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Amazon Prime is looking for an ambitious Economist to help create econometric insights for world-wide Prime. Prime is Amazon's premiere membership program, with over 200M members world-wide. This role is at the center of many major company decisions that impact Amazon's customers. These decisions span a variety of industries, each reflecting the diversity of Prime benefits. These range from fast-free e-commerce shipping, digital content (e.g., exclusive streaming video, music, gaming, photos), and grocery offerings. Prime Science creates insights that power these decisions. As an economist in this role, you will create statistical tools that embed causal interpretations. You will utilize massive data, state-of-the-art scientific computing, econometrics (causal, counterfactual/structural, time-series forecasting, experimentation), and machine-learning, to do so. Some of the science you create will be publishable in internal or external scientific journals and conferences. You will work closely with a team of economists, applied scientists, data professionals (business analysts, business intelligence engineers), product managers, and software engineers. You will create insights from descriptive statistics, as well as from novel statistical and econometric models. You will create internal-to-Amazon-facing automated scientific data products to power company decisions. You will write strategic documents explaining how senior company leaders should utilize these insights to create sustainable value for customers. These leaders will often include the senior-most leaders at Amazon. The team is unique in its exposure to company-wide strategies as well as senior leadership. It operates at the research frontier of utilizing data, econometrics, artificial intelligence, and machine-learning to form business strategies. A successful candidate will have demonstrated a capacity for building, estimating, and defending statistical models (e.g., causal, counterfactual, time-series, machine-learning) using software such as R, Python, or STATA. They will have a willingness to learn and apply a broad set of statistical and computational techniques to supplement deep-training in one area of econometrics. For example, many applications on the team use structural econometrics, machine-learning, and time-series forecasting. They rely on building scalable production software, which involves a broad set of world-class software-building skills often learned on-the-job. As a consequence, already-obtained knowledge of SQL, machine learning, and large-scale scientific computing using distributed computing infrastructures such as Spark-Scala or PySpark would be a plus. Additionally, this candidate will show a track-record of delivering projects well and on-time, preferably in collaboration with other team members (e.g. co-authors). Candidates must have very strong writing and emotional intelligence skills (for collaborative teamwork, often with colleagues in different functional roles), a growth mindset, and a capacity for dealing with a high-level of ambiguity. Endowed with these traits and on-the-job-growth, the role will provide the opportunity to have a large strategic, world-wide impact on the customer experiences of Prime members.

RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As an Applied Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will independently file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Applied Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.

Join the next science and engineering revolution at Amazon's Delivery Foundation Model team, where you'll work alongside world-class scientists and engineers to pioneer the next frontier of logistics through advanced AI and foundation models. We are seeking an exceptional Applied Scientist to help develop innovative foundation models that enable delivery of billions of packages worldwide. In this role, you'll combine highly technical work with scientific leadership, ensuring the team delivers robust solutions for dynamic real-world environments. Your team will leverage Amazon's vast data and computational resources to tackle ambitious problems across a diverse set of Amazon delivery use cases. Key job responsibilities - Design and implement novel deep learning architectures combining a multitude of modalities, including image, video, and geospatial data. - Solve computational problems to train foundation models on vast amounts of Amazon data and infer at Amazon scale, taking advantage of latest developments in hardware and deep learning libraries. - As a foundation model developer, collaborate with multiple science and engineering teams to help build adaptations that power use cases across Amazon Last Mile deliveries, improving experience and safety of a delivery driver, an Amazon customer, and improving efficiency of Amazon delivery network. - Drive technical direction for specific research initiatives, ensuring robust performance in production environments. A day in the life As a member of the Delivery Foundation Model team, you’ll spend your day on the following: - Develop and implement novel foundation model architectures, working hands-on with data and our extensive training and evaluation infrastructure - Collaborate with fellow scientists in solving complex technical challenges, from trajectory planning to efficient multi-task learning - Collaborate with fellow engineers in building scalable and reusable infra to support model training, evaluation, and inference - Contribute to focused technical initiatives from conception through deployment, ensuring successful integration with production systems - Drive technical discussions within the team and and key stakeholders - Conduct experiments and prototype new ideas - Make significant hands-on contribution to technical solutions About the team The Delivery Foundation Model team combines ambitious research vision with real-world impact. Our foundation models provide generative reasoning capabilities required to meet the demands of Amazon's global Last Mile delivery network. We leverage Amazon's unparalleled computational infrastructure and extensive datasets to deploy state-of-the-art foundation models to improve the safety, quality, and efficiency of Amazon deliveries. Our work spans the full spectrum of foundation model development, from multimodal training using images, videos, and sensor data, to sophisticated modeling strategies that can handle diverse real-world scenarios. We build everything end to end, from data preparation to model training and evaluation to inference, along with all the tooling needed to understand and analyze model performance. Join us if you're excited about pushing the boundaries of what's possible in logistics, working with world-class scientists and engineers, and seeing your innovations deployed at unprecedented scale.

Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply innovative Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, define paths to navigate technical or business challenges, develop proof-of- concepts, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for Applied Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. Key job responsibilities As an Applied Scientist, you will: Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate cutting-edge generative AI solutions to address real-world challenges Interact with customers directly to understand their business problems, aid them in implementation of generative AI solutions, brief customers and guide them on adoption patterns and paths to production Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder Provide customer and market feedback to product and engineering teams to help define product direction A day in the life This is a customer facing role. You will be required to travel to client locations and deliver professional services as needed. About the team Sales, Marketing and Global Services (SMGS) 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 the 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. The Professional Services team is part of Global Services. About AWS 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 empower us to be proud of our differences. 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.

Amazon Industrial Robotics is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Enable unprecedented robustness and reliability, industry-ready - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities As an Applied Science Manager in the Foundations Model team, you will: - Build and lead a team of scientists and developers responsible for foundation model development - Define the right ‘FM recipe’ to reach industry ready solutions - Define the right strategy to ensure fast and efficient development, combining state of the art methods, research and engineering. - Lead Model Development and Training: Designing and implementing the model architectures, training and fine tuning the foundation models using various datasets, and optimize the model performance through iterative experiments - Lead Data Management: Process and prepare training data, including data governance, provenance tracking, data quality checks and creating reusable data pipelines. - Lead Experimentation and Validation: Design and execute experiments to test model capabilities on the simulator and on the embodiment, validate performance across different scenarios, create a baseline and iteratively improve model performance. - Lead Code Development: Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Research: Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Collaboration: Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies.

We're seeking an Applied Scientist to lead and innovate in applying advanced AI technologies that will reshape how businesses sell on Amazon. Our team is passionate about leveraging Machine Learning, GenAI, and Agentic AI to help B2B sellers optimize their operations and drive growth. Join Amazon Business 3P (Third Party - Sellers) - a rapidly growing global organization where we innovate at the intersection of AI technology and B2B commerce. We're reimagining how sellers reach and serve business customers, creating intelligent solutions that help them grow their B2B business on Amazon. From AI-powered Seller Central tools to smart business certifications, dynamic pricing capabilities, and advanced analytics, we're transforming how B2B selling happens. As an Applied Scientist II on our AB 3P Tech team, you'll drive the development and implementation of state-of-the-art algorithms and models for supervised fine-tuning and reinforcement learning. You'll work with highly technical, entrepreneurial teams to: - Design and implement AI models that power the B2B selling experience - Lead the development of GenAI products that can handle Amazon-scale use cases - Drive research and implementation of advanced algorithms for human feedback and complex reasoning - Make strategic AI technology decisions and mentor technical talent - Own critical AI systems spanning from Seller Central to Amazon Business detail pages Join us in shaping the future of B2B selling - we're building applied AI solutions that businesses love and trust for their day-to-day success. If you are scrappy and bias for action is your favorite Leadership Principle, you'll fit right in as we innovate across the seller experience to create significant impact in this fast-growing business. Key job responsibilities Key job responsibilities: - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in Gen AI - Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results - Think big about the arc of development of Gen AI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences About the team At Amazon Business Third Party (AB3P) Tech, we're revolutionizing B2B e-commerce by empowering sellers in the business marketplace. Our scope spans the complete B2B selling journey, from Seller Central to Amazon Business detail pages, cart, and checkout for merchant-fulfilled offers. Our entrepreneurial culture and global reach define us. We develop features across seller experience, delivery, certifications, fees, registration, and analytics, collaborating with worldwide teams and leveraging advanced AI technologies to continuously innovate. Working in true Day 1 spirit, we build next-generation solutions that shape the future of B2B commerce. Join us in building next-generation solutions that shape the future of B2B commerce.

Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. Starting in 2024, the Innovation Center launched a new Custom Model and Optimization program to help customers develop and scale highly customized generative AI solutions. The team helps customers imagine and scope bespoke use cases that will create the greatest value for their businesses, define paths to navigate technical or business challenges, develop and optimize models to power their solutions, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for Applied Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. Key job responsibilities • Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate generative AI solutions to address real-world challenges • Interact with customers directly to understand their business problems, aid them in implementation of generative AI solutions, brief customers and guide them on adoption patterns and paths to production • Help customers optimize their solutions through approaches such as model selection, training or tuning, right-sizing, distillation, and hardware optimization • Provide customer and market feedback to product and engineering teams to help define product direction

Amazon Autonomy is a research lab based in San Francisco with the charter to develop state of the art browser use agents at scale. Our research leverages Large Language Models (LLMS) and reinforcement learning (RL) algorithm at scale and benefits from the ability to work Amazon’s scale and a startup’s agility. As a Member of Technical staff (MTS) on the gym pod, you’ll be responsible for helping develop the training environments, tasks, and integrations needed to scale our data and drive core model capabilities. You’ll work closely with our researchers and given the autonomy to drive significant outcomes. This role is for you if you have a learner’s mindset, enjoy building at the frontier of what’s possible, and thrive in a collaborative fast-paced environment. Key job responsibilities Core Engineering: Architect and deliver robust software solutions throughout the development lifecycle Design and implement applications using modern TypeScript and Python Build and deploy cloud-native applications and RESTful APIs Engineer and optimize high-performance systems at scale Collaboration & Leadership: Collaborate with cross-functional stakeholders to deliver aligned features Conduct code reviews and provide constructive feedback Mentor junior engineering staff Maintain technical documentation and architectural decision records Preferred: Assume technical leadership and guide architectural decisions Manage PostgreSQL (or equivalent) databases in production Implement observability, monitoring, and testing frameworks Enhance product quality with focus on UX and DX Build evaluation frameworks to inform data-driven decisions Design and analyze A/B tests using statistical methodologies Contribute to machine learning initiatives and architect scalable AI pipelines

As part of grocery replenishment organization, Sr. Applied Scientists own inventory optimization, distribution optimization, and end-to-end modeling / simulation of Amazon grocery supply chain utilizing optimization and machine learning toolsets. We are looking for a talented and experienced applied scientist with a passion for designing and implementing elegant scientific solutions for Amazon-scale problems. Key job responsibilities - Design and develop advanced mathematical optimization models and apply them to define strategic and tactical needs and drive the appropriate business and technical solutions in the areas of inventory optimization, distribution optimization, network design, and control theory. - Apply mathematical optimization and control techniques (linear, quadratic, SOCP, robust, stochastic, dynamic, mixed-integer programming, network flows, nonlinear, nonconvex programming, decomposition methods, model predictive control) and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software. - Research, prototype, simulate, and experiment with these models using modeling languages such as Java, Python, MATLAB, Mosel or R; participate in the production level deployment. - Closely work with software engineering teams and write production well-tested Java code for science modules within engineering-managed services. Provide time-sensitive on-call support and high-severity issue support when bugs are identified in production code. Improve code quality of legacy scientific production code. - Create, enhance, and maintain technical documentation and science designs. - Present to other Scientists, Product, and Software Engineering teams, as well as Stakeholders. - Lead project plans from a scientific perspective by managing product features, technical risks, milestones and launch plans. - Influence organization's long-term roadmap and resourcing, onboard new technologies onto Science team's toolbox, mentor other Scientists. A day in the life - Engage with customers to understand their problems. - Collaborate with product partners and peers to design and deliver algorithmic solutions to these problems. - Implement these solutions in java within engineering systems through close collaboration with engineering partners achieving high code quality. - Deploy and measure impact of implementations. - Support customers and stakeholders whenever deep-dives and enhancements are needed as they relate to scientific products the team owns. - Contribute to product roadmap through new innovations on behalf of customers. - Publish work in internal and external scientific community. About the team SCOT IB GRO Science team is comprised of applied scientists with strong optimization & ML science depth and object-oriented programming & design patterns knowledge. Given the scale of problems we solve for our customers and mission-critical nature of our solutions, systems thinking driven approach, with attention to algorithmic complexity, solution quality, simplicity, and extensibility are of critical importance. We collaborate with engineering teams closely and prioritize solving problems with minimally complex solutions while maintaining quality. We build solutions that must consistently improve customer experience with maximum transparency and explainability of decisions made by such solutions. We strive for every member of the team to be knowledgeable about every product that the team owns to enable meaningful collaboration within the team. We seek to publish our work at internal and external scientific communities when they produce novel solutions.

RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As an Applied Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will independently as well as guide team to file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Applied Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.