Careers

The Amazon GDS-MOP (modeling, Optimization and Planning) Science team is seeking an exceptional Applied Scientist with strong operations research and optimization expertise to develop production solutions for one of the most complex systems in the world: Amazon's Fulfillment Network labor capacity planning. At MOP Science, we design, build, and deploy optimization, statistics, machine learning, and GenAI/LLM solutions that power Amazon Labor Planning systems (ALPS) running across Amazon Fulfillment Centers worldwide. We solve a wide range of challenges encountered throughout the network, including labor planning and staffing, pick scheduling, stow guidance, and capacity risk management. We are tasked with developing innovative, scalable, and reliable science-driven production solutions that exceed the published state of the art, enabling systems to run frequently (ranging from every few minutes to every few hours per use case) and continuously across our large-scale network. Key job responsibilities As an Applied Scientist, you will collaborate with other scientists, software engineers, product managers, and operations leaders to develop optimization-driven solutions using a variety of tools and observe direct impact on process efficiency and associate experience in the fulfillment network. Key responsibilities include: • Develop understanding and domain knowledge of operational processes, system architecture and functions, and business requirements • Deep dive into data and code to identify opportunities for continuous improvement and/or disruptive new approaches • Develop scalable mathematical models for production systems to derive optimal or near-optimal solutions for existing and new challenges • Create prototypes and simulations for agile experimentation of devised solutions • Advocate for technical solutions with business stakeholders, engineering teams, and senior leadership • Partner with engineers to integrate prototypes into production systems • Design experiments to test new or incremental solutions launched in production and build metrics to track performance About the team 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: • Medical, Dental, and Vision Coverage • Maternity and Parental Leave Options • Paid Time Off (PTO) • 401(k) Plan

Have you ever placed an order on Amazon and wondered how it got to you so fast? Behind that speed is a massive transportation network generating billions of data points daily. We need someone who can turn that data into clarity. Come join the Network Engineering, Scheduling and Technology (NEST) Science team within Amazon Transportation Services. We are looking for a Data Scientist who is equal parts data engineer, visualization architect, and analytical modeler. You will own the end-to-end build process for data-driven solutions: identifying business needs, developing simulation and optimization models, building computationally efficient analytical tools, and narrating results through compelling data storytelling. This is not a dashboard-building role. You will work at the intersection of large-scale data processing, advanced analytics (including simulation and optimization), and data visualization, building tools that allow stakeholders to explore millions of records interactively, uncover patterns in network performance, and make data-driven decisions with confidence. The ideal candidate is a data wizard who thrives on wrangling massive datasets, building predictive and prescriptive models, architecting performant query and aggregation pipelines, and crafting visualizations that communicate complex findings with precision and clarity. You will own the full lifecycle, from problem identification and data extraction through modeling and simulation to production-grade analytical applications that narrate results back to stakeholders. You will collaborate closely with scientists, engineers, and product managers Key job responsibilities - Own the end-to-end analytical lifecycle: identify stakeholder needs, frame problems, build models, and narrate results through data tools and visualizations - Design and build production-grade analytical tools, BI applications, and interactive data products that enable self-service exploration of very large transportation datasets (billions of records) - Develop and enhance simulation and optimization models (discrete event simulation, agent-based modeling, mathematical optimization) applied to network planning and transportation operations - Architect computationally efficient data pipelines and aggregation strategies that support responsive, real-time or near-real-time visualization at scale - Develop advanced data storytelling artifacts that communicate complex network dynamics, trends, and anomalies to technical and non-technical stakeholders - Build and maintain reusable visualization frameworks and libraries tailored to transportation network data (routing, scheduling, flow, capacity) - Work with large-scale data platforms (Redshift, Spark, S3, Athena) to extract, transform, and model data for analytical consumption - Develop code (Python, SQL, Scala) for data processing, statistical modeling, simulation, and building automated analytical workflows - Collaborate with Applied Scientists, Research Scientists, Software Engineers, and Product Managers to integrate analytical tools into broader planning and decision-support systems - Define and implement best practices for data visualization performance, including sampling strategies, level-of-detail rendering, and progressive loading for large datasets - Communicate findings, methodology, and recommendations through compelling written and verbal presentations to leadership and business customers About the team The Network Engineering, Scheduling, and Technology (NEST) Science Team prototype, build, and productionize mathematical models that reduce transportation cost and improve customer experience in Amazon's Middle Mile network. Equipped with techniques from Operations Research, Machine Learning and Simulation, these models are used to govern scheduling and equipment selection of hundreds of thousands of truck movements, optimize network configurations, determine the transit times between nodes, and simulate network flow under uncertainty for informed decision making. Our core team consists of Applied, Data, and Research Scientists along with technical Product Managers that come from diverse backgrounds.

Amazon serves hundreds of millions of customers. Each one has a unique history of purchases, preferences, and behaviors. Our team's mission: turn that history into real-time contextual intelligence that makes every Amazon experience feel personal. We're hiring an Applied Scientist to push the boundaries of what's possible with LLMs, semantic retrieval, and customer understanding at scale. The problem space: Imagine a system that can instantly synthesize years of customer signals — what they bought, what they love, what they're planning — and surface the exact right context for any experience, in milliseconds. That's what we build. It's equal parts information retrieval, generative AI, and systems engineering. Why this role: 1. Scale: Your models will serve 1,500+ requests per second across Amazon's largest surfaces. 2. Impact: Direct revenue attribution in the hundreds of millions — your work shows up in customer experiences the same week. 3. Frontier tech: Fine-tuning LLMs, building custom embedding models, designing retrieval architectures that balance quality with sub-100ms latency constraints. 4. Data richness: Access to one of the most comprehensive customer behavior datasets anywhere. 5. Ownership: End-to-end — from research to production deployment to metric evaluation. Key job responsibilities 1. Invent new approaches to contextual retrieval, relevance scoring, and LLM-based summarization. 2. Fine-tune and evaluate language models for domain-specific understanding. 3. Design experiments that measure real customer impact, not just benchmark scores. 4. Ship production systems and iterate based on live metrics. 5. Collaborate across teams — Alexa, Search, Recommendations — as a platform that powers them all. 6. Mentor team members and shape the technical direction of our roadmap. Please visit https://www.amazon.science for more information. A day in the life You'll analyze large-scale behavioral data, design experiments, and build models that ship to production. You'll work closely with engineers to ensure your science translates into low-latency, high-reliability systems. You'll present findings to leadership and influence product strategy. Some weeks you'll be deep in model architecture; other weeks you'll be debugging a relevance gap in production. Every day, your work reaches real customers. About the team We're a small, high-impact team that values scientific rigor and engineering craft equally. The team values innovations and offers a safe place to try, fail and learn while fostering a culture of continuous improvement. Everyone is a leader and owner for everything we do as a team. Our team offers creative space with entrepreneurial work environment focusing on customer obsession.

Join Amazon's AOP (Analytics Operations and Programs) RoW ((Rest of World) Research Science Team to revolutionize supply chain planning across multiple geographies including India, Japan, Mexico, Brazil, MENA, Australia, and Singapore. As an Operations Research Scientist, you'll develop optimization solutions using simulations, integer programming, and heuristic search to solve complex short-term capacity planning challenges that directly impact millions of customers worldwide. Key job responsibilities We are looking for an Operations Research Scientist to develop & support our Short term capacity planning initiatives using Simulations, Integer Programming, Linear Programming or Heuristic search. This Scientist, will work closely with our program partners to define business requirements, build data pipeline, write optimization code, deep dive on solution quality and drive adoption with operations. The employee will also be responsible for interfacing with global science teams to help launch their tools to new geographies. A day in the life The employee will work with our program partners to find new opportunities for building/launching decision support tools for our Supply Chain planning teams. This will include 1) Understanding the current planning process for program teams and tools available to them. 2) Determining the gaps in the tools/decision making through data analysis or simulation systems. 3) Determining the best possible tool to solve for the current gaps. 4) Launch or develop the identified tool through coding or solution deep dives and scenario creation. About the team AOP (Analytics Operations and Programs) team supports supply chain processing for the multiple geographies like IN, Japan, Mexico, Brazil, MENA, AU & SG. The research team works to support network design, labor planning and capacity planning processes through launching decision support tools for planning or execution.

** Join Our Innovative Computer Vision Team at Amazon, Australia ** Are you passionate about developing computer vision models to transform the shopping experience for fresh produce and build AI models for fresh monitoring at scale? We invite you to be part of our high-performing Computer Vision team at Amazon, Australia. As a member of our international Machine Learning group, you will play a key role in building AI solutions that leverage vast amounts of Amazon data and cloud computing resources. Our mission is to build next-generation AI systems that monitor fresh produce 24/7 and ensure we deliver the best quality produce to our customers. We are seeking talented Computer Vision Scientists with a Ph.D. in a related field. This is an opportunity for you to build innovative AI techniques that tackle real-world business challenges. Join a team dedicated to advancing AI technology at Amazon and transforming it into impactful business solutions. #austechjobs Key job responsibilities - Develop scalable machine learning and computer vision solutions for the fresh monitoring system - Analyze and extract meaningful insights from large volumes of Amazon’s data to automate and enhance content - Design, build, and evaluate generative AI models tailored to our business use cases - Communicate clearly with business stakeholders to understand and align on requirements - Conduct s.o.t.a. research and implement novel machine learning techniques to solve customer problems - Mentor interns and junior scientists

AWS Elastic Compute Cloud (EC2) Capacity Research and Engineering Org is looking for an experienced applied optimization expert. This leader will join the Onhand Packing Science team to design, implement, and scale decision-making algorithms to optimize EC2’s use of existing capacity. The team owns the optimization of all aspects of virtual instances placement across EC2, from high level supply shaping right down the final physical machine a customer’s instance gets allocated to. Given the sheer scale of EC2 any improvement efficiency within capacity is utilized has significant business impact. Key job responsibilities We are seeking an expert with a strong background in mathematical optimization with excellent modeling skills, with experience using both exact and heuristic methods. The domain requires a tight collaboration with engineering teams, to ensure we are able to quickly move from modelling and prototyping to full scale production usage. You should be confirmable with the needed statistical approaches for production A/B tests as well as have a solid understanding experimental design more broadly. Being successful in the role requires having the scientific breadth to understand the interactions between different phases of a project from data analysis through to production, including resolving issues after rollout. You will be hands-on with the mathematical modeling and implementation, and will also contribute to the design of the engineering system with the scalability, extensibility, maintainability, and correctness of the optimization engine in mind. You will review approaches by other scientists and engineers in terms of business relevance, technical validity, engineering / science interface, and computational performance. You will mentor and lead junior scientists by example. Communicating your results to guide the direction of the business and working with software development teams to implement your ideas in code is key to success. You will write technical, and less frequently, business documents that influence engineering investments and business direction. Collaborating with other scientists, software engineers, and product managers, you will develop creative, novel, and data-driven approaches to improve our existing cloud compute offerings and define new ones in a fast-paced and quickly changing environment, improving the experience of our customers and impacting the bottom line of EC2. About the team A day in the life 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. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon) 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.

The Ads Marketing Decision Science team builds intelligent, data-driven systems that transform advertiser experiences through precise personalization and automated optimization. We decode complex patterns in advertiser behavior, content effectiveness, and performance signals to power real-time, contextual marketing decisions at scale — moving Amazon Ads from rules-based relevancy to true AI-driven personalization. Our work spans four pillars: Advertiser DNA (behavioral fingerprinting to predict advertiser needs and growth opportunities), Content Intelligence (frameworks to evaluate, select, and generate marketing content aligned to advertiser context), Automated Decision Systems (ML-powered audience targeting and next-best-action recommendations), and Gen-AI Applications (contextual, natural interactions across marketing touchpoints). As a Senior Applied Scientist on the team, you will be at the forefront of our Gen-AI applications, leading the science behind conversational and agentic experiences that help advertisers grow. This role demands a strong foundation in machine learning and in LLM/NLP — deep fundamentals that you apply to build robust, production-grade systems rather than treating models as black boxes. In particular, you will own the development of our chatbot capability — designing the agentic reasoning, retrieval, and evaluation systems that make these interactions accurate, helpful, and trustworthy. You will set the technical vision, innovate on behalf of our customers, and take solutions end-to-end from inception to production. You will partner closely with engineering to deploy at scale and low latency, and with product and business teams to ensure the experience meets real advertiser needs. Key job responsibilities • Lead the design and development of the chatbot/agentic AI capability for WeChat and other third-party channels, from concept through production. • Bring strong ML and LLM/NLP fundamentals to bear on system design — grounding architecture and modeling choices in a deep understanding of the underlying methods. • Architect and build agentic AI systems — planning, tool use, and multi-step reasoning — grounded in Retrieval-Augmented Generation (RAG) over Amazon Ads knowledge sources. • Apply reinforcement learning and model fine-tuning (e.g., instruction tuning, RLHF/RLAIF, preference optimization) to adapt large language models to our domain and channels. • Define and operationalize rigorous LLM evaluation: golden sets, faithfulness/groundedness, precision/recall, and human-in-the-loop evaluation mechanisms that reliably measure and improve quality. • Own applied engineering quality of the science stack — PyTorch modeling, well-designed APIs, and latency/cost optimization for real-time, production-grade interactions. • Collaborate with engineering, product management, and business teams to define requirements and ship measurable customer impact. • Drive continuous improvement through experimentation, iterative development, testing, and optimization. • Translate complex scientific challenges into clear, impactful solutions for business stakeholders. • Mentor and guide junior scientists, fostering a collaborative, high-performing team culture, and engage the broader scientific community through presentations, publications, and patents. About the team We are a team of Applied Scientists, Research Scientists, Data Scientists, and Business Intelligence Engineers with deep expertise in ML, NLP, Gen-AI, RL, and causal inference, from a diverse range of backgrounds. We partner closely with strong engineers, product managers, and sales leaders who bring ads-industry depth and experience building scalable modeling and software solutions.

Amazon Stores Finance Science (ASFS) is committed to integrating industry leading scientific methodologies into our financial processes. Our mission is to collaborate with finance and business partners to leverage advanced scientific and economic products to optimize insights, analysis, and decision-making. Our primary focus lies in developing solutions for Finance's enduring challenges and opportunities, encompassing financial controllership, planning, and operational efficiency. We are seeking a Applied Scientist to propel Gen AI acceleration within Finance. This role will focus on leveraging large language models and agentic Ai to enhance decision-making processes, automate complex controllership processes, and improve operational efficiency in Stores Finance. Key job responsibilities - Develop approaches to automate and enhance financial processes using state-of-the-art language models - Partner with business stakeholders to identify high-impact opportunities for AI-driven transformation - Work with Software Engineering and Data Engineering to implement solutions that scale to the entire organization A day in the life Are you interested in solving complex problems at the intersection of Science/Economics and Finance? And doing this while working in the Finance organization supporting one of the world’s largest and most complex and dynamic businesses? This is a unique opportunity for qualified individuals to drive the entire life cycle of highly impactful, real-world ML applications, focused on use of advanced generative AI technology. If you thrive in a fast-paced, dynamic environment, and are passionate about driving impact, we encourage you to apply! About the team WW Amazon Stores Finance Science (ASFS) works to leverage science and economics to drive improved financial results, foster data backed decisions, and embed science within Finance. ASFS is focused on developing science/econ products that empower controllership, improve business decisions and financial planning by understanding drivers, and innovating Gen AI capabilities for efficiency and scale.

Amazon’s Middle Mile transportation network runs on physical assets and operations that generate an enormous volume of imagery and video. The Network Engineering, Scheduling, Technology (NEST) Science team within the Amazon Transportation Services organization is looking for an Applied Scientist with deep Computer Vision (CV) expertise and the versatility to apply machine learning broadly, to help turn that visual data into automated, operational decisions, spanning asset condition, automated inspection, object and component detection, and other visual understanding problems across the network. In this role, you will develop, train, and productionize computer vision models across a portfolio of high-impact use cases and business challenges, owning the scientific approach from problem formulation through production. You will work closely with other scientists, business owners, and engineering teams to advance modeling approaches, design rigorous experiments to validate them, and launch them to production. This is a hands-on applied science role with a broad scope and direct, measurable business impact. Key job responsibilities -Develop and apply visual perception and representation learning across image and video domains, including recognition, detection, segmentation, and tracking, with deep spatial and temporal reasoning enabled by modern deep learning and foundation-model architectures. -Own large-scale model training, fine-tuning, and learning from heterogeneous or weakly supervised data, including self-supervised and semi-supervised techniques. -Tackle real-world CV challenges at scale: large unlabeled datasets, class imbalance, high visual variability, and inconsistent capture conditions (angle, lighting, occlusion, motion blur). -Partner with product/program, operations, and engineering stakeholders to translate operational problems into well-defined CV objectives with measurable success criteria. -Design rigorous evaluation frameworks with explicit precision/recall tradeoffs and operating-point selection tied to real-world business cost, including the cost asymmetry between false negatives and false positives. -Build and maintain custom training and inference pipelines, and partner with engineering teams to deploy models into production. -Frame ambiguous operational problems from first principles and select the right approach for each, applying CV where it’s the best tool and other machine learning or simpler methods where they are not.

Employer: Twitch Interactive, Inc. Position: Applied Scientist III - AMZ27946.1 Location: San Francisco, CA Multiple Positions Available: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data, and run and analyze experiments in a production environment. Identify new opportunities for research in order to meet business goals. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. (40 hours / week, 8:00am-5:00pm, Salary Range $192200 - $260000) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000