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Amazon Prime Video is looking for passionate, talented, and inventive Applied Scientists with a strong machine learning background to help build industry-leading Audio, Video and Language technology using GenAI. As part of the AI team in Amazon Prime Video, you will work on developing and fine-tuning LLMs and developing new large multimodal models to advance state-of-the-art in the translation, transcription and generation of multingual cinematic content. You will use the latest GenAI techniques to create and assess cinematic content like videos, audio, and captions, and more. You will design novel approaches to integrate speech, vision, and text for holistic understanding and generation of content, and Collaborate cross-functionally with product managers, engineers, and domain experts to define and deliver high-impact solutions. Your work will directly impact millions of our customers in the form of products and services that make use of speech and language technology on Prime Video content. Key job responsibilities We are looking to hire a top notch scientist with strong machine learning background, sharp research acumen and a hunger for solving ambiguous and challenging science problems. This person will work on developing and fine-tuning LLMs and developing new large multimodal models to advance state-of-the-art in the translation, transcription and generation of multingual cinematic content. They will be required to use the latest GenAI advancements to create and assess cinematic content like videos, audio, and captions, and more. Flavor of problems we are trying to solve: a) generating high quality subtitles in many languages meeting high customer satisfaction bar, b) developing foundation model capable of enabling many down stream tasks in localization and accessibility space, c) generating captions with plot pertinency, character identification etc. d) developing solution for assessing quality of subtitles, captions and metadata unified across different content types, linguistic vs non-linguistic aspects, different languages, and which mimics end user satisfaction while at the same time is explainable. About the team By 2027, Prime Video (PV) aspires to be the primary destination for customers to watch and the primary channel for partners to distribute premium cinematic content. The Content Reasoning, Enrichment & Localization (CoREL) science team’s mission is to develop AI to deeply understand the diverse facets of Prime Video's content across multiple languages, and to create content that power immersive, cinematic experiences. To drive this mission and delight our customers, the CoREL Science Bangalore team addresses the localization and accessibility aspects by a) understanding fine-grained information contained in content to enable generation of localized assets in different languages, b) generating subtitles and accessibility assets like captions across different content types and languages, c) assessing quality of generated (1P) and partner submitted (3P) assets and metadata to ensure defect free customer experience on PV.

If you are interested in this position, please apply on Twitch's Career site https://www.twitch.tv/jobs/en/ About Us: Twitch is the world’s biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It is where thousands of communities come together for whatever, every day. We’re about community, inside and out. You’ll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We’re on a quest to empower live communities, so if this sounds good to you, see what we’re up to on LinkedIn and X, and discover the projects we’re solving on our Blog. Be sure to explore our Interviewing Guide to learn how to ace our interview process. About the Role We are looking for an Applied Scientist to solve challenging and open-ended problems in the domain of Creators and content contextual signals. As an Applied Scientist on Twitch's GTM Science team, you will use applied machine learning to tackle ambiguous business problems across problem areas such as Ads, Brand Safety, Creator Sponsorship and Content Understanding. You will lead high profile projects where you will use a wide toolbox of ML tools to handle multiple types of data, including user behavior, metadata, and user generated content such as text and video. You will collaborate with a team of passionate scientists, engineers, and product stakeholders to develop these models, prototype and put them into production, where they can help Twitch's creators and viewers succeed and build communities. You will report to our Senior Data Science Manager for GTM. This position is located in San Francisco, CA. You Will - Build machine learning products to enrich Twitch’s content understanding and help creators and viewers build and discover their communities. - Propose, design, and oversee execution for high-impact projects that work backwards from customer problems to develop the right solution for the job. Know when to use a classical ML model versus a state-of-the-art one. - Collaborate with cross-functional engineering and product teams to prototype and deploy your models into flexible data pipelines and ML-based services. - Bring innovation and help drive science culture on the team by staying up to date and experimenting with new techniques in LLM, computer vision, inference and model evaluation. Perks * Medical, Dental, Vision & Disability Insurance * 401(k) * Maternity & Parental Leave * Flexible PTO * Amazon Employee Discount

We are looking for a talented Applied Scientist to join our team. In this role, you will design, develop, and deploy machine learning and computer vision models that solve real-world problems at scale in the Amazon grocery domain. You will work closely with engineering, product, and business teams to turn complex technical challenges into production-ready solutions, and own the model development lifecycle from experimentation through deployment. You will bring scientific rigor to every stage — from data analysis and model design to evaluation and iteration. This is a high-impact role where your models will directly improve the shopping experience for millions of customers in Amazon grocery stores. Key job responsibilities Design, train, and evaluate computer vision and machine learning models for complex grocery-domain problems including product identification, shelf perception, and in-store scene understanding — iterating rapidly from prototype to production-quality solutions Conduct rigorous exploratory data analysis to characterize domain-specific challenges (image variability, catalog gaps, label noise) and translate findings into actionable modeling decisions Own the model development lifecycle from experimentation through deployment — collaborating with software and ML engineers to ensure models meet latency, throughput, and reliability requirements at production scale Design and execute offline and online evaluation frameworks — defining metrics that capture both model performance and downstream business impact, and diagnosing failure modes to prioritize improvements Build and improve data pipelines and annotation workflows that feed model training, including active learning strategies to maximize label efficiency Communicate technical results, trade-offs, and recommendations clearly to engineering, product, and business stakeholders — connecting model behavior to customer experience outcomes Stay current with state-of-the-art research in computer vision, multimodal learning, and representation learning — evaluating and adapting promising techniques to team-specific problems Contribute to a culture of scientific rigor through reproducible experimentation, thorough documentation, peer code and design reviews, and raising the quality bar for the team A day in the life As an Applied Scientist on the GRAISE team, you'll spend your days analyzing model performance from overnight experiments, collaborating with engineers to deploy computer vision models to production, and prototyping new approaches using multimodal learning with store video and sensor data. You'll present findings to product and business stakeholders, translating technical results into actionable recommendations. Throughout the day, you'll balance rigorous scientific thinking with practical engineering constraints, knowing your work directly improves the shopping experience for millions of customers in Amazon grocery stores. About the team The GRAISE team (Grocery, Retail & In-Store Experience) within World Wide Grocery Store Tech (WWGST) builds foundational AI and machine learning systems that power Amazon's in-store grocery technologies. We develop domain-specific models that solve uniquely complex challenges in grocery — from smart shopping carts and inventory intelligence to personalization and store operations. Our mission is to create technology which makes grocery shopping more convenient, economical, personalized, and enjoyable for customers while empowering retailers with operational efficiency

The Worldwide Design Engineering (WWDE) organization delivers innovative, effective and efficient engineering solutions that continually improve our customers’ experience. WWDE optimizes designs throughout the entire Amazon value chain providing overall fulfillment solutions from order receipt to last mile delivery. We are seeking a Sr. Simulation Scientist to assist in designing and optimizing the fulfillment network concepts and process improvement solutions using discrete event simulations for our World Wide Design Engineering Team. Successful candidates will be technical expert and natural self-starter who have the drive to apply simulation and optimization tools to solve complex flow and buffer challenges during the development of next generation fulfillment solutions. The Simulation Scientist is expected to deep dive into complex problems and drive relentlessly towards innovative solutions working with cross functional teams. Be comfortable interfacing and influencing various functional teams and individuals at all levels of the organization in order to be successful. Lead strategic modelling and simulation projects related to system simulation, optimization, AI powered simulation, advanced simulation tools development, coding simulation algorithms to drive process design decisions. Key job responsibilities - You influence the scientific strategy across multiple teams in your business area. You support go/no-go decisions, build consensus, and assist leaders in making trade-offs. You proactively clarify ambiguous problems, scientific deficiencies, and where your team’s solutions may bottleneck innovation for other teams. - Lead the design, implementation, and delivery of the simulation science solutions to perform system of systems discrete event simulations for significantly complex operational processes that have a long-term impact on a product, business, or function using FlexSim, Demo 3D, AnyLogic or any other Discrete Event Simulation (DES) software packages, AI powered simulation tools, PLC/IPC Emulations, Robotic system simulation and emulation - Lead strategic system modelling and simulation projects using DES tools, software, PLC/IPC emulations, to drive process design decisions - Be an exemplary practitioner in simulation data science discipline to establish best practices and simplify problems to develop discrete event simulations faster with higher standards - Identify and tackle intrinsically hard process flow simulation problems (e.g., highly complex, ambiguous, undefined, with less existing structure, or having significant business risk or potential for significant impact - Deliver artifacts that set the standard in the organization for excellence, from process flow control algorithm design to validation to implementations to technical documents using simulations - Be a pragmatic problem solver by applying judgment and simulation experience to balance cross-organization trade-offs between competing interests and effectively influence, negotiate, and communicate with internal and external business partners, contractors and vendors for multiple simulation projects - Provide simulation data and measurements that influence the business strategy of an organization. Write effective white papers and artifacts while documenting your approach, simulation outcomes, recommendations, and arguments - Lead and actively participate in reviews of simulation data science solutions. You bring clarity to complexity, probe assumptions, illuminate pitfalls, and foster shared understanding within simulation data science discipline - Play a significant role in the career development of others, actively mentoring and educating the larger simulation data science community on trends, technologies, and best practices - Use advanced statistical /simulation tools and develop codes (python or another object oriented language) for research , simulation, and modeling algorithms - Lead and coordinate simulation efforts between internal teams and outside vendors to develop optimal solutions for the network, including equipment specification, material flow control logic, process design, and site layout - Deliver results according to project schedules and quality A day in the life The day-to-day activities include challenging and problem solving scenario with fun filled environment working with talented and friendly team members. The stakeholders includes Worldwide Design Engineering org verticals, Fulfillment Technology and Robotics team members. The team solve problems related to critical design automation of Material handling equipment and technology design solutions. Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team WWDE Simulation Team’s mission is to apply advanced simulation tools and techniques to drive process flow design, optimization, and improvement for the Amazon Fulfillment Network. We take ownership and pride to come up with simulation based solutions to drive the designs and earn the customer trust. We always strive to invent new ways to simulate and optimize the design and to invent design solutions. We help design and operation engineers to make simulation driven decisions by producing detailed math models and generating key performance metrics.

The role will be key to building the products and experiences that will be used by Proserve and Frontier AI organization to deliver business outcomes to our customers Are you excited about building products that help Proserve and Frontier AI teams understand customer needs, develop the right solutions, and deliver them at scale? The Frontier AI tooling team is looking for a Senior Applied Scientist to take a high-impact work across our sales and delivery team. Our work spans GenAI agents that answer sales questions in natural language, knowledge graphs, recommendation systems, synthetic controls, account prioritization models, helping proserve delivery consultant accelerate delivery leveraging AI tooling and more. A key part of our effort will be to built a world class context layer that will improve the quality , accuracy , reliability and security of our agents. As a Senior Applied Scientist, you will own ambiguous, high-value problems end to end, from framing the question with business leaders through to a deployed model that changes how the field operates. You will set technical direction for problems that don't yet have a clear approach, choose the right method rather than the familiar one, and raise the bar for the science across the team. You will partner directly with customers across AWS Sales to understand the challenges they face and deliver solutions they trust and adopt. You must be an expert in advanced quantitative and machine learning methods, and equally strong at synthesizing and communicating insights to audiences of varying technical sophistication. You will influence decisions well beyond your own projects, and you will help grow the scientists around you through mentorship and technical leadership. Key job responsibilities * Own the full lifecycle of complex science problems: problem framing, data exploration, method selection, modeling, evaluation, and production deployment. * Set technical direction on ambiguous problems and make the design decisions that others build on. * Partner with business stakeholders across Sales & Delivery consultants to turn open-ended business questions into well-scoped science, and translate results into recommendations leaders act on. * Raise the scientific bar across the team through design reviews, mentorship, and hands-on guidance. * Communicate methods, trade-offs, and results clearly to both technical and non-technical audiences. * Use Python, PySpark, and SQL for data analysis and model development. About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the 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. 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. 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 empowers us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (diversity, inclusion) conferences, inspire us to never stop embracing our uniqueness. Mentorship & 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. 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. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services.

Amazon Ads runs one of the largest real-time decision systems in the world. To meet advertiser goals, we process >4 million ad requests per second, and this team decides which of them are worth the compute. We mine a petabyte-scale stream of traffic to tell in-demand, valuable requests apart from wasteful ones, filtering out the noise and forwarding only what brings value to advertisers, all within milli-seconds. Our solutions save millions in compute costs and drive long-term growth for Amazon's Retail and Marketplace businesses. Why you'll love this role: The scientific challenge here is scale and signal: large-scale ML on enormous, high-velocity data, optimization and auction dynamics, and causal measurement of what actually moves the business. Generative AI and LLMs are tools you reach for when they help, not the point of the work. The impact also reaches beyond Amazon: we donated the Dynamic Traffic Engine (DTE) to the IAB Tech Lab as an open-source framework that lets demand-side platforms signal which bid requests they value, so supply-side platforms send fewer wasteful ones and QPS waste drops across the whole programmatic supply chain. We are a highly motivated, collaborative team with an entrepreneurial spirit and a broad mandate to experiment and innovate. You will work alongside talented engineers, product managers, and scientists in a culture that rewards bias for action, innovation, and experimentation. What makes this role unique is the combination of scientific rigor with real-world impact at a scale very few systems reach. Your impact and career growth: Amazon Ads is investing heavily in AI and ML, creating room for scientists to innovate and make their mark. As an Applied Scientist, you will own the science for well-defined supply-optimization problems and take your work end to end, from framing the approach through experimentation and into production with measured business impact. You will partner closely with engineers to productionize your models and with product managers to tie your science to customer and advertiser outcomes. You will share your findings, begin to mentor teammates, and grow your influence on the team's technical direction over time. This is your opportunity to do high-rigor applied science in one of the fastest-growing businesses at Amazon. Learn more about Amazon Ads: https://advertising.amazon.com/ Key job responsibilities As an Applied Scientist in Amazon Ads, you will: - Conduct hands-on data analysis and build large-scale ML models and pipelines for supply optimization. - Take your models end to end, from proof of concept through experimentation, optimization, and deployment into production, working closely with software engineers to productionize them and with product managers and fellow scientists along the way. - Design and run A/B experiments and causal analysis to validate hypotheses, draw insights from petabyte-scale data, and measure real business impact. - Research and apply advanced ML, including generative AI and large language models, to demand prediction and traffic filtering. - Drive end-to-end optimization projects that tackle ambiguous problems at massive scale, developing scalable, efficient processes for model development, validation, and deployment that optimize traffic monetization while maintaining customer experience.

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there, we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box, making complex things easy and hard things possible, with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction, and we are looking for builders who share that mindset. The Team Join the next science revolution at AWS Life Sciences Applied AI Solutions where you'll work alongside world-class scientists to build AI that transforms how therapeutics are discovered, developed, and brought to patients. We're out to revolutionize how medicines are discovered, developed, and brought to patients powered by a new generation of AI. Our team tackles some of the hardest open problems at the intersection of frontier AI and life sciences. We apply biological foundation models large language models and agentic reasoning systems to life sciences problems then put them into the hands of customers as applications and managed services they can fine-tune tailor and deploy on their own data. The science challenges are deep: how do you design agentic systems that reason correctly over complex biological regulatory and clinical logic? How do you enable customers to tailor foundation models to their proprietary data and get better outputs with less effort? How do you adapt models to reason faithfully in high-stakes scientific and regulatory domains? Today we're focused on two areas. In clinical trials we're building AI that automates and optimizes regulatory and clinical development workflows. In drug design our products (including Amazon Bio Discovery) accelerate discovery by giving bench scientists AI-guided protein engineering and antibody design capabilities. We combine frontier research with production-scale delivery to put breakthrough science into the hands of customers solving humanity's hardest problems. We value scientific rigor encourage publication and support conference participation. If you want to do research that ships this is the team. The Role We are seeking an Applied Scientist to build the models and methods behind our life sciences AI products with a primary focus on clinical trial operations and agentic reasoning. You will design train and evaluate systems that reason over complex clinical and operational logic and ship them into products customers use directly. You will work closely with senior and principal scientists on well-scoped research problems own your results end to end and see your work reach production. This role combines expertise in LLM reasoning and agentic AI with applied impact in life sciences. You will work on how large language models reason plan and act in complex scientific domains while applying domain knowledge to ensure models produce scientifically valid outputs. The problems span multiple fronts: • How do you build LLM-based agentic systems that correctly reason over clinical protocols regulatory standards and complex multi-step operational workflows? • How do you evaluate agent reliability and faithfulness rigorously enough to trust in high-stakes clinical settings? • How do you develop model customization methods (fine-tuning retrieval augmentation domain adaptation) that let customers get strong results from foundation models on their own data? You will focus on clinical trial operations (agentic automation structured reasoning evaluation domain adaptation) with opportunities to contribute across drug discovery (protein engineering antibody design) as the portfolio grows. You will own end-to-end scientific solutions from research through production and your work will directly shape the tools that scientists use daily. Key job responsibilities • Design train fine-tune and evaluate LLM-based agentic systems that reason over clinical protocols regulatory standards and operational workflows • Build rigorous evaluation harnesses and benchmarks to measure agent reliability faithfulness and failure modes in high-stakes domains • Develop model customization methods (fine-tuning RLHF retrieval augmentation domain adaptation) that help customers get better outputs on their own data with less effort • Contribute to graph-based and causal modeling approaches for clinical trial operations • Partner with Life Sciences domain experts product and engineering to translate scientific challenges into shipped capabilities • Own experiments end to end: problem framing implementation evaluation iteration and hand-off to production • Publish at top-tier venues where the work supports it • Contribute to drug discovery efforts (protein engineering antibody design) as opportunities arise A day in the life • Design and run an experiment to validate a new agentic reasoning or fine-tuning method then ship it as a capability customers can use • Diagnose why a model is failing on a new class of inputs and implement a fix to unblock a delivery milestone • Build or extend an evaluation benchmark to measure how faithfully an agent reasons over clinical logic • Meet with domain experts to scope what the next model release needs to do • Review results with a senior scientist sharpen the approach and get it over the finish line • Prototype a new idea that could become the next capability in the product About the team 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. 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. We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud. 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 and AmazeCon conferences, inspire us to never stop embracing our uniqueness. 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.

We are seeking an Applied Scientist to join Compass. In this role, you will develop the core Control Barrier Function (CBF) algorithms that form the mathematical foundation of the Compass safety system. You will ensure they don't just work in theory but perform reliably on real robots under real-world conditions. You will push the boundaries of concepts central to CBFs: computing robust invariant sets, designing hybrid system formulations that handle contact transitions and mode switches, and developing backup-set approaches that leverage learned policies and multiple controllers. A key challenge of this role is bridging the gap between the mathematics of set invariance and the realities of hardware, including sensor noise, model uncertainty, computational budgets, and discrete state transitions. You will ensure that these algorithms are not only provably correct but also implementable within a safety-critical architecture that must be certified by a third-party. You will contribute directly to the next generation of CBF theory and its practical deployment across Amazon's diverse robot fleet. Key job responsibilities • Develop and implement novel CBF algorithms that provide formal safety guarantees while minimizing conservatism to maximize the permissible operating envelope for each robot platform • Compute and refine invariant sets for complex, high-dimensional robotic systems, developing scalable methods that go beyond what existing analytical approaches can handle • Design formulations for hybrid dynamical systems, handling discrete mode transitions (e.g., contact/no-contact, stance/flight phases) with provable safety across switching boundaries • Address the theory-to-practice gap by developing methods that are robust to model uncertainty, sensor noise, actuation delays, and computational latency • Create reduced-order and full-order dynamics models with both white-box and black-box approach • Implement real-time optimization solvers that execute within the tight timing budgets of safety-critical control loops • Develop formal arguments and documentation sufficient to support third-party safety certification of algorithms • Validate algorithms through rigorous simulation and hardware experiments, characterizing failure modes and quantifying safety margins • Contribute to the theoretical foundations of CBFs through publications at top-tier controls and robotics venues • Collaborate with perception, planning, locomotion, and manipulation teams to accommodate the needs of upstream and downstream systems A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team Work with the inventors of control barrier functions on a novel, universal approach to safe autonomy: one that scales across mobile robots, manipulators, mobile manipulators, and future robot platforms with dynamic stability. You'll push the boundary of safe performance by integrating safety with motion planning, RL, and foundation models, ensuring that safety is never a blocker to robot performance. Your work will underpin robots operating alongside people at Amazon's unprecendented scale.

Robots are about to step out of the movies and into our homes. If you're excited about this robotics revolution, come join Amazon's Fauna Robotics. As an Applied Scientist in this org, you'll push the state-of-the-art techniques in Robotics. You'll drive technical excellence in areas such as perception, manipulation, sim2real transfer, reinforcement learning and multi-task learning, designing novel algorithms that bridge the gap between research and real-world deployment. In this role, you will integrate hands-on technical expertise with scientific leadership, ensuring your team delivers robust solutions for dynamic real-world environments. While staying updated with the latest advancements in robotics research, you will leverage your experience and expertise to guide the team towards the most promising direction. Collaborating with a talented group of software engineers and scientists, you will bring your ideas to fruition. Deploying robots in home environments requires you to anticipate challenges not encountered in structured settings like warehouses. Additionally, consumer-grade sensors and actuators necessitate working around their limitations, requiring out-of-the-box innovative ideas. As such, you are someone who truly enjoys thinking about problems using first principles at a holistic and systems level to address the aforementioned challenges. Key job responsibilities - Lead technical initiatives in areas such as robotics foundation models, reinforcement learning and manipulation - - Design experiments to identify the limitations of current state-of-the-art models and developing new models or techniques that can surpass these models - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Mentor fellow scientists while maintaining strong individual technical contributions - Collaborate with engineering teams to optimize and scale models for real-world applications - Influence technical decisions and implementation strategies within your area of focus - Experienced in communicating complex technical work to a non-technical audience - The ability to work with minimal guidance, be proactive and to handle ambiguity and the challenge of quickly evolving goals A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Guide fellow scientists in solving complex technical challenges, from sim2real transfer to training RL policies - Mentor team members while maintaining significant hands-on contribution to technical solutions 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. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.

At Amazon, we’re inventing on behalf of customers, and with Amazon Leo, we’re redefining what global connectivity looks like. Our mission is to deliver fast, affordable broadband to unserved and underserved communities around the world through a constellation of low Earth orbit (LEO) satellites. Every system we build helps connect people to education, healthcare, opportunity, and each other. As a Data Scientist, you will be responsible for developing advanced analytics and machine learning solutions for user terminals. You will develop predictive models to proactively identify possible user terminal failures in the field. You will work in a collaborative environment with a multi-disciplinary team, including constellation, RF, antenna, silicon, algorithm, and software engineers. Key job responsibilities As a Data Scientist, you will develop analytic tools for a team developing current and future user terminals. Your responsibilities include: • Develop statistical and analytical tool to enable the regression decision from on-orbit and lab measurement of user terminals • Publish documents and create compelling visualizations and presentations to communicate insights to stakeholders • Create and manage datasets for continued pre-training and supervised fine-tuning of LLMs • Develop scalable visualizations for analysis of user terminal performance • Work closely with constellation, RF, antenna, silicon, algorithm, and software engineers to root-cause the failures using data as the primary tool • Drive consensus on metrics and analysis approaches to support product development strategy 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. A day in the life As a Data Scientist in the LEO Customer Terminal Team, you will work daily with satellite constellation, algorithm, RF, antenna, silicon, hardware, and software teams in a collaborative environment. Your focus will be using data as an intelligence source to enable design decisions for the team. About the team The LEO Customer Terminal team is responsible for developing both outdoor and indoor devices that enable customers to access internet service via the LEO satellite network. We own the entire process from early prototypes through mass production, including requirements documentation, architecture definition, hardware development, algorithm development, and all integration and verification testing.