Careers

Profit Intelligence (PI) team in Amazon , owning Amazon's core profitability prediction system, is looking for an ambitious Applied Scientist to build the next generation of profitability science for Amazon. As an AS II in PI, you will partner closely with science leadership, business stakeholders, applied scientists, economists, and engineers to solve key business problems that are at the core of economic prediction and cost optimization for Amazon, empowering downstream teams to make high-velocity, high-quality profitability decisions for both short term and long term. You will utilize massive data, state-of-the-art scientific computing, econometrics, time-series forecasting, and machine-learning, etc. 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, business analysts, data engineers, product managers, and software engineers. The team is unique in its exposure to company-wide strategies, and operates at the research frontier of utilizing data, machine-learning, artificial intelligence to inform business profitability strategies. Key job responsibilities 1) Design, implement, test, deploy and maintain innovative data and machine learning solutions to accelerate our business. Candidate should show a tracking record of delivering projects well and on-time, preferably in collaboration with other team members (e.g. co-authors). 2) Experiments and prototype implementations of new machine learning algorithms and prediction techniques, with a capacity for dealing with a high-level of ambiguity. 3) Partners with scientists, engineers, product managers, and stockholders to use data and model driven approach to influence business decisions 4) Influence the team to adopt cutting edge technologies and machine learning best practices to ensure a high standard of quality for all of the team deliverables

We are embarking on a multi-year journey to improve the shopping experience for customers globally. Amazon Search team creates customer-focused search solutions and technologies that makes shopping delightful and effortless for our customers. Our goal is to understand what customers are looking for in whatever language happens to be their choice at the moment and help them find what they need in Amazon's vast catalog of billions of products. As Amazon expands to new geographies and new interfaces, we are faced with the unique challenge of maintaining the bar on Search Quality due to the diversity in user preferences, multilingual search and data scarcity. We are looking for an applied researcher to work on improving search on Amazon using NLP, ML, and DL technology. As an Applied Scientist, you will lead our efforts in query understanding, semantic matching, and ranking. As part of this role, you will develop high precision, high recall, and low latency solutions for search. Your solutions should work for all languages that Amazon supports and will be used in all Amazon locales world-wide. You will develop scalable science and engineering solutions that work successfully in production. You will work with leaders to develop a strategic vision and long term plans to improve search globally. We are growing our collaborative group of engineers and applied scientists by expanding into new areas.

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. 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 - 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. As an Applied Scientist, you will develop and improve machine learning systems that help robots perceive, reason, and act in real-world environments. You will leverage state-of-the-art models (open source and internal research), evaluate them on representative tasks, and adapt/optimize them to meet robustness, safety, and performance needs. You will invent new algorithms where gaps exist. You’ll collaborate closely with research, controls, hardware, and product-facing teams, and your outputs will be used by downstream teams to further customize and deploy on specific robot embodiments. Key job responsibilities - Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization. - Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments. - Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes. - Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes. - Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - 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. - 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 are embarking on a multi-year journey to improve the shopping experience for customers globally. Amazon Search team creates customer-focused search solutions and technologies that makes shopping delightful and effortless for our customers. Our goal is to understand what customers are looking for in whatever language happens to be their choice at the moment and help them find what they need in Amazon's vast catalog of billions of products. As Amazon expands to new geographies and new interfaces, we are faced with the unique challenge of maintaining the bar on Search Quality due to the diversity in user preferences, multilingual search and data scarcity. We are looking for an applied researcher to work on improving search on Amazon using NLP, ML, and DL technology. As an Applied Scientist, you will lead our efforts in query understanding, semantic matching, and ranking. As part of this role, you will develop high precision, high recall, and low latency solutions for search. Your solutions should work for all languages that Amazon supports and will be used in all Amazon locales world-wide. You will develop scalable science and engineering solutions that work successfully in production. You will work with leaders to develop a strategic vision and long term plans to improve search globally. We are growing our collaborative group of engineers and applied scientists by expanding into new areas.

We are seeking a talented, customer-focused applied scientist to join our JCI Measurement and Optimization Science Team (JCI MOST), with a charter to build scalable systems that automatically detect pricing defects, implement intelligent corrections, measure intervention impacts, and deliver data-driven pricing strategies to leadership. This role requires an individual with exceptional machine learning, LLM, and Causal Inference expertise, strong system architecture capabilities, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit to drive measurable improvements in pricing quality and competitiveness. We are looking for an experienced innovator who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and thrives in a fast-paced, data-driven environment. Key job responsibilities Key Job Responsibilities • Build scalable defect detection systems that automatically identify pricing anomalies, competitive gaps, and quality issues across millions of products using ML and LLM models and real-time monitoring • Deploy automated defect remediation with intelligent pricing recommendations, and validation frameworks that reduce manual intervention requirements • Measure impact and drive strategy by establishing robust measurement frameworks, designing large-scale experiments, building attribution models, and developing executive dashboards that translate findings into actionable insights for leadership • Lead cross-functional collaboration by partnering with product, engineering, and science teams to deploy solutions at scale while communicating complex technical concepts clearly to executive audiences • Stay at the forefront of innovation by applying state-of-the-art techniques in ML, deep learning, LLM, and causal inference to pricing quality challenges while fostering rapid experimentation and continuous learning

Amazon is looking for a motivated individual with strong statistical, analytical skills and technological experience to join the DIGI (Data Infrastructure and Generative Intelligence) Ad Sales Finance analytics team. In this position the successful candidate will be responsible for partnering with Finance and Business leaders to optimize Sales Forecasts Key job responsibilities - Build and train models to support forecasting and planning for Advertising Sales Finance - Have strong technical experience, but also be able to work with non-tech partners and communicate complex and technical topics in a simple and understandable fashion - Have a good understanding of machine learning or statistical modeling techniques, including a strong understanding of model parameters and how they affect performance - Understand time-series forecasting techniques (e.g., STL decomposition, ETS/Holt-Winters, ARIMA, Prophet, or similar models) - Familiarity with hierarchical or segmented forecasting problems (e.g., product, region, channel, or customer-level splits) - Apply theoretical or statistical models in an applied, real-world environment - Perform model evaluation such as confidence intervals, error metrics, backtesting, and validation datasets - Work with large, complex datasets across multiple dimensions - Translate analytical findings into clear, actionable insights for business stakeholders A day in the life In this position the successful candidate will be responsible for partnering with Finance and Business leaders to expand and optimize forecasting models that supports weekly, monthly, quarterly and annual reviews for the Display Ads Finance group and our stakeholders. About the team The Advertising Sales Finance Analytics & FP&A team's responsibilities comprise of corporate reporting, planning, Headcount & OpEx, Goals reporting, and ad-hoc analysis. We support Advertising leaders and finance teams by coordinating and consolidating deliverables, centralizing and standardizing processes, establishing financial controls and mechanisms, building tools that improve the speed of decision making, and providing insightful financial analysis on the short and long term strategy of Advertising.

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.

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). 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 visual), supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, product similarity, using GenAI, LLMs, NLP and Computer Vision. Key job responsibilities As an Applied Science Manager, 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 Lead scientists on the team and oversee research and development projects at various stages ranging from initial exploration to deployment into production systems. 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 create the environment in the team to file for patents and/or publish research work where opportunities arise. You will impact the large product strategy, identifies new business opportunities and provides strategic direction to the team.

As part of the AWS Applied AI Solutions organization, we have a mission to build delightful AI solutions that improve human capabilities and business outcomes. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated AI solutions that solve enduring business challenges. We blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. The Applied Scientist will contribute to the development of Agentic AI solutions leveraging Gen AI. The role requires extending existing scientific techniques and inventing new ones to address specific customer needs. You should be comfortable working semi-autonomously on difficult problems with visible risks or roadblocks. You'll work closely with technical leaders within the team. We're looking for scientists who can maintain high standards while moving quickly, prioritizing both rapid experimentation and responsible AI development to deliver measurable customer impact. Key job responsibilities * Design and implement solutions that extend or adapt scientific approaches for customer needs * Deliver components into production that meet high quality standards (efficient, reproducible, testable code) * Produce science outputs demonstrating correctness, scholarship, and scientific rigor * Work with product, engineering, and science teams to deliver impactful AI features * Contribute to operational excellence in the team's deliverables About the team AWS Solutions As part of the AWS solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use.

Amazon’s Middle Mile Science group is looking for a Senior Applied Scientist specializing in design and evaluation of algorithms for predictive modeling and optimization applied to large-scale transportation planning systems. This includes the development of novel machine learning, pattern detection, and artificial intelligence techniques to improve on marketplace optimization solutions. The Middle Mile Science group is charged with developing an evolving suite of optimization tools to facilitate the design of efficient air and ground transport networks, optimize the flow of packages within the network to efficiently align network capacity and shipment demand, set prices, and effectively utilize scarce resources, such as aircraft and trucks. The scale of Amazon’s fulfillment operations challenges us to design, build and operate robust transportation networks that minimize the overall operational cost while meeting all customer deadlines. Real-time execution of our network depends on state-of-the-art artificial intelligence tools to coordinate the actions of thousands of operators and drivers in real time. Amazon often finds existing techniques do not effectively match our unique business needs, which necessitates the innovation and development of new approaches and algorithms to find an adequate solution. As a Sr. Applied Scientist responsible for middle mile transportation, you will be working closely with different teams including business leaders and engineers to design and build scalable products operating across multiple transportation modes. You will create experiments and prototype implementations of new learning algorithms and prediction techniques. You will have exposure to top level leadership to present findings of your research. You will also work closely with other scientists and engineers to implement your models within our production system. You will implement solutions that are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility, and make decisions that affect the way we build and integrate algorithms across our product portfolio. About the team Our Middle Mile Marketplace Science team builds the algorithms for Amazon’s rapidly growing freight marketplace. Amazon contracts with 3P shippers and a network of independent carriers, using a mix of contract structures with varying service and risk profiles. Our work focuses on mechanisms and learning algorithms to optimize pricing and matching in this complex marketplace, and continually improve the experience for carriers and shippers. This is an area with many challenging problems and a huge business impact for Amazon!