AWS Agentic AI call for proposals - Fall 2026

AWS Agentic AI call for proposals - Fall 2026 is sponsored by Amazon Web Services (AWS). AWS Agentic AI aims to advance agentic AI research by funding the development of open-source tools and research that benefit the AI community at large, or impactful research related to agents.

They seek proposals related to agentic AI in areas such as no-code/low-code solutions, AI-enhanced productivity applications, multi-agent systems, and agentic AI for scientific discovery.

AWS Agentic AI call for proposals - Fall 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 Try Amazon’s frontier foundation models.

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. AWS Agentic AI call for proposals - Fall 2026 Advancing the frontiers of AI agents AWS offers a broad and deep set of tools for businesses to create impactful AI solutions faster.

Our mission is to share our learnings and AI capabilities as fully managed services and put them into the hands of every scientist and developer. AWS Agentic AI aims to advance agentic AI research by funding development of open-source tools and research that benefit the AI community at large, or impactful research related to agents.

We seek proposals related to agentic AI in the areas below: Development and application of no-code / low-code solutions for agentic AI, which facilitate the rapid deployment and management of AI agents at scale AI-enhanced productivity applications, which enable seamless human-AI collaboration and improve automation Multi-agent systems and collaborations, which enable agents to work together securely and effectively Agentic AI for accelerating scientific discovery in fields including but not limited to health & life sciences, chemistry, materials science, and physics Scalable verification of agent trajectories to reduce human-in-the-loop intervention and increase autonomous operation Agent customization, involving post-training the execution model or optimization of prompts, tools, specs, steering files, etc. outside of the model Other topics related to agentic AI development and applications are also welcome, including, but not limited to: Safety and responsible AI for agentic behavior Access methods for data integration and retrieval systems, e.g., vector, lexical, graph, relational, hybrid Enterprise-scale agent operations and governance Application domains, including but not limited to, software engineering, enterprise, healthcare & life sciences, finance, media & entertainment, consumer agentic assistants Submission period: October 1 — November 11, 2026 (11:59PM Pacific Time).

Decision letters will be sent out in February 2027. Selected Principal Investigators (PIs) may receive the following: Unrestricted funds, no more than $70,000 USD on average AWS Promotional Credits, no more than $50,000 USD on average 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. Please refer to the ARA Program rules on the Rules and Eligibility page .

Proposals for this CFP should be prepared according to the proposal template and are encouraged to be a maximum of 4 pages, not including Appendices. In addition, to submit a proposal for this CFP, please also include the following information: Please describe how your research differs from or builds upon existing work in your field. What makes your findings, applications, or conceptual framework innovative or unexpected?

Please list the open-source tools you plan to contribute to. Please list the AWS ML tools you will use. ARA will make the funding decisions based on the potential impact to the research community and the quality of the scientific content.

We encourage research that uses machine learning tools, for example AWS AI/ML services (Amazon SageMaker, Amazon AI services, Amazon Bedrock). Expectations from recipients Previously, this CFP was issued through the AWS Machine Learning Research Awards (MLRA) program. MLRA now funds awards through ARA.

Applied Scientist, Fauna Robotics Robots are about to step out of the movies and into our homes. If you're excited about this robotics revolution, come join Amazon's Fauna Robotics. As an Applied Scientist in this org, you'll push the state-of-the-art techniques in Robotics.

You'll drive technical excellence in areas such as perception, manipulation, sim2real transfer, reinforcement learning and multi-task learning, designing novel algorithms that bridge the gap between research and real-world deployment. In this role, you will integrate hands-on technical expertise with scientific leadership, ensuring your team delivers robust solutions for dynamic real-world environments.

While staying updated with the latest advancements in robotics research, you will leverage your experience and expertise to guide the team towards the most promising direction. Collaborating with a talented group of software engineers and scientists, you will bring your ideas to fruition. Deploying robots in home environments requires you to anticipate challenges not encountered in structured settings like warehouses.

Additionally, consumer-grade sensors and actuators necessitate working around their limitations, requiring out-of-the-box innovative ideas. As such, you are someone who truly enjoys thinking about problems using first principles at a holistic and systems level to address the aforementioned challenges.

Key job responsibilities - Lead technical initiatives in areas such as robotics foundation models, reinforcement learning and manipulation - - Design experiments to identify the limitations of current state-of-the-art models and developing new models or techniques that can surpass these models - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Mentor fellow scientists while maintaining strong individual technical contributions - Collaborate with engineering teams to optimize and scale models for real-world applications - Influence technical decisions and implementation strategies within your area of focus - Experienced in communicating complex technical work to a non-technical audience - The ability to work with minimal guidance, be proactive and to handle ambiguity and the challenge of quickly evolving goals About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life.

Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals.

We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience.

We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build.

If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.

Research Scientist II, Global Risk Intelligence and Prevention We are seeking an exceptional Applied Scientist, Seller Abuse Prevention, to lead the development and implementation of advanced AI solutions that will transform how we prevent bad actors from operating in our store and enable Selling Partners to start and grow their business without fear of disruption, so that customers and Selling Partners across the globe trust us and have confidence in the integrity of Amazon's store.

This role will focus on building risk detection models leveraging state-of-the-art AI, including small language models, to detect and prevent abuse of Amazon's catalog worldwide. You will design, develop, and deploy scalable AI solutions to proactively detect and prevent marketplace abuse throughout the seller lifecycle.

You will work with massive-scale, multi-modal datasets spanning behavioral patterns, transactional histories, and behavioral data to build detection systems that are ahead of evolving adversarial tactics.

Key job responsibilities - Design and build predictive risk detection models using advanced AI techniques, including Natural Language Processing including LLMs and agents to proactively identify bad actors and prevent marketplace abuse at scale - Own the end-to-end scientific solution from risk quantification through decision optimization, determining the appropriate actions to take across varying risk levels - Develop interpretability and reasoning pipelines that provide transparent, actionable explanations for model decisions to support enforcement and seller experience - Work with risk programs across the seller lifecycle to define detection strategies, translate operational investigation patterns into automated systems, and prioritize high-impact risk areas - Partner with engineering teams to deploy models into production, define evaluation frameworks, and collaborate with operations and verification teams to measure and improve detection effectiveness A day in the life Day-to-day you can expect to: - Explore datasets to understand predictors and patterns of abuse - Work with product, program, and engineering stakeholders to build solutions into production that will last - Identify new and emerging abuse vectors as abusers get more sophisticated - Use search, graph, computer vision, NLP, and anomaly detection methodologies to automatically detect abusive actions.

About the team Our team is driven by the belief that scientific innovation unlocks transformative customer experiences. We tackle hard, ambiguous problems by combining rigorous research methods with practical engineering, and we value collaboration across disciplines.

We are building an inclusive environment where scientists at every stage of their career can grow, publish impactful work, and see their research come to life at Amazon scale. If you want to do science that matters, this is the place.

Applied Scientist II, Sponsored Products and Brands The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon. com and beyond.

We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights.

We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising.

As an Applied Scientist within the Brand Intelligence team, you will be responsible for improving the quality of underlying agentic solutions and their impact within the ads delivery stack. We are looking for a passionate candidate with technical expertise in agentic systems, continual learning, evaluation, information retrieval, Natural Language Processing (NLP), and Large Language Models (LLM).

In addition to having hands-on experience in building ML-based solutions, an ideal candidate should be able to create and articulate a customer-centric science vision, show willingness to continuously learn about new scientific approaches, and enjoy operating in startup-like environment.

Principal Applied Scientist, Fauna Fauna Robotics is building capable, safe, and delightful robots for everyday life, and understanding where it is, and what's around it, is fundamental to everything a robot does.

A robot that lives alongside people must build and maintain a map of its environment, plan paths through cluttered and changing spaces, and do so reliably whether it's navigating a hallway for the hundredth time or entering a room it has never seen. We are hiring a Principal Applied Scientist to serve as a technical authority on how our robots understand and move through the world.

You will design, prototype, and ship the core algorithms that give the robot spatial intelligence - from real-time SLAM and scene understanding to motion planning and autonomous navigation.

You will set the mapping and navigation architecture that other engineers build on, shape sensor and compute decisions across robot generations, mentor the engineers and scientists working on spatial perception, and be the person teams come to when the robot is lost.

Key job responsibilities - Set the long-range science roadmap and technical architecture for the robot's mapping, localization, and navigation stack, and serve as Fauna's primary technical authority on spatial intelligence - Design and implement real-time visual-inertial and/or lidar-based SLAM systems that build, update, and maintain persistent maps of home and indoor environments - Develop robust localization algorithms that allow the robot to determine its precise pose in previously mapped spaces, handling environmental changes such as moved furniture, lighting variation, and dynamic occupants - Build navigation and motion planning systems that produce safe, efficient, and natural-looking trajectories through cluttered, dynamic human spaces - Architect semantic mapping capabilities that go beyond geometry — enabling the robot to understand rooms, objects, surfaces, and traversable space at a level that supports higher-level task planning - Drive sensor selection and placement decisions for depth cameras, lidar, IMUs, and encoders in collaboration with mechanical, electrical, and industrial design teams, backed by your own benchmarking and analysis - Design the robot-specific data collection, benchmarking, and evaluation methods to validate mapping accuracy, localization robustness, and navigation safety across diverse real-world environments - Develop strategies for graceful degradation and recovery — ensuring the robot behaves safely when localization is uncertain, maps are incomplete, or sensors are occluded - Present mapping and navigation science and its tradeoffs to senior leadership and partner teams - Mentor scientists and engineers, raising the scientific bar for spatial perception across the organization through design reviews, code reviews, and hiring About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life.

Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won't arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals.

We're changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience.

We're building robots that feel responsive, expressive, and genuinely useful. At Fauna, you'll work at the frontier of this space, helping define how robots move, navigate, and interact with people in natural environments. It's an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build.

If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we're interested in hearing from you. Amazon is looking for an Applied Scientist with a strong deep learning background to help build the training environments that teach large language models to reason, use tools, and complete complex, multi-step tasks.

Key job responsibilities You will design, build, and evaluate reinforcement learning environments (RL gyms) for LLM training: realistic, verifiable task environments where models learn through interaction and feedback.

This includes defining tasks and reward signals, building the infrastructure to run environments at scale, measuring how well environments transfer into model capability, and developing rigorous methods to verify environment quality and correctness. You will work closely with model training teams to understand where environments are most valuable and iterate based on what the models learn.

Your work will directly shape products and services that use generative AI. About the team The Foundational AI (FAI) team has a mission to push the envelope in GenAI with LLMs, in order to provide the best-possible experience for our customers. AI Engineer - RL Manipulation, RIVR Are you passionate about pushing the boundaries of what robots can do?

Amazon is looking for a creative and driven Applied Scientist to help build intelligent robotic manipulation systems powered by reinforcement learning. In this role, you will research, design, and deploy machine learning solutions that enable robots to interact with the physical world in increasingly capable ways.

You will tackle complex, ambiguous problems where textbook answers don't exist, working backwards from real-world needs to invent new approaches. Your work will directly influence how automation systems learn, adapt, and perform at Amazon's scale — and you will see your research come to life on real hardware.

Key job responsibilities - Research and develop reinforcement learning methods for robotic manipulation, advancing the state of the art in sim-to-real transfer, contact-rich control, and adaptive grasping strategies. - Design, implement, and evaluate machine learning models and algorithms, leveraging deep learning frameworks such as PyTorch or TensorFlow to train policies that generalize across varied manipulation tasks.

- Build and maintain production-quality code that integrates scientific prototypes into larger robotics systems, making thoughtful tradeoffs between complexity, performance, and maintainability. - Collaborate with cross-functional engineering and science teams to define simulation requirements, validate model performance, and iterate on solutions informed by real-world hardware feedback.

- Contribute to the team's scientific roadmap by identifying new research directions, mentoring fellow scientists, and publishing results at peer-reviewed venues when appropriate. A day in the life You might start your morning reviewing overnight training runs, analyzing reward curves and failure modes from your latest reinforcement learning experiments.

After a quick sync with robotics engineers, you shift to refining a simulation environment to close a gap you spotted between sim and real performance. In the afternoon, you pair with a teammate to design a new evaluation framework, then wrap up by drafting a short write-up of your findings to share with the broader team.

About the team Our team sits at the intersection of machine learning research and real-world robotics, focused on teaching robots to manipulate objects with precision and adaptability. We combine reinforcement learning, physics-based simulation, and hands-on hardware validation to deliver solutions that work beyond the lab.

We value intellectual curiosity, collaborative problem-solving, and a shared commitment to turning ambitious research ideas into tangible impact. If you are excited about building intelligent systems that learn from experience and operate at scale, this is the team for you.

Economist, Entertainment Economics The charter of the Entertainment Economics team includes working on high-impact projects that require economics expertise and a cross-business perspective. Our team supports Amazon’s diverse entertainment businesses – especially Prime Video, Amazon Music, Amazon MGM Studios, and Gaming (Luna) – through complex causal measurements (observational and experimental) and structural modeling.

Some of our team’s areas of science expertise include marketing science, long-term value measurement, and experimentation. As a central economics team not embedded in any particular business, we strive to provide objective analyses that take a customer-obsessed, pan-Amazon perspective.

This role partners with the Prime Science team to evaluate and improve the way Amazon allocates Prime subscription revenue across its benefit teams, including entertainment businesses. Prime is Amazon's premiere membership program, with over 200mm members world-wide, bringing in billions in Prime subscription revenue annually. Prime is at the center of many major company decisions that impact Amazon's customers.

These decisions span a variety of industries, each reflecting the diversity of Prime benefits: fast and free shipping, entertainment & digital content, reading, healthcare, and grocery. Each of these Prime benefits is supported by a different organization, and each relies on Prime subscription revenue to make critical investment decisions.

Our team partners with Prime Science to ensure that these allocations are credible, actionable, and incentive compatible for every benefit. To support this work, this role will create science models, validate them, and productionize them.

In addition to stellar economics expertise, this role requires systems thinking (anticipating how complex interconnected models will respond to changes in data or modeling), strategic thinking (supporting decisions that affect 200 million+ customers and set Amazon’s direction for years to come), and scientifically novel thinking (using AI and the latest science advancements to achieve move).

The ideal candidate is expert in causal inference with experience working in large, complex structural modeling environments. They are a creative problem-solver, able to identify new approaches that solve multiple problems at once while minimizing churn. They think big, not limiting themselves to what’s been done before, while tactfully accounting for the costs of major changes.

They can add structure to incredibly ambiguous business contexts, making defensible assumptions and delivering measurements that drive impactful business decisions. They have effective communication and the skill to translate data-driven findings into actionable insights for senior leaders. Finally, they are empathetic and constructive.

Our healthy team culture is supportive and fast-paced, and we prioritize learning, growth, and helping each other to continuously raise the bar. Impact and Career Growth In today’s entertainment landscape, critical decisions are made with data and economic models. You’ll help leaders ask the right questions, and then deliver data-driven answers, helping to create the future of Amazon.

You’ll help define a long-term science vision in this space and translate it into an actionable roadmap. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding – a perfect recipe for career growth as an economist in tech.

Key job responsibilities • Design and build econometric models, especially causal models, to measure the value of the business and its many features • Develop science products from concept to prototype to production, incorporating feedback from scientists and business partners • Independently identify and pursue new opportunities to leverage economic insights across entertainment and Prime • Write business and technical documents communicating business context, methods, and results to business leadership and other scientists • Serve as a technical reviewer for our team and related teams, including document and code reviews Applied Scientist, AWS Science of Security, AWS Provable Security Application deadline: Nov 8, 2026 AWS Science of Security is dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for security, privacy, and sovereignty.

The team sits within Amazon Security and operates in a fast-growing, startup-like environment within AWS. You will participate in the design, development, evaluation, and deployment of automated reasoning solutions for verifying the correctness, security, and reliability of software systems.

You’ll be applying techniques such as constraint solving, model checking, static analysis, theorem proving, and program analysis to unique applications in business and engineering. How you build and deploy automated reasoning tools and verification frameworks, plus research and implement novel approaches, will add distinct value to the business and our customers.

This is work on open, hard problems: the kind where the tools you build get used to secure systems that millions of customers depend on. You will dive deep alongside our world-renowned automated reasoning scientist community, bring fresh ideas from your research experience, and see your work ship rather than sit on a shelf.

This is an exceptional opportunity to join a fast moving segment of AWS and help shape the technology, product and business, with notable customer and business impact.

Key job responsibilities - Solve significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation - Own the design, implementation, and delivery of solutions that have a long-term quantifiable impact - Provide cross-organizational technical influence, driving effective outcomes by sharing your deep knowledge and experience - Develop fundamentally new solutions for uniquely challenging business problems A day in the life This is a unique opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business.

You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a meaningful business and customer impact. About the team Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage you 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 exploring what's next. Principal Applied Scientist, Items and Relationships Platform Interested in setting the scientific vision for how every product at Amazon is understood, identified, and connected, with the opportunity to impact every Amazon customer and every selling partner?

The Items and Relationships Platform is the foundation that establishes what every product in Amazon’s catalog is and how products relate to one another. We are rebuilding the platform to establish identity and relationships from meaning and context, using multimodal large language models and generative AI.

This is a foundational problem: understanding products deeply enough to identify them, relate them, and keep the catalog trustworthy as it scales to the full breadth of Amazon’s global contribution traffic. A Principal Applied Scientist on the Items and Relationships Platform owns the science direction for product identity and product relationships.

It spans how contributions are resolved into a committed product identity, how products are connected into structural relationships (variations, multipacks, newer versions, lines of products, bundles), how confident those decisions are, and how their quality is measured.

You will own the strategy for multimodal understanding, representation learning, relationship inference, calibration, and evaluation, so that science capability compounds across identity and relationships rather than being built one use case at a time. This role lives at the intersection of research and production.

Product identity and relationships are fundamentally an entity resolution problem: deciding what is the same product, and how different products relate, across diverse sources, languages, and formats. You will make the hard modeling calls on where sameness ends and relatedness begins, how models express and calibrate confidence, and how the platform measures quality when there is no external ground truth.

Successful candidates are passionate about translating ambiguous business goals into a coherent science roadmap that enables many businesses at once. You are comfortable being the final scientific arbiter on hard cross-team decisions, and working alongside top-notch applied scientists and software engineers to turn advanced research into production systems.

Key job responsibilities As a Principal Applied Scientist on the Items and Relationships Platform, you will: - Own the science direction for product identity: define how products are semantically understood, resolved, and committed as coherent identities, navigating the precision/recall and friction/selection trade-offs inherent in every decision - Build representations that decide both sameness and typed relatedness, and drive relationship inference from semantic identity rather than one pipeline per relationship type - Own structural relationship science across variations, multipacks and base products, newer versions, lines of products, and bundles, and discover new relationship types from the gap between defined policy and what identity reveals - Own calibration and evaluation science: define how models emit calibrated confidence, and build the LLM judge methodology and Catalog Identity Quality metric that measure identity and relationship quality at catalog scale - Partner with Principal Engineers on cost-aware modeling (cascades, distillation, routing) and close the loop from production signals and corrections back into training - Set science direction partnering with ACCS Applied Science teams, mentoring senior and principal-track scientists, and driving the documentation and publication of scientific innovation A day in the life On a typical day, you move between setting science direction and going deep on hard problems: reviewing a multimodal identity model or a relationship inference experiment with scientists, deciding how a borderline case (a multipack, a newer version, a near-identical variant) should be modeled, and pressure-testing whether a quality gain is real by examining the judge and its calibration.

You also spend time zoomed out, shaping the longer-term science roadmap, aligning with Principal Engineers and partner teams on the identity and relationship signals they consume, and mentoring scientists as they take on the ambiguous, high-impact problems that define the platform’s future.

About the team The Items and Relationships Platform advances state-of-the-art GenAI to deeply understand, uniquely identify, and meaningfully connect every product at Amazon scale.

We build the canonical product identity, matching, and relationship systems that power Amazon’s universal catalog: eliminating duplicates, preventing abuse, keeping product families coherent, and integrating the world’s product data to power agentic shopping and trusted discovery for customers worldwide.

We solve foundational challenges including schema-agnostic multimodal understanding, relationship inference, evaluation without ground truth, and efficient inference at reduced cost. Applied Scientist, Mobile Manipulation Robotics (I/O) Amazon Robotics is transforming warehouse automation through edge AI and machine learning applied to real-world robotics challenges.

We're seeking a Applied Scientist to advance our mobile manipulation capabilities by developing learning-based approaches that enable robots to navigate and manipulate objects in dynamic fulfillment environments. This role offers the opportunity to apply state-of-the-art research to production systems operating at Amazon's unprecedented scale.

Key job responsibilities - Model Development and Training: Designing and implementing the model architectures, training and fine tuning the models using various datasets, and optimize the model performance through iterative experiments - Data Management: Process and prepare training data, including data governance, provenance tracking, data quality checks and creating reusable data pipelines.

- Experimentation and Validation: Design and execute experiments to test model capabilities on the simulator and on the embodiment, validate performance across different scenarios, create a baseline and iteratively improve model performance.

- Code Development: Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Research: Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions.

- Collaboration: Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children.

Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision