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AWS AI: Generative AI call for proposals — Spring 2023

Advancing the frontiers of machine learning.

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About this CFP

AWS offers a broad and deep set of tools for businesses to create impactful 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 AI aims to advance machine learning research by funding development of open-source tools and research that benefit the machine learning community at large, or impactful research that uses machine learning tools on AWS, including AWS AI Services, AWS ML Services (AWS AI Services, AWS ML Services (Amazon SageMaker, Amazon SageMaker Ground Truth, Amazon SageMaker Neo, and Amazon Augmented AI), and Apache MXNet on AWS.

AWS AI is soliciting funding proposals related to generative AI. We specifically aim to solicit funding proposals that focus on innovation related to supporting annotators with machine learning and artificial intelligence. Proposals should be attached to one or more of the following themes:

  1. New approaches that improve the capabilities of foundational models without fine-tuning: chaining, advanced prompting techniques (e.g., chain of thought, self-consistency), integration with external services and APIs, data augmented generation, etc.).
  2. Novel approaches that improve the capabilities of generative models to perform complex and composite tasks, potentially involving multiple types of inputs and outputs (text, images, videos, audio, structured data).
  3. Novel approaches for using generative models to create synthetic data sets at scale with specified properties.
  4. Work on open source projects that provide building blocks for developing applications with generative AI.
  5. New approaches to responsibly building and operating generative models, including measuring, preventing or mitigating hallucinations, biases or other forms of harmful or undesired output.

Other generative AI topics are also welcome.

Timeline

Submission period: March 15 to April 26, 2023.

Decision letters will be sent out August 2023.

Award details

Selected Principal Investigators (PIs) may receive the following:

  • Unrestricted funds, no more than $70,000 USD on average
  • AWS Promotional Credits, no more than $50,000 USD on average
  • Training resources, including AWS tutorials and hands-on sessions with Amazon scientists and engineers

Awards are structured as one-year 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.

Eligibility requirements

Please refer to the ARA Program rules on the FAQ page.

Proposal requirements

PIs are encouraged to exemplify how their proposed techniques or research studies will apply to tasks related to generative AI. 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

ARA will make the funding decisions based on the creativity and quality of the scientific content, and potential impact to the research community and society at large.

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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Principal Applied Scientist, WorkSpaces for AI Agents, Applied AI Solutions
US, WA, Seattle
We are seeking a Principal Applied Scientist to own the scientific vision across Agentic WorkSpaces. This is a foundational role spanning the full portfolio — Personal, Applications, and Core, and the agentic surfaces (WS4Builders and WorkSpaces for Agents). You will define how we measure, improve, and guarantee the performance of AI agents and human-AI teams. A core part of the role is defining the science agenda itself — identifying which problems are most worth solving and where the highest-leverage bets lie. Directions worth exploring might include Organizational Intelligence (turning institutional knowledge into agent-consumable skills), AI Agent Experience / AiAX (agent observability and autonomous remediation), and contextual, behavioral security that adapts enforcement in real time for human and agent sessions — but these are illustrative examples, not a fixed roadmap, and many other directions are possible. You will help define which ones we pursue. The problems you will solve do not have established industry patterns. You will set the direction for the science of how AI agents and people perceive, reason about, and act reliably within computing environments at enterprise scale. Key job responsibilities - Set the long-term scientific vision: Define what best-in-class agent performance, evaluation, and learning look like across Agentic WorkSpaces — for computer-using agents and human-AI teams alike. Identify the unsolved scientific problems, chart a multi-year research roadmap, and secure buy-in from VP-level leadership. - Solve highly ambiguous, novel problems: Independently frame and deliver solutions to foundational challenges in agent perception, reasoning, evaluation, reliability, and human-AI collaboration — problems where neither the approach nor the success criteria are pre-defined. - Own the evaluation and measurement foundation: Build the benchmarks, datasets, and metrics that quantify agent and team accuracy, cost, productivity, and safety across the portfolio and diverse enterprise workflows, and that gate what we ship. - Drive cross-organizational scientific alignment: Work across partner teams (AgentCore, Bedrock model teams, Identity, Security, the MCP ecosystem) and across the Applied AI Solutions product portfolio to shape how models and agent frameworks are applied, and ensure scientific decisions compose into a coherent system. - Deliver measurable business impact: Ensure research translates to customer outcomes: higher task accuracy, lower cost-per-action, faster time-to-production, measurable productivity for human-AI teams, and the trust that lets enterprises scale agent workflows. - Raise the scientific bar: Establish rigor in experimentation, evaluation, and reproducibility. Mentor and grow senior scientists and engineers. Set the standard for applied science quality across the organization. - Advance the state of the art: Contribute to the external technical community through publications, patents, and open-source contributions that position AWS as the leader in the science of secure agent-computer interaction and human-AI teamwork. About the team AWS Applied AI Solutions' (AAIS) vision is every business innovating with Amazon AI teammates. Our mission is to build delightful AI solutions that improve human capabilities and business outcomes. The Agentic WorkSpaces organization within AAIS envisions a world where people, teams, and AI collaborate securely from anywhere to create unprecedented value for every organization. We build lovable products that empower every business to unlock the full potential of human-AI teamwork, driving smarter decisions, greater creativity, more value, and faster innovation with confidence. Amazon Agentic WorkSpaces (AAWS) is building the world's most lovable, secure, and trusted always-on workspace where AI agents and humans work as partners behind enterprise-grade security. Our portfolio spans persistent desktops (Personal), application streaming (Applications), and Core, and is evolving into the governed operating environment for the hybrid workforce: humans get AI-native desktops for their role, and agents get governed desktops scoped to their task, with administrators managing both as one. This surface includes WS4Builders (an AI-native environment for builders) and WorkSpaces for Agents (W4A) — enabling AI agents to work the way humans do, with access to real applications, real interfaces, and real computing environments. Enterprises want to use AI agents for critical business workloads that touch legacy desktop applications and mainframes, yet 75% of organizations run legacy applications that lack modern APIs, and 90% of corporate data remains locked in systems never designed for agents. Agentic WorkSpaces solves this: it gives enterprises a secure, governed environment where agents and humans operate both legacy and modern applications directly, just as an employee would, without costly migrations.
Senior Manager, Applied Science
US, WA, Seattle
We are seeking a Senior Manager, Applied Science to build and lead the science organization across Agentic WorkSpaces. This is a foundational leadership role spanning the full portfolio — Personal, Applications, and Core, and the agentic surfaces (WS4Builders and WorkSpaces for Agents). You will hire, grow, and lead a team of applied scientists who define how we measure and improve the performance of AI agents and human-AI teams. A core part of the role is defining the science agenda itself — identifying which problems are most worth solving and where the highest-leverage bets lie. Directions worth exploring might include Organizational Intelligence (turning institutional knowledge into agent-consumable skills), AI Agent Experience / AiAX (agent observability and autonomous remediation), and contextual, behavioral security that adapts enforcement in real time for human and agent sessions — but these are illustrative examples, not a fixed roadmap, and many other directions are possible. You and your team will define which ones we pursue. The problems your team will solve do not have established industry patterns. You will set the scientific direction and build the team that determines how AI agents and people perceive, reason about, and act reliably within computing environments at enterprise scale. What You Will Do Build and lead the applied science team. Hire, develop, and retain a high-caliber team of applied scientists spanning the Agentic WorkSpaces portfolio. Set the bar for scientific talent, create the growth paths, and build the culture that makes AAWS a destination for the best agent and human-AI researchers. Own the science strategy across the portfolio. Direct the research agenda for how we measure and improve agents and human-AI teams: the benchmarks, task suites, and metrics (accuracy, cost-per-task, task completion, productivity) that turn subjective "it works" judgments into rigorous, reproducible measurement that gates what we ship. Define and drive high-leverage research directions. Work with your team to identify the problems most worth solving and shape the science agenda. Directions worth exploring might include how agents combine deterministic tool use (MCP) with visual reasoning from computer use; Organizational Intelligence and workflow learning (learning from expert recordings, voice annotations, and SOPs); and AI Agent Experience / AiAX (detecting when agents are stuck or degrading productivity and autonomously remediating) — these are illustrative starting points, and your team will weigh them against many other possibilities. Translate science into shipped product. Partner with engineering, product, and program leaders to move models, evaluation, and learning systems from prototype into a decade-old production service operating at massive scale, without compromising the reliability that customers depend on. Represent science in leadership and to customers. Be the scientific voice in org-level planning and roadmap decisions across AAWS, and engage directly with enterprise customers on how agent performance, safety, and human-AI productivity are measured and earned. Key job responsibilities Build and lead the applied science team. Hire, develop, and retain a high-caliber team of applied scientists spanning the Agentic WorkSpaces portfolio. Set the bar for scientific talent, create the growth paths, and build the culture that makes AAWS a destination for the best agent and human-AI researchers. Own the science strategy across the portfolio. Direct the research agenda for how we measure and improve agents and human-AI teams: the benchmarks, task suites, and metrics (accuracy, cost-per-task, task completion, productivity) that turn subjective "it works" judgments into rigorous, reproducible measurement that gates what we ship. Define and drive high-leverage research directions. Work with your team to identify the problems most worth solving and shape the science agenda. Directions worth exploring might include how agents combine deterministic tool use (MCP) with visual reasoning from computer use; Organizational Intelligence and workflow learning (learning from expert recordings, voice annotations, and SOPs); and AI Agent Experience / AiAX (detecting when agents are stuck or degrading productivity and autonomously remediating) — these are illustrative starting points, and your team will weigh them against many other possibilities. Translate science into shipped product. Partner with engineering, product, and program leaders to move models, evaluation, and learning systems from prototype into a decade-old production service operating at massive scale, without compromising the reliability that customers depend on. Represent science in leadership and to customers. Be the scientific voice in org-level planning and roadmap decisions across AAWS, and engage directly with enterprise customers on how agent performance, safety, and human-AI productivity are measured and earned. Set the long-term scientific vision and team strategy: Define what best-in-class agent performance, evaluation, and learning look like across Agentic WorkSpaces — for computer-using agents and human-AI teams alike. Chart a multi-year research roadmap, and build the team and plan to deliver it. Secure buy-in from VP-level leadership. Hire and grow scientific talent: Own recruiting, calibration, development, and retention for the science team. Mentor scientists toward senior and principal scope, and raise the scientific bar across the organization. Direct research on highly ambiguous, novel problems: Guide the team through foundational challenges in agent perception, reasoning, evaluation, reliability, and human-AI collaboration — problems where neither the approach nor the success criteria are pre-defined. Drive cross-organizational alignment: Work across partner teams (AgentCore, Bedrock model teams, Identity, Security, the MCP ecosystem) and across the Applied AI Solutions product portfolio, with product and engineering leadership, to ensure scientific decisions compose into a coherent product. Deliver measurable business impact: Ensure your team's research translates to customer outcomes: higher task accuracy, lower cost-per-action, faster time-to-production, measurable productivity for human-AI teams, and the trust that lets enterprises scale agent workflows. Establish scientific rigor and operational excellence: Set the standard for experimentation, evaluation, and reproducibility, and the mechanisms that keep the science organization productive and accountable. Advance the state of the art: Enable and champion contributions to the external technical community through publications, patents, and open-source work that position AWS as the leader in the science of secure agent-computer interaction and human-AI teamwork. About the team AWS Applied AI Solutions' (AAIS) vision is every business innovating with Amazon AI teammates. Our mission is to build delightful AI solutions that improve human capabilities and business outcomes. The Agentic WorkSpaces organization within AAIS envisions a world where people, teams, and AI collaborate securely from anywhere to create unprecedented value for every organization. We build lovable products that empower every business to unlock the full potential of human-AI teamwork, driving smarter decisions, greater creativity, more value, and faster innovation with confidence. Amazon Agentic WorkSpaces (AAWS) is building the world's most lovable, secure, and trusted always-on workspace where AI agents and humans work as partners behind enterprise-grade security. Our portfolio spans persistent desktops (Personal), application streaming (Applications), and Core, and is evolving into the governed operating environment for the hybrid workforce: humans get AI-native desktops for their role, and agents get governed desktops scoped to their task, with administrators managing both as one. This surface includes WS4Builders (an AI-native environment for builders) and WorkSpaces for Agents (W4A) — enabling AI agents to work the way humans do, with access to real applications, real interfaces, and real computing environments. Enterprises want to use AI agents for critical business workloads that touch legacy desktop applications and mainframes, yet 75% of organizations run legacy applications that lack modern APIs, and 90% of corporate data remains locked in systems never designed for agents. Agentic WorkSpaces solves this: it gives enterprises a secure, governed environment where agents and humans operate both legacy and modern applications directly, just as an employee would, without costly migrations.
Applied Scientist I, Alexa Shopping
US, WA, Seattle
We’re working to improve shopping on Amazon using the conversational capabilities of LLMs, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You'll be working with talented scientists, engineers, across the breadth of Amazon Shopping and AGI to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!
Sr. Applied Scientist , Personalization
US, WA, Seattle
How can we improve the customer experience by tailoring what we display on our pages based on available data? How do we build models that help us innovate in different ways to enhance customer experience? What is the relationship between what customers do on the site vs. what they actually buy? How do we do all of this without asking the customer a single question? Our team's stated missions is to "grow each customer’s relationship with Amazon by leveraging our deep understanding of them to provide relevant and timely product, program, and content recommendations." Recommendations at Amazon is a way to help customers discover products. Our team strives to better understand how customers shop on Amazon (and elsewhere) and build recommendations models to streamline customers' shopping experience by showing the right products at the right time. Understanding the complexities of customers' shopping needs and helping them explore the depth and breadth of Amazon's catalog is a challenge we take on every day. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations, and run these models directly on the retail website. You will participate in the Amazon ML community and mentor Applied Scientists and software development engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and the retail business and you will measure the impact using scientific tools. We are looking for a passionate, hard-working, and talented Applied Scientist who has experience building mission critical, high volume applications that customers love. You will have an opportunity to make an enormous impact on the design, architecture, and implementation of cutting edge products used everyday by people you know.
Applied Scientist , Personalization & Ranking
US, WA, Seattle
Are you a scientist interested in pushing the state of the art in machine learning and recommendation systems? Are you interested in working on novel ideas that can positively impact millions of customers? Do you wish you had access to large datasets and tremendous computational resources? Answer yes to any of these questions and you will be a great fit for our team at Amazon. As an Senior Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for Personalization. You will adopt or invent new machine learning and analytical techniques in the realm of recommendations and large language models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products and systems. Key job responsibilities - Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to optimize the shopping experience, and run these models directly on the retail website. - Develop AI solutions for Recommendation systems using Deep learning, LLMs, Reinforcement Learning, distillation, and Optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses; - Effectively communicate technical and non-technical ideas with teammates and stakeholders; - Stay up-to-date with advancements and the latest modeling techniques in the field;
Applied Scientist, Amazon Selection and Catalog Systems (ASCS)
US, NY, New York
At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research
Applied Science Manager, AWS Startups
US, WA, Seattle
Trusted by more startups around the world, AWS makes the power of cloud computing accessible for all by giving founders everywhere access to the same technology that powers the world's largest companies. With nearly two decades of experience supporting hundreds of thousands of startups, including 80% of unicorns, we democratize cloud computing to help founders bring their innovative ideas to life. We support founders at every stage of their journey, from initial onboarding and credit programs to AI-powered guidance and scale solutions. Data is central to how we do this: it helps us identify high-potential startups early, personalize the guidance we deliver, and prioritize where we can create the most value for founders and for AWS. We are seeking an Applied Science Manager to lead a team of applied scientists and analysts building the data and machine learning capabilities behind AWS Startups. You will own the science roadmap end-to-end, from the data foundation that unifies signals about founders, startups, and their products, through a portfolio of machine learning models, to the surfaces that put insights in the hands of the teams and products that serve startups. You will balance hands-on technical leadership with people management, setting the technical bar for your team while developing their careers. Key job responsibilities · Lead, coach, and grow a team of applied scientists, business intelligence engineers, and business analysts; hire and develop talent and set a high technical bar. · Own and prioritize the team's science roadmap and set technical direction for its machine learning models and data assets, balancing rapid experimentation with production quality, cost, and reliability. · Scope scientific projects, design and evaluate experiments, and productionize models that deliver measurable impact, establishing measurement, evaluation, and operational-excellence standards so quality and impact are quantified and defensible. · Drive the science behind recommendation systems, startup segmentation and targeting, and fraud detection, delivering models that surface relevant opportunities, group and prioritize startups by need and fit, and protect the business from fraud and abuse. · Partner with product, engineering, design, and go-to-market teams to translate science into scalable products, and communicate strategy, results, and trade-offs clearly to technical and non-technical leaders. · Foster a culture of scientific rigor and rapid experimentation, and proactively identify and escalate risks with clear mitigation plans. About the team The AWS Startups team builds innovative products and platforms that support startup customers throughout their journey, from initial onboarding and credit programs to AI-powered guidance and scale solutions. Our portfolio serves hundreds of thousands of startup customers globally, and we partner with business development, field marketing, and solutions architecture teams worldwide. We are building the next generation of AI-native products that make world-class cloud expertise accessible to every founder.
Applied Scientist, Pricing Science
US, WA, Seattle
Pricing is one of the most consequential decisions Amazon makes — and the science behind it needs to be causally rigorous, not just predictive. The P2 Optimization Science (P2OS) team builds the machine learning systems that power Amazon's pricing decisions at scale: demand lift models, customer lifetime value frameworks, and the experimentation infrastructure that validates whether our pricing changes actually work. We're hiring an Applied Scientist to own causal inference at the intersection of ML and pricing experimentation. This role exists because our team has identified a real gap: the methodological bridge between econometric analysis (owned by our economists) and production-scale ML pipelines (owned by our engineers) needs a practitioner who lives in both worlds. You'll build CATE estimation models, design analysis workflows for pricing weblabs, and develop the reusable causal ML infrastructure that the broader team — including non-ML scientists — can rely on. This is not a research role. The bias here is toward shipping production-quality causal pipelines with real downstream business impact. You'll measure success by what changes in LTV estimates, what pricing errors your models help avoid, and whether the economists on your team can actually use what you build. If you're a scientist who wants to work on hard causal identification problems in a high-stakes production environment — and who finds satisfaction in making rigorous methods accessible to a broader team — this role is for you. Key job responsibilities * Build causal ML pipelines for pricing — Design, train, evaluate, and deploy end-to-end causal estimation models for pricing use cases. * Own the science on heterogeneous treatment effects — Be the team SME on causal ML methodology: identification strategies, model selection, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation. * Support pricing experiment analysis — Contribute causal analysis methodology to pricing weblab and A/B test post-analysis; build reusable tooling that economists can use without requiring ML expertise * Connect model outputs to business outcomes — Define, before writing code, what business metric each model moves; deliver model evaluation reports framed around pricing errors avoided and LTV estimate changes. * Evaluate and adopt novel techniques — Assess applicability of emerging causal inference methods (synthetic DiD, generalized random forests, causal representation learning) to Amazon's pricing context; write internal methodology proposals for adoption * Write internal documentation and methodology papers — Produce at least one internal write-up per half that connects a causal ML technique to a concrete pricing use case; make pipelines extensible and well-documented so other scientists can build on them. * Collaborate across disciplines — Partner closely with the Sr. Economist on identification strategy and causal assumptions; work with SDE and DE partners on production deployment; align with PMs on experiment design requirements A day in the life As an Applied Scientist on the P2OS team, your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given workweek, you might: * Investigate an optimization anomaly in simulation and trace it back to a model input gap or an unmodeled market dynamic * Design an offline evaluation framework to benchmark competing optimization approaches before committing to online testing * Collaborate with Sr. Economists on the identification strategy for the model you're building for a pricing lab * Present a science proposal for incorporating a new competitiveness or inventory signal into an optimization system * Work cross-team with the experimentation platform team on randomization design. * Develop and write up a novel scientific finding — preparing a paper or technical report for submission to a top-tier venue such as KDD, NeurIPS, or the ACM Conference on Economics and Computation
Applied Scientist, Fauna
US, CA, Sunnyvale
We are seeking an Applied Scientist to focus on Robot Navigation. In this role, you'll research and develop advanced navigation systems that enable robots to move reliably and safely through complex, dynamic environments. You'll work across a broad spectrum of navigation approaches—from classical methods to learning-based techniques and foundation models—to build robust solutions for autonomous robot navigation. Key job responsibilities - Develop and implement robust navigation systems that enable reliable autonomous operation in complex, dynamic indoor environments with static and dynamic obstacles - Build simulation-based and on-device evaluation frameworks with comprehensive benchmarks and metrics for systematic comparison of navigation methods - Conduct sim-to-real transfer experiments, analyzing performance gaps and developing techniques to ensure reliable real-world navigation performance - Collaborate with world model, manipulation, and other teams to ensure seamless integration of navigation capabilities into the full robot system - Stay current with the latest advances in robot navigation, spatial reasoning, and related fields, and apply relevant findings to improve system performance - Mentor fellow scientists and engineers while maintaining strong individual technical contributions About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products.
Applied Scientist II, Text-to-Speech on Device team
PL, Gdansk
Have you ever wondered how we give voice to devices — even when they're offline? The Text-to-Speech on Device team at Amazon builds AI-powered voice models that run locally on hardware with limited resources, serving customers across Alexa, automotive, and accessibility experiences for visually impaired users. We sit at the intersection of speech generation, generative AI, and on-device machine learning, and we're looking for a curious, collaborative Applied Scientist to help us push what's possible. In this role, you will research and develop production-ready speech generation models optimized for constrained environments. You will work across the full model lifecycle — from early experimentation and prototyping through to integration on real devices. If you're excited about solving hard scientific problems that directly improve how millions of people interact with technology, we'd love to hear from you. Key job responsibilities - Design and develop end-to-end machine learning models for on-device speech generation, from early research and experimentation through production-ready deployment. - Research and apply advanced techniques in generative AI, model compression, and knowledge distillation to deliver high-quality voice models within tight hardware constraints. - Propose and validate novel scientific approaches by authoring detailed technical specifications and contributing to peer-reviewed publications when appropriate. - Evaluate model performance rigorously, identify improvement opportunities, and iterate on training and inference pipelines to optimize quality and efficiency. - Collaborate with science and engineering teams across cloud and device platforms to bring speech generation capabilities from research prototypes to integrated product experiences. About the team The Text-to-Speech on Device team builds low-footprint AI models for speech generation that run locally on devices such as Android and FireOS platforms. Our models require significantly less computation than cloud-hosted alternatives, enabling offline voice experiences for Alexa, automotive partners, and accessibility solutions. We work closely with device engineering teams and cloud-based speech science teams to deliver the best possible experience for our customers. Our focus in the coming years is expanding the range of voices and languages we support while continuing to improve naturalness and efficiency on constrained hardware.
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