Yu Zeng

Senior Applied Scientist, Real-Time Conversational AI , AGI

US, CA, Sunnyvale

We are looking for a Senior Applied Scientist to help drive the research and development of real-time multimodal conversational AI. You will contribute across two focus areas: advancing foundation models for speech and audio, and building the post-training systems (reward modeling, reinforcement learning) that shape natural, human-like conversational behavior. You will own a significant research area and contribute across the full model lifecycle — from pre-training and architecture design through post-training alignment and real-time deployment. You will work at the frontier of what’s possible in conversational AI, with the compute, data, and runway to pursue problems that few teams in the world have the resources to tackle. As a Senior Scientist, you will drive the technical execution of your research area, contribute to the team’s roadmap, and work closely with inference engineers to ensure your models are designed for real-time production deployment. Key job responsibilities What You’ll Do Foundation Model Scaling - Help build and train large-scale multimodal foundation models for real-time speech and audio generation, from architecture design through production-scale training - Advance the scaling and efficiency of conversational models, including the relationship between data, model size, and real-time performance - Design model architectures informed by hardware constraints and inference requirements, working with inference engineers to ensure models are servable from inception - Develop training methodologies for multimodal models that jointly process and generate speech, language, and audio in real-time streaming contexts - Contribute to the state of the art on efficient architectures and training methods for conversational AI at scale Post-Training & Reinforcement Learning - Design and build reward models and reward functions for speech systems — capturing naturalness, fluency, conversational quality, and real-time responsiveness - Develop and apply reinforcement learning methods to shape conversational behavior — teaching models natural timing, responsiveness, and fluid interaction - Build parts of the post-training pipeline from SFT through RL alignment, optimized for real-time multimodal outputs rather than text-only generation - Design evaluation frameworks that capture the quality dimensions unique to real-time conversation (latency sensitivity, audio quality, prosody, interaction naturalness) Real-Time Perception & Generation - Advance the team’s capabilities in real-time perception — the ability of the model to process incoming audio/speech while simultaneously generating responses - Develop techniques for natural interactive systems where the model handles concurrent input and output with human-like timing - Work at the intersection of model architecture and production constraints to ensure multimodal capabilities function within hard real-time latency budgets

Sr. Comm Systems Engineer, Wireless Systems

US, TX, Austin

Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Systems Engineer, this role is primarily responsible for the design, development and integration of communication payload and customer terminal systems. The Role: Be part of the team defining the overall communication system and architecture of Amazon Leo’s broadband wireless network. This is a unique opportunity to innovate and define groundbreaking wireless technology at global scale. The team develops and designs the communication system for project Leo and analyzes its overall system level performance such as for overall throughput, latency, system availability, packet loss etc. This role in particular will be responsible for leading the effort in designing and developing advanced technology and solutions for communication system. This role will also be responsible developing advanced physical layer + protocol stacks systems as proof of concept and reference implementation to improve the performance and reliability of the LEO network. In particular this role will be responsible for using concepts from digital signal processing, information theory, wireless communications to develop novel solutions for achieving ultra-high performance LEO network. This role will also be part of a team and develop simulation tools with particular emphasis on modeling the physical layer aspects such as advanced receiver modeling and abstraction, interference cancellation techniques, FEC abstraction models etc. This role will also play a critical role in the integration and verification of various HW and SW sub-systems as a part of system integration and link bring-up and verification. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.

Research Scientist II, Special Projects

US, WA, Seattle

Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best.

Applied Scientist, Prime Video - Generative AI

US, NY, New York

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video subscriptions such as Apple TV+, HBO Max, Peacock, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities • Build generative AI models that create production-ready content, including movie content, localized assets, and visual marketing materials used across Prime Video's global platform. • Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, complexity. • Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models. • Run experiments, gather data, and perform statistical analysis. • Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. • Research new and innovative machine learning approaches. • Share knowledge and research outcomes via internal and external conferences and journal publications A day in the life Prime Video is pioneering the use of Generative AI to empower the next generation of creatives. Our mission is to make world-class media creation accessible, scalable, and efficient. We are seeking an Applied Scientist to advance the state of the art in Generative AI and to deliver these innovations as production-ready systems at Amazon scale. Your work will give creators unprecedented freedom and control while driving new efficiencies across Prime Video’s global content and marketing pipelines. This is a newly formed team within Prime Video Science!

Applied Scientist II, IESP Digital Payments

BR, SP, Sao Paulo

Do you like working on projects that are highly visible and are tied closely to Amazon’s growth? Are you seeking an environment where you can drive innovation leveraging the scalability and innovation with Amazon's AWS cloud services? The Amazon IESP Digital Payments & Emerging Team is hiring Applied Scientists to work in our Machine Learning team in São Paulo, Brazil. The Payments team designs and builds scalable payment systems that power Amazon's checkout and financial services across emerging countries (Brazil, Mexico, Australia, Singapore, and more). We tackle complex challenges in payment orchestration, fraud detection, and regional payment method integration to drive conversion and deliver seamless experiences at scale. Key job responsibilities About you You want to make changes that help millions of customers. You don’t want to make something 10% better as a part of an enormous team. Rather, you want to innovate with a small community of passionate peers. You have experience in analytics, machine learning, LLMs and Agentic AI, and a desire to learn more about these subjects. You want a trusted role in strategy and product design. You put the customer first in your thinking. You have great problem solving skills. You research the latest data technologies and use them to help you innovate and keep costs low. You have great judgment and communication skills, and a history of delivering results. Your Responsibilities - Define and own complex machine learning solutions in the consumer space, including targeting, measurement, creative optimization, and multivariate testing. - Design, implement, and evolve Agentic AI systems that can autonomously perceive their environment, reason about context, and take actions across business workflows—while ensuring human-in-the-loop oversight for high-stakes decisions. - Influence the broader team's approach to integrating machine learning into business workflows. - Advise leadership, both tech and non-tech. - Support technical trade-offs between short-term needs and long-term goals.

Senior Applied Scientist, Special Projects

US, WA, Seattle

Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the limits. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. As a Senior Applied Scientist on our team, you will focus on building state-of-the-art ML models for healthcare. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the forefront of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams. This role offers a unique opportunity to work on projects that could fundamentally transform healthcare outcomes. Key job responsibilities In this role, you will: • Design and implement novel AI/ML solutions for complex healthcare challenges • Drive advancements in machine learning and data science • Balance theoretical knowledge with practical implementation • Work closely with customers and partners to understand their requirements • Navigate ambiguity and create clarity in early-stage product development • Collaborate with cross-functional teams while fostering innovation in a collaborative work environment to deliver impactful solutions • Establish best practices for ML experimentation, evaluation, development and deployment • Partner with leadership to define roadmap and strategic initiatives You’ll need a strong background in AI/ML, proven leadership skills, and the ability to translate complex concepts into actionable plans. You’ll also need to effectively translate research findings into practical solutions. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the Special Projects organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. About the team We represent Amazon's ambitious vision to solve the world's most pressing challenges. We are exploring new approaches to enhance research practices in the healthcare space, leveraging Amazon's scale and technological expertise. We operate with the agility of a startup while backed by Amazon's resources and operational excellence. We're looking for builders who are excited about working on ambitious, undefined problems and are comfortable with ambiguity.

Senior Data Scientist, Amazon Leo

US, WA, Bellevue

Amazon Leo is Amazon’s low Earth orbit satellite broadband network. Its mission is to deliver fast, reliable internet to customers and communities around the world, and we’ve designed the system with the capacity, flexibility, and performance to serve a wide range of customers, from individual households to schools, hospitals, businesses, government agencies, and other organizations operating in locations without reliable connectivity. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. As a Senior Data Scientist (DS), you will drive the development and implementation of advanced analytics and machine learning solutions. You will work on critical initiatives including natural language processing for non-conformance analysis, statistical process controls (SPC) for test optimization, and equipment predictive maintenance models to enable manufacturing rate acceleration. Your work will directly influence Kuiper’s production manufacturing workflow. You are an analytical problem solver who enjoys diving into data from various businesses, is excited about investigations and algorithms, can multi-task, and can credibly interface between scientists, engineers, and business stakeholders. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future. Key job responsibilities Lead the design and implementation of ML/LLM solutions to analyze manufacturing data and identify failure patterns and operational risks Design predictive models for statistical process control and equipment maintenance optimization Build production-ready ML pipelines leveraging AWS services (e.g., SageMaker, Bedrock, AWS Glue) Formalize assumptions about how models are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them Develop and test model enhancements, running computational experiments, and fine-tuning model parameters for new models Collaborate with engineering teams to translate complex manufacturing challenges into data-driven solutions Drive consensus on metrics and analysis approaches to support business strategy Write documents and create compelling visualizations and presentations to communicate insights to stakeholders Mentor team members and drive data science best practices across the organization About the team Established in 2023 as ProdOps was preparing to transition from development into full-rate production, OCC was purpose-built to ensure that as manufacturing scaled, the most critical problems would be identified, solved, and prevented from recurring through a single integrated operating system. OCC focuses on the most critical problems for ProdOps at each phase of production. The team operates as a closed-loop flywheel where each rotation identifies value, solves problems, implements change, and generates new data that feeds the next cycle. Every rotation compounds returns.

Applied Scientist - Reinforcement learning, OMHS SCS

US, MA, N.reading

As an Applied Scientist on the Science SW team, you will collaborate closely with other scientists and engineers to bring Reinforcement Learning (RL) research to production. This role combines the scientific application of ML, and specifically RL and sequential decision making, with software development engineering and a strong product focus. It will be your job to design, implement, and deploy novel RL agents, reward models, and control policies in both prototype and production environments, and to prove their impact in high-fidelity simulation before scaling them across the fleet. Key job responsibilities • Own the research and development of reinforcement learning and sequential decision making solutions spanning deep RL, policy optimization, offline/batch RL, contextual bandits, and multi-agent RL for real-time MHE control and building-wide optimization in a production environment. • Formulate fulfillment operations problems (throughput optimization, flow, merge, and congestion control) as sequential decision-making problems, and design multi-objective reward functions that balance competing operational objectives. • Build and leverage high-fidelity simulation environments for safe offline training, policy validation, and sim-to-real transfer before fleet-scale deployment. • Collaborate across multiple science and engineering teams to integrate RL policies into real-time production and control systems. About the team Amazon is building next generation software, hardware, and processes that will run our global network of fulfillment centers that move millions of units of inventory, and ensure customers get what they want when promised. The Science Software team in the One MHS organization unlocks Material Handling Equipment (MHE) innovation through a multiplicity of disciplines within Artificial Intelligence (AI) and applied science, including Computer Vision (CV), Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned experimentation, the team is dedicated to building self-optimizing fulfillment centers, developing the models that drive real-time, building-wide orchestration of MHE. We conduct experiments, develop models, and apply machine learning (ML) at scale to optimize throughput, flow, merge, and congestion control, and to improve operational performance across the fulfillment network.

Sr. Applied Scientist, Prime Video - Personalization and Discovery Science

US, CA, Sunnyvale

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities - Develop AI solutions for various Prime Video Search systems using Deep learning, GenAI, Reinforcement Learning, 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; - Publish your research findings in top conferences and journals. About the team Prime Video Search Science team owns science solution to power search experience on various devices, from sourcing, relevance, ranking, to name a few. We work closely with the engineering teams to launch our solutions in production.

Applied Scientist, Fauna

US, NY, New York

We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers

Yu Zeng

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