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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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    Senior Applied Scientist, Amazon Q Developer Science
    US, WA, Seattle
    The Amazon Q Developer Science team is looking for an Applied Scientist who is passionate about building services and tools for developers that leverage artificial intelligence (AI) agents and machine learning (ML). You will be part of a team building AI-based services for Amazon Q Developer with the focus on redefining the way developer work. The team works in close collaboration with other AWS AI services such as AWS Bedrock, the AWS IDE Toolkit, and Amazon Sagemaker. If you are excited about working in cloud computing and building new AWS services, then we'd love to talk to you. Key job responsibilities As a senior Applied Scientist, you are recognized for your expertise, advise team members on a range of machine learning topics, and work closely with software engineers to drive the delivery of end-to-end modeling solutions. Your work focuses on ambiguous problem areas where the business problem or opportunity may not yet be defined. The problems that you take on require scientific breakthroughs. You take a long-term view of the business objectives, product roadmaps, technologies, and how they should evolve. You drive mindful discussions with customers, engineers, and scientist peers. You bring perspective and provide context for current technology choices, and make recommendations on the right modeling and component design approach to achieve the desired customer experience and business outcome. 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. Utility Computing (UC) 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, including support for customers who require specialized security solutions for their cloud services. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. 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. Mentorship and 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. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred 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.
    Applied Scientist, Artificial General Intelligence, AGI Data Services
    US, MA, Boston
    The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
    Applied Scientist II, Sponsored Products Marketplace Intelligence
    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. We are the Sponsored Products - Marketplace Intelligence (MI) team. We are looking for an Applied Scientist to help build production ML and bandit solutions to customize the search experience. We determine which ads to show in Amazon search, where to place them, how many ads to place, and to which customers. This helps shoppers discover new products while helping advertisers put their products in front of the right customers, aligning shoppers’, advertisers’, and Amazon’s interests. To do this, we apply a broad range of machine learning, causal inference, and optimization techniques to continuously explore, learn, and optimize the allocation and ranking of ads on the search page. We are an interdisciplinary team with a focus on customer obsession and inventing and simplifying. Our primary focus is on improving the SP experience in search by gaining a deep understanding of shopper pain points and developing new innovative solutions to address them. You will be on the Search Ad Ranking and Interleaving team org - specifically the team that focusses on whole page optimization. Our mission is to personalize and contextualize SP ad allocation on the entire search page. We do this by modeling shopper responses to the number, placement, and quality of ads. We are a data- and hypothesis-driven organization that uses online experimentation, simulation, causal modeling, and online feedback to place ads where they’re useful to shoppers and provide improved discoverability and sales for advertisers. This is a unique opportunity for someone who wants to have broad business impact, a direct impact on customers and the search experience, and get broad exposure to a wide range of scientific techniques (machine learning, bandit learning, optimization, LLMs). We are looking for an Applied Scientist to join Interleaving team in Marketplace Intelligence with a broad mandate to experiment and innovate to grow Sponsored Products. We’d like someone with practical experience with LLMs / GenAI for production to improve how we rank and allocate ads on the page today. If you thrive in a product-focussed and data-driven environment, then this role is for you. As a Applied Scientist on this team, you will help to identify unique opportunities to create customized and delightful shopping experience for our growing marketplaces worldwide. Your job will be to identify big opportunities for the team that can help to grow Sponsored Products business working with retail partner teams, product managers, software engineers and TPMs. You will have opportunity to design, run and analyze / experiments to improve the experience of millions of Amazon shoppers while driving quantifiable revenue impact. More importantly, you will have the opportunity to broaden your technical skills 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. * Develop real-time machine learning algorithms to allocate billions of ads per day in advertising auctions. * Develop efficient algorithms for multi-objective optimization and AI control methods to find operating points for the ad marketplace then evolve them * Be an expert at designing and implementing solutions that use a range of data science methodologies to automate data analysis or to solve complex business problems. * Perform hands-on analysis and modeling of enormous data sets to develop insights that improve shopper experience, without compromising Ad revenue in addition to designing metrics for complex systems. * Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, complexity. * Run A/B experiments, gather data, and perform statistical analysis.
    Applied Scientist, Alexa Sensitive Content Intelligence (ASCI)
    IN, KA, Bengaluru
    Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Privacy team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa. We are looking for an experienced Senior Applied Scientist to build industry-leading technologies in attribute extraction and sensitive content detection across all languages and countries. An Applied Scientist will be in a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in Natural Language Processing (NLP) or Computer Vision (CV) related tasks. They will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. They will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon. Their work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. We are looking for candidate with strong technical experiences and a passion for building scientific driven solutions in a fast-paced environment. This Senior Applied Scientist should have good understanding of NLP models (e.g. LSTM, transformer based models) or CV models (e.g. CNN, AlexNet, ResNet) and where to apply them in different business cases. They should leverage exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience of building large-scale distributed systems to creating reliable, scalable, and high-performance products. In addition to technical depth, they must possess exceptional communication skills and understand how to influence key stakeholders. This Applied Scientist will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you. Key job responsibilities This Applied Scientist will lead the science solution design, run experiments, research new algorithms, and find new ways of optimizing customer experience. They will set examples for the team on good science practice and standards. Besides theoretical analysis and innovation, they will work closely with talented engineers and ML scientists to put algorithms and models into practice. This Applied Scientist's work will also directly impact the trust customers place in Alexa, globally. They will contribute directly to our growth by hiring smart and motivated scientists to establish teams that can deliver swiftly and predictably, adjusting in an agile fashion to deliver what our customers need. A day in the life You will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership. About the team The mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video.
    Senior Economist, Device Economics
    US, WA, Seattle
    Device Economics is looking for a senior economist experienced in causal inference, machine learning, empirical industrial organization, and scaled systems to work on business problems to advance critical resource allocation and pricing decisions in the Amazon Devices org. Senior roles lead vision setting, methods innovation, and act as thought leaders to Devices finance and business executives. Output will be included in scaled systems to automate existing processes and to maximize business and customer objectives. Amazon Devices designs and builds Amazon first-party consumer electronics products to delight and engage customers. Amazon Devices represents a highly complex space with 100+ products across several product categories (e-readers [Kindle], tablets [Fire Tablets], smart speakers and audio assistants [Echo], wifi routers [eero], and video doorbells and cameras [Ring and Blink]), for sale both online and in offline retailers in several regions. The space becomes more complex with dynamic product offering with new product launches and new marketplace launches. The Device Economics team leads in analyzing these complex marketplace dynamics to enable science-driven decision making in the Devices org. Device Economics achieves this through scientific applications that provide deep understanding of customer preferences. Our team’s outputs inform product development decisions, investments in future product categories, and product pricing and promotion. We have achieved substantial impact on the Devices business, and will achieve more. Device Economics seeks an experienced economist adept in measuring customer preferences and behaviors with proven capacity to innovate, scale measurement, drive rigor, and mentor talent. Key job responsibilities The candidate will work with Amazon Devices science leadership to refine science roadmaps, models, and priorities for innovation and simplification, and advance adoption of insights to influence important resource allocation and prioritization decisions. Effective communication skills (verbal and written) are required to ensure success of this collaboration. The candidate must be passionate about advancing science for business and customer impact.
    Sr Applied Scientist, CreativeX Ad Formats
    US, CA, Santa Monica
    Amazon Advertising operates at the intersection of eCommerce and advertising, offering a rich array of advertising solutions with the goal of helping our customers find and discover anything they want to buy. We help advertisers reach Amazon customers on Amazon owned and operated sites, other high quality sites across the web, and on millions of TV, tablet, and mobile devices. We start with the customer and work backwards in everything we do, including advertising. If you're interested in working in a world-class organization with a relentless focus on the customer, you've come to the right place! Our team within the CreativeX organization drives engaging user experiences through content and interactivity enrichment, enabling advertisers to reach target audiences at scale. We are looking for a passionate, talented, and resourceful Senior Applied Scientist in the field of Personalization and Recommender Systems to invent and build scalable solutions for more engaging and customized ads for brands of all sizes across the marketing funnel. Key job responsibilities The scientist will lead research and development for streaming TV dynamic creative optimization and content personalization to drive measurable improvements in user engagement and advertising performance. Partner with cross-functional engineering, product, and business teams to execute our product vision, design and implement scalable machine learning solutions that leverage generative AI capabilities, drive innovation in AI-powered content optimization and audience engagement, and demonstrate strong technical leadership, stakeholder management, and project execution skills.
    Sr. Battery System Engineer
    CN, 44, Shenzhen
    As a Battery System Engineer, you will engage with an experienced cross-disciplinary staff to conceive, and design innovative consumer product. You will work closely with an internal interdisciplinary team, and outside partners to drive key aspects of product definition and execution. You must be responsive, flexible, and able to succeed within an open collaborative peer environment. The role operates primarily in high-ambiguity problem spaces, requires original scientific judgment, and produces reusable scientific assets that influence multiple programs, suppliers, and long-term roadmaps. In this role, you will: 1. Lead the design, development, and delivery of Li-ion battery system per performance and safety requirements 2. Drive battery development from NPI through mass production 3. Research and evaluate emerging battery technologies 4. Collaborate with product teams to define battery specifications 5. Design battery protection circuit and pack design for NPI programs include schematic design, and component selection. 6. Develop and review battery pack schematics, BOMs and layout to meet design requirements 7. Conduct system and design reviews, failure mode and effects analysis (DFMEA), and risk assessments 8. Analyze and resolve battery-related issues in production and field 9. Perform battery safety assessment and design for safety 10. Support battery certification processes (CTIA/IEEE1725) 11. Manage and coordinate with CMs (contract manufacturers) on battery development for NPI programs 12. Build and maintain strong relationships with suppliers and manufacturing partners
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