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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, ASCS AI Lab Team
US, WA, Seattle
We are seeking a Senior Applied Scientist to join our team in developing pioneering AI research, Generative AI, Agentic AI, Large Language Models (LLMs), Diffusion and Flow Models, and other advanced Machine Learning and Deep Learning solutions for Amazon Selection and Catalog Systems, within the AI Lab Team. This role offers a unique opportunity to work on AI research and AI products that will shape the future of online shopping experiences. Our team operates at the forefront of AI research and development, working on challenges that directly impact millions of customers worldwide. We push the boundaries of AI at both the foundational and application layers. As a Senior Applied Scientist, you will have the chance to experiment with LLMs and deep learning techniques, apply your research to solve real-world problems at an unprecedented scale, and collaborate with experienced scientists to contribute to Amazon's scientific innovation. Join us in redefining the future of shopping. Your work will directly influence how customers interact with the world's largest online store. Key job responsibilities - Design and implement novel AI solutions for Amazon catalog of products - Develop and train state-of-the-art LLMs, Diffusion Models, and other Generative AI models - Build and deploy autonomous AI Agents in Amazon production ecosystem - Scale AI models to handle billions of diverse products across multiple languages and geographies - Conduct research in areas such as Autonomous AI Agents, Generative AI, Language Modeling, Multi-modality Computer Vision, Diffusion Models, Reinforcement Learning - Collaborate with cross-functional teams to integrate AI models into Amazon's production ecosystem - Contribute to the scientific community through publications and conference presentations
Sr. Applied Scientist, Pricing Science
US, WA, Seattle
Here's the job description with causal ML woven in: We are looking for a talented, organized, and customer-focused applied researcher to join our Pricing Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon. This role requires an individual with exceptional machine learning modeling and architecture expertise — particularly in deep learning, neural networks, and transformer-based architectures applied to price prediction and forecasting problems. Equally important is deep expertise in causal machine learning — including causal inference, treatment-effect estimation, and experimentation methods (e.g., uplift modeling, double/debiased machine learning, instrumental variables, and A/B and quasi-experimental design) — to isolate the true impact of pricing and promotion decisions on customer behavior and business outcomes. The ideal candidate brings a strong foundation in applied statistics and probabilistic modeling, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment. Key job responsibilities See the big picture. Understand and influence the long-term vision for Amazon's science-based competitive, perception-preserving pricing techniques. Develop and advance price prediction models leveraging deep learning frameworks, transformer architectures, and advanced statistical methods to drive pricing accuracy at scale. Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale. Design and implement neural network-based architectures — including sequence models and transformers — for large-scale price prediction and optimization. Stay informed. Establish mechanisms to stay up to date on the latest scientific advancements in deep learning, transformer architectures, applied statistics, neural network design, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems. Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery. Leverage statistical rigor and modern deep learning approaches to validate hypotheses and drive measurable pricing improvements. Successfully execute & deliver. Apply your exceptional technical machine learning expertise — including deep neural networks, attention-based models, and applied statistical analysis — to incrementally move the needle on some of our hardest pricing problems. A day in the life We are hiring a Sr. Applied Scientist to drive our pricing optimization initiatives. We drive cross-domain and cross-system improvements through: * shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs. * Error detection and price quality guardrails at scale. * Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) Price is a highly relevant input into Stores architectures; this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication. About the team The Pricing Optimization science group builds and refines Amazon's algorithmic pricing and promotion models at scale. Our team combines expertise in deep learning, transformer architectures, applied statistics, and probabilistic forecasting to develop price prediction systems that directly impact the customer experience. The team also brings hands-on experience with causal modeling and inference — including uplift modeling and treatment effect estimation — to rigorously measure the impact of pricing decisions on customer behavior and business outcomes. We partner closely with product, engineering, and business teams to take solutions from research through production deployment.
Sr. Applied Scientist, MAPLE
IN, KA, Bengaluru
Are you excited by the idea of developing personalized experiences for Amazon customers as they shop? Are you looking for new challenges and to solve hard science problems while applying state-of-the-art recommendation system modeling and GenAI techniques? Join us and you'll help millions of customers make informed purchase decisions while also advancing the state of Amazon's science by publishing research! Key job responsibilities - Participate in the design, development, evaluation, deployment and updating of data-driven models for shopping personalization. - Develop and test new signals for improving recommendation models - Use supervised and uplift learning algorithms to improve customer experience - Contribute to production code and science tooling - Design A/B tests and conduct statistical analysis on their results - Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers - Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area - Present and publish science research internally and externally, contributing to Amazon's science community - Mentor junior engineers and scientists. About the team Our team's mission is to surface the right payments-related recommendations to customers at the right time, helping create a rewarding and successful shopping experience for Amazon's customers. Our team's culture is highly collaborative, with an emphasis on supporting each other and learning from one another. We dedicate time each week to focus on personal development and expanding our knowledge as a team. We also highly value having a big impact, both for Amazon's business and for our customers.
Applied Scientist II, ASCS AI Lab Team
US, CA, Sunnyvale
We are seeking an Applied Scientist II to join our team in developing pioneering AI research, Generative AI, Agentic AI, Large Language Models (LLMs), Diffusion and Flow Models, and other advanced Machine Learning and Deep Learning solutions for Amazon Selection and Catalog Systems, within the AI Lab Team. This role offers a unique opportunity to work on AI research and AI products that will shape the future of online shopping experiences. Our team operates at the forefront of AI research and development, working on challenges that directly impact millions of customers worldwide. We push the boundaries of AI at both the foundational and application layers. As a Applied Scientist, you will have the chance to experiment with LLMs and deep learning techniques, apply your research to solve real-world problems at an unprecedented scale, and collaborate with experienced scientists to contribute to Amazon's scientific innovation. Join us in redefining the future of shopping. Your work will directly influence how customers interact with the world's largest online store. Key job responsibilities - Design and implement novel AI solutions for Amazon catalog of products - Develop and train state-of-the-art LLMs, Diffusion Models, and other Generative AI models - Build and deploy autonomous AI Agents in Amazon production ecosystem - Scale AI models to handle billions of diverse products across multiple languages and geographies - Conduct research in areas such as Autonomous AI Agents, Generative AI, Language Modeling, Multi-modality Computer Vision, Diffusion Models, Reinforcement Learning - Collaborate with cross-functional teams to integrate AI models into Amazon's production ecosystem - Contribute to the scientific community through publications and conference presentations
Senior Applied Scientist, PXT
US, WA, Seattle
Do you want to leverage your expertise in translating innovative science into impactful products to improve the lives and work of over a million people worldwide? If so, People eXperience Technology Core Science team would love to discuss how you can make that a reality. Our team is an interdisciplinary team that uses behavioral science, statistics, and machine learning to identify products, mechanisms, and process improvements that enhance Amazonians' well-being and their ability to deliver value for Amazon's customers. We collaborate with HR teams across Amazon to make Amazon PXT the most scientific human resources organization in the world. In this role, you will spearhead science design and technical implementation innovations across our talent solution science work-streams. You'll enhance existing models and create new ones, empowering leaders throughout Amazon to make data-driven business decisions. You'll collaborate with scientists and engineers to deliver solutions while working closely with business stakeholders to address their specific needs. Your work will span various business domains (corporate, operations, safety) and analysis levels (individual, group, organizational), utilizing a range of modeling approaches (linear, tree-based, deep neural networks, and LLM-based). You'll develop end-to-end ML solutions from problem formulation to deployment, maintaining high scientific standards and technical excellence throughout the process. As an Applied Scientist, you'll also contribute to the team's science strategy, keeping pace with emerging AI/ML trends. You'll mentor junior scientists, fostering their growth by identifying high-impact opportunities. Your guidance will span different analysis levels and modeling approaches, enabling stakeholders to make informed, strategic decisions. If you excel at building advanced scientific solutions and are passionate about developing technologies that drive organizational change in the AI era, join us as we work hard, have fun, and make history. Key job responsibilities Key job responsibilities • Model Development & Innovation: Design and implement novel GenAI/LLM solutions using foundation models (e.g., Claude, GPT) and AWS services including Amazon Bedrock, SageMaker, and other AWS AI/ML tools • Research & Experimentation: Conduct applied research to advance the state-of-the-art in LLM applications, including prompt engineering, few-shot learning, fine-tuning, and model evaluation • Production Deployment: Build scalable, production-ready AI systems that serve millions of requests with high reliability, low latency, and cost efficiency • Cross-Functional Collaboration: Partner with product managers, engineers, and business stakeholders to translate business requirements into technical solutions and drive measurable impact • Technical Leadership: Mentor junior scientists, contribute to technical strategy, and establish best practices for GenAI development across the organization • Evaluation & Metrics: Design rigorous evaluation frameworks to measure model performance, bias, safety, and business impact • Documentation & Influence: Publish technical papers, create documentation, and influence both technical and non-technical audiences About the team The People eXperience and Technology (PXT) Core Science Team uses science, engineering, and customer-obsessed problem solving to proactively identify mechanisms, process improvements, and products that simultaneously improve Amazon and Amazonians' lives, wellbeing, and value of work. As an interdisciplinary team combining talents from machine learning, statistics, economics, behavioral science, engineering, and product development, the Core Science team develops and delivers measurable solutions through innovation and rapid prototyping to accelerate informed, accurate, and reliable decision-making backed by science and data. We are building a talent intelligence layer — fusing natural language understanding, network science, and large-scale predictive modeling into a unified platform that continuously learns from how people work, collaborate, and grow across one of the world's largest and most complex workforces.
Applied Scientist, Alexa for Shopping (Rufus) Experience Science
US, CA, Palo Alto
Amazon Alexa for Shopping (Rufus) Experience Science team is seeking a highly motivated Scientist who is passionate about building next-generation agentic shopping experiences. In this role, you will be developing Agentic Language Models to unlock new shopping experiences. Prior experiences with LLM Post-training techniques, Reinforcement Learning Training and business specific RL gym creation is preferred. You will collaborate closely with a multidisciplinary team of scientists, engineers, product managers, and designers to deliver these experiences across multiple Rufus customer-facing features. You will thrive in this role if you enjoy bringing latest LLM post-training research into everyday life—both for customers and for yourself. There’s nothing quite like realizing that a model you deployed yesterday is already improving your own shopping experience today. You will work side by side with scientists and engineers in a fast-paced environment, driving rapid model development and experimentation. You’ll also have access to Amazon’s rich datasets, AWS’s massive computational resources, and a network of world-class science and engineering leaders across the company.
Applied Scientist II, Alexa International Tech
IN, KA, Bengaluru
Alexa International is looking for a passionate, talented, and inventive Applied Scientist to help build industry-leading technology with Large Language Models (LLMs), ASR, TTS, and Speech to Speech models, requiring strong deep learning and generative models knowledge. You will contribute to developing novel solutions and deliver high-quality results that impact Alexa's international products and services. Key job responsibilities As an Applied Scientist with the Alexa International team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with Large Language Models (LLMs), ASR, TTS, and Speech to Speech model. Your work will directly impact our international customers in the form of products and services that make use of digital assistant technology. You will leverage Amazon's heterogeneous data sources, unique and diverse international customer nuances and large-scale computing resources to accelerate advances in text, voice, and vision domains in a multimodal setup. The ideal candidate possesses a solid understanding of machine learning, natural language understanding, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environments to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and collaborate effectively with cross-functional teams. A day in the life * Analyze, understand, and model customer behavior and the customer experience based on large-scale data. * Build novel online & offline evaluation metrics and methodologies for multimodal personal digital assistants. * Drive research in ASR, TTS, and Speech-to-Speech (S2S) model training and fine-tuning * Advance multilingual speech recognition and synthesis using LLM-based architectures * Fine-tune/post-train LLMs using techniques like SFT, DPO, RLHF, and RLAIF. * Collaborate with partner teams on evaluation frameworks and post-training methodologies. * Communicate solutions clearly to partners and stakeholders. * Contribute to the scientific community through publications and community engagement.
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