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Foundation Model Development call for proposals — Winter 2024

Advancing the frontiers of foundation model development.

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About this CFP

AWS offers a broad and deep set of services for organizations to create meaningful machine learning solutions faster. Our mission is to share our learnings and ML capabilities as fully managed services, and put them into the hands of every scientist and developer.

AWS is soliciting funding proposals related to foundation model (FM) development, including enhancements for dialogue, factual questioning and answering, text generation, document summarization, image generation, and more. Proposals should be expanding the state of the art in terms of training methods, with a focus on one of the following areas: 1) to reduce incorrect or nonsensical answers, 2) to reduce the sensitivity to tweaks to the input prompt, 3) ask clarifying questions when facing ambiguous questions, 4) develop efficiency enhancements across the training and hosting FM lifecycle.

We are inviting work that both critiques and enhances the state-of-the-art in FM, including but not limited to the following topics:

  • Reinforcement learning with human feedback
  • Scaling laws and their inverse, including for model fine-tuning
  • Novel datasets and training methods
  • Distillation with enhanced reasoning
  • Foundation models in novel modalities and domains, such as biology, manufacturing, fashion, etc.
  • Bias detection and mitigation throughout the foundation model lifecycle

AWS aims to advance foundation model development by funding the creation of open-source artifacts, datasets and code that benefit the research community at large, or impactful research that uses machine learning tools. AWS will provide credits for use on EC2 instances powered by AWS Trainium chips to help researchers develop and test solutions at scale. In addition to AWS Trainium, proposed research can be conducted using AWS AI Services, AWS ML Services (Amazon SageMaker, Amazon SageMaker Ground Truth, Amazon SageMaker Neo, and Amazon Augmented AI), and Amazon Bedrock.

Timeline

  • Submission period: January 17 - March 6, 2024 (11:59PM Pacific Time)
  • Decision letters will be sent out in October 2024

Award details

Selected Principal Investigators (PIs) may receive the following:

  • $250,000 in AWS Promotional Credits, which can be used on generally available EC2 instances that are powered by AWS Trainium chips
  • AWS Trainium training resources, including AWS tutorials and hands-on sessions with Amazon scientists and engineers

Awards are structured as one-year unrestricted gifts. The budget should include a list of expected costs specified in USD, and should not include administrative overhead costs.

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.

Eligibility requirements

Please refer to the ARA Program rules on the Rules and Eligibility page.

Proposal requirements

PIs are encouraged to exemplify how their proposed techniques or research studies expand the body of knowledge on pretraining and evaluation methods. 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 5 pages, not including Appendices.

Selection criteria

Funding decisions are based on the creativity and quality of the scientific content, and potential impact to the research community and society at large. AWS may solicit feedback on proposal title and abstracts from select AWS customers, but final award decisions will be made solely by AWS. Proposals will be evaluated based on their express interest in open-sourcing their model artifacts, datasets, and development frameworks. Although usage of Trainium is not a requirement, proposals will be evaluated on their intention to use and explore novel hardware for AI/ML, such as Trainium.

Expectations from recipients

To the extent deemed reasonable, Award recipients should acknowledge the support from ARA. Award recipients will inform ARA of publications, presentations, code and data releases, blogs/social media posts, and other speaking engagements referencing the results of the supported research or the Award. Award recipients are expected to engage with AWS throughout the duration of their award year including, but not limited to, providing reports on the status of their research or updates and feedback to ARA via surveys. Award recipients will have the opportunity to work with ARA on an informational statement about the awarded project that may be used to generate visibility for their institutions and ARA.

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Senior Applied Scientist, Special Projects
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the limits. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. As a Senior Applied Scientist on our team, you will focus on building state-of-the-art ML models for healthcare. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. 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 this product area, 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 healthcare outcomes. Key job responsibilities In this role, you will: • Design and implement novel AI/ML solutions for complex healthcare challenges • Drive advancements in machine learning and data science • Balance theoretical knowledge with practical implementation • Work closely with customers and partners to understand their requirements • 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 • Establish best practices for ML experimentation, evaluation, development and deployment • Partner with leadership to define roadmap and strategic initiatives You’ll need a strong background in AI/ML, proven leadership skills, and the ability to translate complex concepts into actionable plans. You’ll also need to effectively translate research findings into practical solutions. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the Special Projects organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. About the team We represent Amazon's ambitious vision to solve the world's most pressing challenges. We are exploring new approaches to enhance research practices in the healthcare space, leveraging Amazon's scale and technological expertise. We operate with the agility of a startup while backed by Amazon's resources and operational excellence. We're looking for builders who are excited about working on ambitious, undefined problems and are comfortable with ambiguity.
Applied Scientist, Prime Video - Generative AI
US, NY, New York
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video subscriptions such as Apple TV+, HBO Max, Peacock, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities • Build generative AI models that create production-ready content, including movie content, localized assets, and visual marketing materials used across Prime Video's global platform. • Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, complexity. • Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models. • Run experiments, gather data, and perform statistical analysis. • Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. • Research new and innovative machine learning approaches. • Share knowledge and research outcomes via internal and external conferences and journal publications A day in the life Prime Video is pioneering the use of Generative AI to empower the next generation of creatives. Our mission is to make world-class media creation accessible, scalable, and efficient. We are seeking an Applied Scientist to advance the state of the art in Generative AI and to deliver these innovations as production-ready systems at Amazon scale. Your work will give creators unprecedented freedom and control while driving new efficiencies across Prime Video’s global content and marketing pipelines. This is a newly formed team within Prime Video Science!
Applied Scientist II, IESP Digital Payments
BR, SP, Sao Paulo
Do you like working on projects that are highly visible and are tied closely to Amazon’s growth? Are you seeking an environment where you can drive innovation leveraging the scalability and innovation with Amazon's AWS cloud services? The Amazon IESP Digital Payments & Emerging Team is hiring Applied Scientists to work in our Machine Learning team in São Paulo, Brazil. The Payments team designs and builds scalable payment systems that power Amazon's checkout and financial services across emerging countries (Brazil, Mexico, Australia, Singapore, and more). We tackle complex challenges in payment orchestration, fraud detection, and regional payment method integration to drive conversion and deliver seamless experiences at scale. Key job responsibilities About you You want to make changes that help millions of customers. You don’t want to make something 10% better as a part of an enormous team. Rather, you want to innovate with a small community of passionate peers. You have experience in analytics, machine learning, LLMs and Agentic AI, and a desire to learn more about these subjects. You want a trusted role in strategy and product design. You put the customer first in your thinking. You have great problem solving skills. You research the latest data technologies and use them to help you innovate and keep costs low. You have great judgment and communication skills, and a history of delivering results. Your Responsibilities - Define and own complex machine learning solutions in the consumer space, including targeting, measurement, creative optimization, and multivariate testing. - Design, implement, and evolve Agentic AI systems that can autonomously perceive their environment, reason about context, and take actions across business workflows—while ensuring human-in-the-loop oversight for high-stakes decisions. - Influence the broader team's approach to integrating machine learning into business workflows. - Advise leadership, both tech and non-tech. - Support technical trade-offs between short-term needs and long-term goals.
Sr. Applied Scientist, Prime Video - Generative AI (Video)
US, CA, Sunnyvale
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video subscriptions such as Apple TV+, HBO Max, Peacock, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities • Build generative AI models that create production-ready content, including movie content, localized assets, and visual marketing materials used across Prime Video's global platform. • Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, complexity. • Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models. • Run experiments, gather data, and perform statistical analysis. • Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. • Research new and innovative machine learning approaches. • Share knowledge and research outcomes via internal and external conferences and journal publications About the team Prime Video is pioneering the use of Generative AI to empower the next generation of creatives. Our mission is to make world-class media creation accessible, scalable, and efficient. We are seeking an Applied Scientist to advance the state of the art in Generative AI and to deliver these innovations as production-ready systems at Amazon scale. Your work will give creators unprecedented freedom and control while driving new efficiencies across Prime Video’s global content and marketing pipelines. This is a newly formed team within Prime Video Science!
Senior Data Scientist, Amazon Leo
US, WA, Bellevue
Amazon Leo is Amazon’s low Earth orbit satellite broadband network. Its mission is to deliver fast, reliable internet to customers and communities around the world, and we’ve designed the system with the capacity, flexibility, and performance to serve a wide range of customers, from individual households to schools, hospitals, businesses, government agencies, and other organizations operating in locations without reliable connectivity. 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. As a Senior Data Scientist (DS), you will drive the development and implementation of advanced analytics and machine learning solutions. You will work on critical initiatives including natural language processing for non-conformance analysis, statistical process controls (SPC) for test optimization, and equipment predictive maintenance models to enable manufacturing rate acceleration. Your work will directly influence Kuiper’s production manufacturing workflow. You are an analytical problem solver who enjoys diving into data from various businesses, is excited about investigations and algorithms, can multi-task, and can credibly interface between scientists, engineers, and business stakeholders. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future. Key job responsibilities Lead the design and implementation of ML/LLM solutions to analyze manufacturing data and identify failure patterns and operational risks Design predictive models for statistical process control and equipment maintenance optimization Build production-ready ML pipelines leveraging AWS services (e.g., SageMaker, Bedrock, AWS Glue) Formalize assumptions about how models are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them Develop and test model enhancements, running computational experiments, and fine-tuning model parameters for new models Collaborate with engineering teams to translate complex manufacturing challenges into data-driven solutions Drive consensus on metrics and analysis approaches to support business strategy Write documents and create compelling visualizations and presentations to communicate insights to stakeholders Mentor team members and drive data science best practices across the organization About the team Established in 2023 as ProdOps was preparing to transition from development into full-rate production, OCC was purpose-built to ensure that as manufacturing scaled, the most critical problems would be identified, solved, and prevented from recurring through a single integrated operating system. OCC focuses on the most critical problems for ProdOps at each phase of production. The team operates as a closed-loop flywheel where each rotation identifies value, solves problems, implements change, and generates new data that feeds the next cycle. Every rotation compounds returns.
Applied Scientist - Reinforcement learning, OMHS SCS
US, MA, N.reading
As an Applied Scientist on the Science SW team, you will collaborate closely with other scientists and engineers to bring Reinforcement Learning (RL) research to production. This role combines the scientific application of ML, and specifically RL and sequential decision making, with software development engineering and a strong product focus. It will be your job to design, implement, and deploy novel RL agents, reward models, and control policies in both prototype and production environments, and to prove their impact in high-fidelity simulation before scaling them across the fleet. Key job responsibilities • Own the research and development of reinforcement learning and sequential decision making solutions spanning deep RL, policy optimization, offline/batch RL, contextual bandits, and multi-agent RL for real-time MHE control and building-wide optimization in a production environment. • Formulate fulfillment operations problems (throughput optimization, flow, merge, and congestion control) as sequential decision-making problems, and design multi-objective reward functions that balance competing operational objectives. • Build and leverage high-fidelity simulation environments for safe offline training, policy validation, and sim-to-real transfer before fleet-scale deployment. • Collaborate across multiple science and engineering teams to integrate RL policies into real-time production and control systems. About the team Amazon is building next generation software, hardware, and processes that will run our global network of fulfillment centers that move millions of units of inventory, and ensure customers get what they want when promised. The Science Software team in the One MHS organization unlocks Material Handling Equipment (MHE) innovation through a multiplicity of disciplines within Artificial Intelligence (AI) and applied science, including Computer Vision (CV), Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned experimentation, the team is dedicated to building self-optimizing fulfillment centers, developing the models that drive real-time, building-wide orchestration of MHE. We conduct experiments, develop models, and apply machine learning (ML) at scale to optimize throughput, flow, merge, and congestion control, and to improve operational performance across the fulfillment network.
Applied Scientist, Fauna
US, NY, New York
We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers
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