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Build on Trainium call for proposals — Spring 2025

Building the future of AI with AWS Trainium.

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About this CFP

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.

Below are key topics Build on Trainium is exploring to drive innovation and enhance the future of AI/ML on AWS. Please develop your proposal addressing one or more of these topics in detail, unless you know better ones.

  1. Novel kernels and compiler extensions for Trainium. With the recent launch of the Neuron Kernel Interface (NKI), we welcome projects that identify improvement areas such as kernels and compilation artifacts. This could be through implementing an algorithm which was not previously supported on Trainium, improving performance on a known bottleneck, or some other solution. Kernels that are already available on NKI are available here.
    1. Mixture of expert training and hosting, particularly kernels which optimize these compute patterns. We also invite projects which explore ambitious test-time compute requirements and identify kernel-based solutions for these.
    2. Kernels to improve model distillation and fine-tuning recipes, such as from a compute perspective. We are inviting researchers to study the compute workloads of model distillation and fine-tuning regimes, including multi-stage. We invite PIs to develop novel kernels which improve these.
    3. Quantization, such as developing high performance kernels that enable and explore the impact of quantization for language models.
    4. Novel projects to generate NKI kernels under various conditions, such as improvements upon existing compilation paths, novel bindings, and more.
  2. Novel algorithms for large language models. As demand increases for integrating language models across services and applications, so too does the need to increase performance while lowering costs. The growing diversity of applications and use cases creates new opportunities for hardware-centric machine learning solutions. Towards that end, we request the development of novel algorithms for large language models, such as:
    1. Improvements on attention, quantization, embedding generation and processing within neural networks.
    2. Extensions on context length and necessary algorithms for these.
    3. Ability to reason. As the complexity of questions sent to AI increases, so too must the ability of language models to address them correctly and efficiently. We invite proposals that study reasoning and show improvement in this area.
    4. Beyond Transformers. We invite projects that explore novel ways of learning sequences beyond the standard matrix multiplication-based Transformer architecture. In particular we invite proposals that show superior results to Transformers at much smaller scales, while containing the promise of better results at larger scales.
    5. Multiple modalities. We invite proposals that hope to improve upon existing algorithms which combine language with other modalities. This includes language jointly trained with vision, robotics, etc. We also invite projects which expand the overall breadth of model support on Trainium.
    6. Model adaptation, fine-tuning, and alignment, such as post-training. We invite proposals that discover novel algorithms to improve this space, with a high focus on GRPO and similar techniques.
  3. Systems improvements for distributed training and hosting. In this section we invite proposals that adopt a systems perspective around distributed training and hosting for large foundation models. This can include topics along the following:
    1. Improvements for distributed systems for mixture of experts (MoE), including perspectives that explore the implications for this from data, network, and topology perspectives.
    2. Improved training efficiency during scale-out training, including checkpoint acceleration and minimizing data movement overhead introduced through this.
    3. Faster and more resource efficient hosting, especially distributed inference. We also invite hosting-aware training regimes that attempt to mitigate the computational challenges of hosting models by early-stage changes in the training methodology
    4. Performance analysis tooling and fault handling, including the development of novel performance methodologies. We welcome projects that aim to improve KPIs for foundation models at scale, such as MFU, HBMu, TTFT, TPS, etc.
  4. Streamline development. In this section, we invite the study and development of tools that accelerate the adoption of AI through a simplified developer experience. We invite proposals around the following:
    1. Automation to reduce the search space time in finding optimal compute architectures and model parameters.
    2. Automation to reduce development work in migration across accelerator architectures.
    3. Studying broadly adopted compute orchestration platforms and identifying novel enhancements for them, such as incorporating distributed system benefits and reducing operator complexity. Overall we welcome work that seeks to minimize operator intervention in distributed training

Technical deep dive: Your approach to building on Trainium

We invite applicants to study the Trainium and Inferentia accelerator design, available software libraries and our samples for Trainium. We welcome your detailed perspective about this toolset. We want to know what you think about how well the specific models and operations you intend to leverage in your research proposal should work on Trainium, giving the existing tools you see available today. Please plan on bringing your educated perspective about your approach to building on Trainium into the proposal.

Applicants are strongly encouraged to test small versions of their proposed software stacks on the Trainium and/or Inferentia instance families, using Neuron SDK solutions like NxD and NKI, in advance of submitting their proposals. The most compelling and ambitious proposals will present empirical results of their tests in the proposal itself. For details about how to get started on Trainium, follow instructions here.

Timeline

Submission period: March 19 to May 7, 2025 (11:59PM Pacific Time).
Decision letters will be sent out by August 2025.

Award details

Selected Principal Investigators (PIs) may receive the following:

  1. Applicants are encouraged to request AWS Promotional Credits in one of two ranges:
    1. AWS Promotional Credits, up to $50,000
    2. AWS Promotional Credits, up to $250,000 and beyond
  2. 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.

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 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.

    Selection criteria

    Proposals will be evaluated on the following:

    1. Creativity and quality of the scientific content
    2. Potential impact to the research community and society at large
    3. Interest expressed in open-sourcing model artifacts, datasets and development frameworks
    4. Intention to use and explore novel hardware for AI/ML, primarily AWS Trainium and Inferentia

    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 provide updates and feedback to ARA via surveys or reports on the status of their research. Award recipients will have an 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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    US, WA, Seattle
    Come be a part of a rapidly expanding $35 billion-dollar global business. At Amazon Business, a fast-growing startup passionate about building solutions, we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. Amazon Business Data Insights and Analytics team is looking for a Data Scientist to lead the research and thought leadership to drive our data and insights strategy for Amazon Business. This role is central in shaping the definition and execution of the long-term strategy for Amazon Business. You will be responsible for researching, experimenting and analyzing predictive and optimization models, designing and implementing advanced detection systems that analyze customer behavior at registration and throughout their journey. You will work on ambiguous and complex business and research science problems with large opportunities. You'll leverage diverse data signals including customer profiles, purchase patterns, and network associations to identify potential abuse and fraudulent activities. You are an analytical individual who is comfortable working with cross-functional teams and systems, working with state-of-the-art machine learning techniques and AWS services to build robust models that can effectively distinguish between legitimate business activities and suspicious behavior patterns You must be a self-starter and be able to learn on the go. Excellent written and verbal communication skills are required as you will work very closely with diverse teams. Key job responsibilities - Interact with business and software teams to understand their business requirements and operational processes - Frame business problems into scalable solutions - Adapt existing and invent new techniques for solutions - Gather data required for analysis and model building - Create and track accuracy and performance metrics - Prototype models by using high-level modeling languages such as R or in software languages such as Python. - Familiarity with transforming prototypes to production is preferred. - Create, enhance, and maintain technical documentation
    Economist, Customer Experience and Business Trends
    US, WA, Seattle
    Are you passionate about leveraging data and economics to enhance customer experience across Amazon's diverse businesses? The Customer Experience and Business Trends (CXBT) organization is seeking an Economist to join our Benchmarking Economics Analytics and Measurement (BEAM) team. Our mission is to drive customer experience improvements through innovative economic modeling, advanced analytics, and scalable scientific solutions. As an Economist on our team, you will collaborate with senior management, business stakeholders, scientists, engineers, and economics leadership to solve complex business challenges across Amazon's business lines. You'll develop sophisticated econometric models using our world-class data systems, applying diverse methodologies spanning causal inference, machine learning, and generative AI. In this fast-paced environment, you'll tackle challenging problems that directly influence strategic decision-making and drive measurable business impact. Key job responsibilities - Develop economic theory and deliver causal machine learning models at scale - Collaborate with cross-functional teams to translate research into scalable solutions - Write effective business narratives and scientific papers to communicate to both business and technical audiences - Drive data-driven decision making to improve customer experience About the team Customer Experience and Business Trends (CXBT) is an organization made up of a diverse suite of functions dedicated to deeply understanding and improving customer experience, globally. We are a team of builders that develop products, services, ideas, and various ways of leveraging data to influence product and service offerings – for almost every business at Amazon – for every customer (e.g., consumers, developers, sellers/brands, employees, investors, streamers, gamers). Our approach is based on determining the customer need, along with problem solving, and we work backwards from there. We use technical and non-technical approaches and stay aware of industry and business trends. We are a global team, made up of a diverse set of profiles, skills, and backgrounds – including: Product Managers, Software Developers, Computer Vision experts, Solutions Architects, Data Scientists, Business Intelligence Engineers, Business Analysts, Risk Managers, and more.
    Economist, Customer Experience and Business Trends
    US, WA, Seattle
    Are you passionate about leveraging data and economics to enhance customer experience across Amazon's diverse businesses? The Customer Experience and Business Trends (CXBT) organization is seeking an Economist to join our Benchmarking Economics Analytics and Measurement (BEAM) team. Our mission is to drive customer experience improvements through innovative economic modeling, advanced analytics, and scalable scientific solutions. As an Economist on our team, you will collaborate with senior management, business stakeholders, scientists, engineers, and economics leadership to solve complex business challenges across Amazon's business lines. You'll develop sophisticated econometric models using our world-class data systems, applying diverse methodologies spanning causal inference, machine learning, and generative AI. In this fast-paced environment, you'll tackle challenging problems that directly influence strategic decision-making and drive measurable business impact. Key job responsibilities - Develop economic theory and deliver causal machine learning models at scale - Collaborate with cross-functional teams to translate research into scalable solutions - Write effective business narratives and scientific papers to communicate to both business and technical audiences - Drive data-driven decision making to improve customer experience About the team Customer Experience and Business Trends (CXBT) is an organization made up of a diverse suite of functions dedicated to deeply understanding and improving customer experience, globally. We are a team of builders that develop products, services, ideas, and various ways of leveraging data to influence product and service offerings – for almost every business at Amazon – for every customer (e.g., consumers, developers, sellers/brands, employees, investors, streamers, gamers). Our approach is based on determining the customer need, along with problem solving, and we work backwards from there. We use technical and non-technical approaches and stay aware of industry and business trends. We are a global team, made up of a diverse set of profiles, skills, and backgrounds – including: Product Managers, Software Developers, Computer Vision experts, Solutions Architects, Data Scientists, Business Intelligence Engineers, Business Analysts, Risk Managers, and more.
    Applied Scientist, Sponsored Products
    US, VA, Arlington
    About Sponsored Products and Brands The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through 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. About our team The SPB Offsite team builds solutions to extend campaigns to reach customers off the store and extend shopping experiences on third party sites where shoppers search and discover products. We use industry leading machine learning, high scale low latency systems, and AI technologies to create better sponsored customer experiences off the store. This role will have deep interest in building the next innovations in ad tech and shopping wherever shoppers go. You will work with external and internal partners to connect ad tech systems, understand customers, and drive results at scale. You are a deeply technical leader who operates with a GenAI first approach to product, engineering, and science based solutions. As an Applied Scientist on this team, you will: - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Run A/B 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. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.
    Economist , Economic Decision Science
    GB, London
    We are looking for an Economist to work on exciting and challenging business problems related to Amazon Retail’s worldwide product assortment. You will build innovative solutions based on econometrics, machine learning, and experimentation. You will be part of a interdisciplinary team of economists, product managers, engineers, and scientists, and your work will influence finance and business decisions affecting Amazon’s vast product assortment globally. If you have an entrepreneurial spirit, you know how to deliver results fast, and you have a deeply quantitative, highly innovative approach to solving problems, and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you. Key job responsibilities * Work on a challenging problem that has the potential to significantly impact Amazon’s business position * Develop econometric models and experiments to measure the customer and financial impact of Amazon’s product assortment * Collaborate with other scientists at Amazon to deliver measurable progress and change * Influence business leaders based on empirical findings
    Applied Scientist, Sponsored Products and Brands
    US, WA, Seattle
    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. Calling all inventors to work on exciting new opportunities in Sponsored Products. We are a highly motivated, collaborative and fun-loving group with an entrepreneurial spirit and bias for action. You will join a newly-founded team with a broad mandate to experiment and innovate, with a focus on driving growth of sponsored products ad experiences across Amazon stores worldwide. This broad charter gives us the flexibility to explore and apply scientific techniques to novel product problems. You will have the satisfaction of seeing your work improve the experience of millions of Amazon shoppers worldwide while driving quantifiable revenue impact. More importantly, you will have the opportunity to broaden your technical skills, and be a science leader in an environment that thrives on creativity, experimentation, and product innovation. Key job responsibilities - Tackle and solve challenging science and business problems that balance the interests of advertisers, shoppers, and Amazon. - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Develop real-time machine learning algorithms to allocate billions of ads per day in advertising auctions. - Develop efficient algorithms for multi-objective optimization using deep learning methods to find operating points for the ad marketplace then evolve them - Research new and innovative machine learning approaches. - Recruit Scientists to the team and provide mentorship.
    Applied Scientist, Sponsored Products and Brands
    US, WA, Seattle
    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. Calling all inventors to work on exciting new opportunities in Sponsored Products! You will join a newly-founded team with a broad mandate to experiment and innovate, with a focus on driving growth of sponsored products ad experiences across Amazon stores worldwide. This broad charter gives us the flexibility to explore and apply scientific techniques to novel product problems. You will have the satisfaction of seeing your work improve the experience of millions of Amazon shoppers worldwide while driving quantifiable revenue impact. More importantly, you will have the opportunity to broaden your technical skills, and be a science leader in an environment that thrives on creativity, experimentation, and product innovation. Key job responsibilities - Tackle and solve challenging science and business problems that balance the interests of advertisers, shoppers, and Amazon. - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Develop real-time machine learning algorithms to allocate billions of ads per day in advertising auctions. - Develop efficient algorithms for multi-objective optimization using deep learning methods to find operating points for the ad marketplace then evolve them - Research new and innovative machine learning approaches. - Recruit Scientists to the team and provide mentorship.
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