59 PM Pacific Time) Decision letters will be sent out in August 2026.

Selected Principal Investigators (PIs) may receive the following: Applicants are encouraged to request AWS Promotional Credits in one of two ranges: AWS Promotional Credits, up to $50,000 AWS Promotional Credits, up to $250,000 and beyond AWS Trainium training resources, including AWS tutorials and hands-on sessions with Amazon scientists and engineers Awards are structured as one-time unrestricted gifts.

The budget should include a list of expected costs specified in USD, and should not include administrative overhead costs. The final award amount will be determined by the awards panel. Your receipt and use of AWS Promotional Credits is governed by the AWS Promotional Credit Terms and Conditions , which may be updated by AWS from time to time.

Please refer to the ARA Program rules on the Rules and Eligibility page. PIs are encouraged to exemplify how their proposed techniques or research studies advance kernel optimization, LLM innovation, distributed systems, or developer efficiency. PIs should either include plans for open source contributions or state that they do not plan to make any open source contributions (data or code) under the proposed effort.

Proposals for this CFP should be prepared according to the proposal template and are encouraged to be a maximum of 3 pages, not including Appendices.

Proposals will be evaluated on the following: Creativity and quality of the scientific content Potential impact to the research community and society at large Interest expressed in open-sourcing model artifacts, datasets and development frameworks Intention to use and explore novel hardware for AI/ML, primarily AWS Trainium and Inferentia Expectations from recipients Amazon Research Awards team Amazon Research Awards recipients announced Amazon Research Awards team Awardees, who represent 41 universities in 8 countries, have access to Amazon public datasets, along with AWS AI/ML services and tools.

Amazon Research Awards issues Spring 2026 call for proposals Amazon Research Awards team Submissions open March 25 and close on May 6, 2026 AI-native 6G: From networks to intelligence fabrics Imen Grida Ben Yahya , Ejaz Sial , Kaniz Mahdi “Network language models” will coordinate complex interactions among intelligent components, computational infrastructure, access points, data centers, and more.

Data Scientist, Security Issue Management Are you interested in building Agentic AI solutions that solve complex builder experience challenges with significant global impact?

The Security Tooling team designs and builds high-performance AI systems using LLMs and machine learning that identify builder bottlenecks, automate security workflows, and optimize the software development lifecycle—empowering engineering teams worldwide to ship secure code faster while maintaining the highest security standards.

As a Data Scientist on our Security Tooling team, you will focus on building state-of-the-art ML models to enhance builder experience and productivity. You will identify builder bottlenecks and pain points across the software development lifecycle, design and apply experiments to study developer behavior, and measure the downstream impacts of security tooling on engineering velocity and code quality.

Our team rewards curiosity while maintaining a laser-focus on bringing products to market that empower builders while maintaining security excellence. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment.

At the forefront of both academic and applied research in builder experience and security automation, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams.

This role offers a unique opportunity to work on projects that could fundamentally transform how builders interact with security tools and how organizations balance security requirements with developer productivity.

Key job responsibilities • Design and implement novel AI/ML solutions for complex security challenges and improve builder experience • Balance theoretical knowledge with practical implementation • Navigate ambiguity and create clarity in early-stage product development • Collaborate with cross-functional teams while fostering innovation in a collaborative work environment to deliver impactful solutions • Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results • Establish best practices for ML experimentation, evaluation, development and deployment About the team Diverse Experiences Amazon Security values diverse experiences.

Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security?

At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services.

We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness.

Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional.

Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

Applied Scientist III, Intelligent Talent Acquisition - Lead Generation & Detection Services Do you want to make a real difference to real people's lives? Want to design and build fair and explainable systems which automate recruitment processes across Amazon? Come and be part of a team that develops new machine learning (ML) technologies, which help Amazon scale for its customers by recruiting diverse teams.

Join our Recommendations team within Intelligent Talent Acquisition (ITA) where you’ll build machine learning products that transform how job seekers find opportunities and recruiters discover talent. You’ll develop sophisticated recommendation systems powering both Amazon Jobs and internal hiring platforms, operating at global scale to match the right people with the right positions.

Using techniques including representation learning, reinforcement learning, and probabilistic modeling, your work will directly improve efficiency for recruiters and help candidates find their ideal roles. This position offers the chance to solve complex problems with significant impact by creating systems that make Amazon’s entire hiring ecosystem more effective while collaborating with scientists across the organization.

Key job responsibilities - Design and implement machine learning models that power recommendation systems for job seekers and recruiters, ensuring high performance, scalability, and reliability at global scale. Our ideal candidate has a strong scientific foundation and experience of statistical analysis and model building and has a passion for fairness and explainability in ML systems.

- Collaborate with engineers, scientists, and product managers to define requirements, create solutions, and deliver products that improve the hiring experience. - Participate in the full software development lifecycle including scoping, design, coding, testing, documentation, deployment, and maintenance of recommendation systems and ML models.

- Solve complex ML problems using optimal data structures and algorithms, making thoughtful trade-offs between efficiency and maintainability. - Stay current with scientific literature and develop novel approaches that address business challenges in talent acquisition. You will have the opportunity to provide feedback on scientific work across the organization helping the entire Intelligent Talent Acquisition organization improve.

A day in the life You might spend the morning reviewing a colleague’s code for a new recommendation algorithm feature, then collaborate with product managers to refine requirements for an upcoming enhancement. After lunch, you’ll dive into model development, analyzing performance metrics from recent A/B tests and implementing improvements to the job-seeker recommendation pipeline.

Throughout the day, you’ll participate in scientific discussions with peers across the organization, providing valuable feedback while continuing to refine your expertise. About the team The Recommendations team is a hybrid group of software engineers and applied scientists located in Edinburgh. We build tools that match people to jobs and jobs to people, optimizing experiences for both recruiters and candidates.

Our work directly impacts Amazon’s ability to find and hire exceptional talent globally. The team maintains a collaborative environment with regular knowledge sharing and mentorship opportunities. We work closely with our product teams to understand business needs and develop innovative scientific solutions that improve hiring outcomes across both industry and student requisitions worldwide.

Research Scientist, AMX Research The PXT (People Experience and Technology) AMX Research is seeking a highly skilled and motivated Research Scientist to join our team. You will be leading manager experience research space to support the PXT talent evaluation/talent management initiatives.

If you enjoy innovating, thinking big and want to contribute directly to the success of a growing team, you may be a prime candidate for this position.

Key job responsibilities Design experiments, test hypotheses, and build actionable models Conduct quantitative analyses of talent management data and trends Conduct qualitative data collection and analysis Partner closely and drive effective collaborations across multi-disciplinary research and product teams Consult on appropriate analytic methodologies and scope research requests Amazon is on a mission to redefine the future of automation — and we're looking for exceptional talent to help lead the way.

We are building the next generation of advanced robotic systems that seamlessly blend cutting-edge AI, sophisticated control systems, and novel mechanical design to create adaptable, intelligent automation solutions capable of operating safely alongside humans in dynamic, real-world environments.

At Amazon, we leverage the power of machine learning, artificial intelligence, and advanced robotics to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence — and we're just getting started.

As an Applied Scientist in Robot Perception, you will be at the forefront of this transformation. You will develop and deploy state-of-the-art perception algorithms that enable robots to truly understand and interact with the physical world — bridging the gap between theoretical research and real-world impact.

Bringing deep expertise in Computer Vision and a nuanced understanding of the capabilities and limitations of modern Vision-Language Models (VLMs), you will innovate boldly and push the boundaries of what's possible. Our vision for the Perception layer is ambitious: to enable seamless, intelligent interaction between the user, the robot, and its environment.

This is a rare opportunity to work at the intersection of deep learning, large language models, and robotics — contributing to research that doesn't just advance the field, but reshapes it. You will collaborate with world-class teams pioneering breakthroughs in dexterous manipulation, locomotion, and human-robot interaction, all at an unprecedented scale.

Join us in building intelligent robotic systems that will define the future of automation and human-robot collaboration.

Key job responsibilities - Design, develop, and deploy perception algorithms for robotics systems, including object detection, segmentation, tracking, depth estimation, and scene understanding - Contribute to research initiatives in computer vision, sensor fusion and 3D perception - Collaborate with cross-functional teams including robotics engineers, software engineers, and product managers to define and deliver perception capabilities - Drive end-to-end ownership of ML models — from data collection and labeling strategy to training, evaluation, and deployment - Define and track key metrics to measure perception system performance in real-world environments - Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Contribute to technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team Principal Applied Scientist, Neuro-Symbolic AI Labs We are looking for researchers who aim to build super-intelligent AI systems that leverage proof assistants to guide learning and reasoning.

Our neuro-symbolic AI technology is applied across a wide range of science and engineering domains within Amazon, and you will join the team at the forefront of this research. As a Principal Applied Scientist, you will play a pivotal role in shaping the definition, vision, and development of product features from beginning to end.

You will: - Define and implement new neuro-symbolic applications that employ scalable and efficient approaches to solve complex problems. - Work in an agile, startup-like development environment, where you are always working on the most important stuff. - Deliver high-quality scientific artifacts.

About the team We work closely with academia. Our team includes an Amazon Scholar in mathematics, and we maintain active research collaborations with faculty at leading CS departments (MIT, Berkeley, CMU).

Applied Scientist, AMXL Worldwide Science Do you want to join an innovative team applying machine learning, advanced optimization techniques, and Large Language Models (LLMs) to transform the delivery of heavy and bulky items for Amazon customers? Are you excited about working with large-scale operational data and developing models that solve real-world logistics and fulfillment challenges?

If so, the Amazon Extra Large (AMXL) Science team may be the right fit for you. AMXL is Amazon's specialized business for delivering heavy and bulky items, including appliances, furniture, fitness equipment, and mattresses, with a premium customer experience that includes room-of-choice delivery, at-home installations, and assembly services.

We are seeking an Applied Scientist to help develop scalable machine learning and optimization solutions that improve delivery efficiency, capacity planning, network design, and customer experience across our rapidly growing network.

In this role, you will partner with senior scientists and engineers to translate complex operational problems into data-driven solutions, build and evaluate models, and contribute to next-generation fulfillment and logistics systems.

Key job responsibilities Apply machine learning, statistical techniques, time series modeling, and operations research to build and improve models for delivery routing, capacity planning, demand forecasting, workforce scheduling, and network optimization Analyze large-scale historical and real-time operational data to identify efficiency patterns, bottlenecks, and emerging trends across the AMXL network Develop, validate, and deploy innovative models under the guidance of senior scientists to improve cost-to-serve and customer experience Experiment with emerging technologies, including Generative AI and LLMs, to enhance automation, scheduling, and operational decision-making Collaborate closely with software engineers to implement models in real-time production systems Partner with operations, product, and business teams to translate operational insights into actionable improvements Build scalable, automated pipelines for data analysis, model training, and validation Monitor model performance and provide clear reporting on key operational and business metrics Research and prototype new modeling approaches to improve system performance and delivery quality A day in the life You will be working within a dynamic, diverse, and supportive group of scientists who share your passion for innovation and excellence in logistics and fulfillment science.

You will work closely with business partners, operations teams, and engineering teams to create end-to-end scalable machine learning solutions that address real-world challenges across AMXL's heavy and bulky delivery network, including demand forecasting, capacity planning, routing optimization, and customer experience improvement.

You will build scalable, efficient, and automated processes for large-scale data analyses, model development, model validation, and model implementation in production systems. You will also provide clear and compelling reports on your solutions to both technical and non-technical stakeholders, and contribute to the ongoing innovation and knowledge-sharing that are central to the team's success.

About the team The AMXL (Amazon Extra Large) Worldwide Science team is a multidisciplinary organization of data scientists, applied scientists, and product managers dedicated to solving some of the most complex supply chain and logistics challenges in Amazon's heavy bulky business.

The team's mission is to leverage advanced analytics, machine learning, and optimization science to drive measurable improvements across the AMXL end-to-end supply chain — from inbound fulfillment and middle-mile transportation to last-mile delivery of heavy and bulky items.

The science team transforms complex operational data into actionable intelligence that directly impacts customer experience, cost efficiency, and delivery performance at a worldwide scale.

Quantum Applied Scientist, Processor Test & Measurement, Amazon Center for Quantum Computing The Amazon Web Services (AWS) Center for Quantum Computing in Pasadena, CA, is looking to hire a Quantum Research Scientist in the Processor Test and Measurement group.

You will join a multi-disciplinary team of theoretical and experimental physicists, materials scientists, and hardware and software engineers working at the forefront of quantum computing. You should have a deep and broad knowledge of experimental measurement techniques. Candidates with a track record of original scientific contributions will be preferred.

We are looking for candidates with strong engineering principles, resourcefulness and a bias for action, superior problem solving, and excellent communication skills. Working effectively within a team environment is essential. As a research scientist you will be expected to work on new ideas and stay abreast of the field of experimental quantum computation.

Key job responsibilities We are looking to hire a Research Scientist to develop and test novel calibration and optimization tools for Quantum Error Correction on large scale quantum processors. You will be on a team of engineers and scientists at the frontier of quantum processor control and error correction. You are expected to take part in high-impact research projects that intersect with our engineering roadmap.

We are looking for candidates with strong engineering principles and resourcefulness. Organization and communication skills are essential. A day in the life About the team Why AWS?

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry.

As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS. Within AWS UC, Amazon Dedicated Cloud (ADC) roles engage with AWS customers who require specialized security solutions for their cloud services. Inclusive Team Culture AWS values curiosity and connection.

Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.

Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony.

Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

Export Control Requirement: Due to applicable export control laws and regulations, candidates must be either 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, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility.

Senior Data Scientist, JP Seller Services We are seeking an exceptional Senior Data Scientist to join our JP Seller Services team, where you will play a pivotal role in enabling seller growth and success on Amazon Marketplace through innovative products, technology, and data-driven solutions.

As a key member of JP Seller Services, you will collaborate with cross-functional stakeholders across Amazon to develop sophisticated AI-native science solutions and innovative problem-solving products through advanced analytics, machine learning, statistical modeling and generative AI. These solutions will enable seller business growth on Amazon Marketplace and deliver key strategic decisions impacting our entire business.

The ideal candidate combines strong technical depth with the strategic thinking to address complex business problems at scale. Key job responsibilities (1) Implement AI-driven solutions to streamline and accelerate the science model development and evaluation cycle, enabling faster iteration and impact delivery. (2) Develop science-based solutions to optimize seller engagement channel strategies.

(3) Build and scale end-to-end AI-native recommendation models using generative AI and ML to identify critical seller challenges and unlock business growth opportunities. (4) Collaborate with stakeholders to transform business insights into rigorous scientific solutions. Applied Scientist, Trust CX Innovations&AI Policy Alexa+ is Amazon’s next-generation, AI-powered assistant.

Building on the original Alexa, it uses generative AI to deliver a more conversational, personalized, and effective experience. The Trust CX Innovations team is looking for an Applied Scientist with strong background in Generative AI space to build solutions that help in upholding customer trust for Alexa+.

As an Applied Scientist in Trust CX innovations, you will be at the forefront of developing innovative solutions to critical challenges in AI trust and privacy. You'll lead research in trust-preserving machine learning techniques. We are working on revolutionizing the way Amazonians work and collaborate.

You will help us achieve new heights of productivity through the power of advanced generative AI technologies.

Key job responsibilities - Lead research initiatives in generative AI, focusing on LLMs, multimodal models, and frontier AI capabilities - Develop innovative approaches for model optimization, including prompt engineering, few-shot learning, and efficient fine-tuning - Pioneer new methods for AI safety, alignment, and responsible