Careers

A customer may encounter a recommendation that is irrelevant, two widgets offering similar products, and confusing labels. Customers notice all of it. The ranking systems that chose those impressions largely do not, because until recently there was no way to turn a judgment about how a page feels into a signal a model could learn from. LLMs changed that. We can now take an ambiguous statement about customer perception, make it a judgment that holds up consistently at scale, and validate it against what shoppers actually do next. What we cannot do is call a large model in a ranking request path at large scale. Every shopping session passes through those systems under latency and cost budgets that leave no room for one. So we can describe quality far better than we can optimize for it, and closing that gap is what this role exists to do. You will distill LLM quality judgments into models compact enough to serve online, model how customers respond to the defects they encounter so we know which ones are worth trading engagement to prevent, and work with Search, Homepage and Detail Page ranking teams to get those signals into online objectives. Scientists on this team own the measurement side of the problem. You own the half that turns their judgments into systems that act and test them in controlled experiments. It is an unusual combination of problems: research-grade modeling with an unambiguous production bar, on surfaces where the change you ship is visible to nearly every Amazon customer. Key job responsibilities - You will model how customers respond to the defects they encounter. Using newly instrumented logging data, you will build representations of customer sessions from page sequences, intent signals, and quality exposures, and quantify what changes downstream when a customer meets an irrelevant recommendation, a set of near-duplicate widgets, or a confusing label early in a journey. You will identify the contextual factors that mediate that impact, including session intent, category, device, and prior interactions, and turn the results into a ranking of which defects are worth trading engagement to prevent, on which surfaces, for which customers. - You will distill LLM quality judgments into models compact enough to serve online. That means training compact models against LLM-generated labels, characterizing where the student diverges from its teacher and on which segments, and holding accuracy under the latency and cost budgets of Search, Homepage, and Detail Page ranking. Where a distilled model cannot meet that bar, you will say so early and propose what would. - You will work with Search, Homepage, and Detail Page ranking teams to get those signals into online objectives. You will analyze which of those systems offers the most leverage, recommend where to invest first, and design quality-aware objective formulations that trade impression quality against engagement deliberately, replacing the current pattern of suspending a strategy after a problem surfaces. - You will prove all of it in controlled experiments. You will design the experiment, choose the right success metrics metrics and make the call on what ships. About the team Core Shopping Data Science owns the measurement of shopping quality across Amazon’s Homepage, Search, and Detail Page experiences, including the company-level defect metrics reviewed by Amazon’s most senior leadership. We build the metrics, tools, and datasets that teams across Stores and Advertising use to decide what to ship. We are a small team, which means your work is visible and your scope grows as fast as you do.

Amazon Ads is building a >$100BN business, and our 3000+ advertising partners — agencies and tech providers — are strategic growth engines for that ambition. The Partner Science team drives the Advertising Partner flywheel by infusing science-based interventions at every stage of the partner journey: demand generation, partner selection, partner engagement and growth, and partner value, and partner experience measurement. We are looking for an Applied Scientist to join our team and develop ML/AI models and causal inference studies that directly improve how advertisers find, work with, and succeed through partners. In this role, you will design, build, and productionize ML/AI and econometric solutions. You will work on ambiguous, real-world and high-impact problems where neither the problem nor the solution is well-defined, and you will be trusted to operate with growing autonomy while collaborating closely with senior and principal product managers, engineers, data engineers, BIEs, and sales/marketing stakeholders. Key job responsibilities • Design, prototype, validate, and productionize ML models across science domains: Predictive/Supervised (e.g., propensity models, deep learning, reinforcement learning), Causal Measurement (A/B tests, causal inference studies), and Text Analytics/LLMs (signal extraction, model explainability, Gen-AI application). • Independently own one or more production science models end-to-end— from initial scoping and design, to final deployment and ongoing monitor and refinement. Conduct scientific literature reviews, benchmark state-of-the-art approaches, and develop novel techniques when no textbook solution exists for our partner ecosystem challenges • Design and run A/B experiments using our scalable experiment framework to validate whether science interventions and product features drive partner growth and ad spend, working with our small, skewed partner population • Perform hands-on data analysis with large-scale advertising datasets, leveraging our centralized Partner Knowledge Base with hundreds of numeric features and unstructured data and Andes data infrastructure Collaborate with the MLOps engineering team to deploy models, and with Data Engineering to create curated datasets that power science and analytics efforts • Translate model outputs into business impact for cross-functional stakeholders (Different Sales and Marketing teams, Finance, Partner Product teams), simplify and provide business friendly communication to drive effective debates and trade-off discussion, and lead to alignment and decision. • Contribute to model quality monitoring, data quality frameworks, and operational excellence — including defining evaluation thresholds and data quality checks at each pipeline stage • Mentor teammates and contribute to a culture of intellectual integrity, continuous learning, and knowledge sharing About the team The Partner Science team sits within the Partner Analytics organization in PartnerTech, Amazon Ads. Our mission is to drive the Advertising Partner flywheel by infusing science-based interventions at all stages of the partner journey — demand generation, partner selection, partner engagement and growth, and partner value — ultimately improving the partner-managed advertiser experience. We are part of a broader Partner Analytics team comprising Data Engineering, Business Intelligence, and Science functions, all unified by a shared commitment to both advertiser and partner success. The Science team currently includes senior applied scientists, data scientists, supported by MLOps engineering partners who help us scale model deployment. Together, we own 10+ production science models and studies that power Partner Network platform features, sales and marketing programs, and finance attribution and forecasting across 20+ marketplaces. We bias for action, embrace a culture of fast iteration and reinforcement learning, celebrate both achievements and lessons learned, and invest in growing top scientist talent. If you are enthusiastic about applying ML/AL, causal inference, and LLMs to real-world advertising ecosystem problems with measurable business impact, we'd love to hear from you. Too learn more about us, see our wiki https://w.amazon.com/bin/view/AdSales/SPE/PEG/Analytics/Science/Overview

The R2L team is responsible for building the next generation supply chain for Amazon’s world-class ultra-fast customer experiences including Amazon Fresh groceries, Sub-Same Day, Amazon Now, and other soon-to-launch exciting new businesses. Join us and you'll be taking part in serving our customers in as fast as 30 minutes! R2L Science & AI team sits under R2L and is a central team for all Data Science/AI related asks. We are looking for an experienced and curious data scientist with effective superior analytical skills to inform the data science charter of the team. Key job responsibilities We are looking for an experienced and curious data scientist with effective superior analytical skills to inform the data science charter of the team. This position is critical in helping us learn more about our data and finding opportunities to delight customers with data driven insights and machine learning models. The Data Science and Analytics team owns data science, data engineering, and business intelligence. You will be supporting multiple business and technical stakeholders with high velocity analytics. This role is uniquely positioned in the team as we have a growing need for looking around corners, prioritizing opportunities using data driven insights, and finding solutions to these opportunities using different machine learning techniques and causal inference models. You will be diving deep in our data and have a strong bias for action to quickly produce high quality data analyses with clear findings and recommendations. As part of our journey to learn about our data, some opportunities may be a dead end and you will be balancing unknowns with delivering results for our customers. A day in the life 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! 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 Learn more about our benefits here: https://amazon.jobs/en/internal/benefits/us-benefits-and-stock

The Shopping Convo Foundations Team - Pre-purchases Science is looking for an Applied Scientist with expertise in Artificial Intelligence and Machine Learning to drive scientific innovation that expands Amazon's product catalogue. Our goal is to leverage AI/ML solutions to enhance catalogue coverage with high precision. In this role, you will research and develop novel machine learning approaches to solve complex catalogue expansion and product attribute challenges. You will design and develop state-of-the-art ML models, conduct rigorous experimentation, and translate scientific breakthroughs into production-ready solutions. You will work closely with ML Engineers and Software Development Engineers to optimize model performance, ensure scalability, and deploy low-latency solutions at Amazon scale.

The Region Flexibility Engineering (RFE) team builds and leverages foundational infrastructure capabilities, tools, and datasets needed to support the rapid global expansion of Amazon's SOA infrastructure. Our team focuses on robust and scalable architecture patterns and engineering best practices, driving adoption of ever-evolving and AWS technologies. RFE is looking for a passionate, results-oriented, inventive Data Scientist to refine and execute experiments towards our grand vision, influence and implement technical solutions for regional placement automation, cross-region libraries, and tooling useful for teams across Amazon. As a Data Scientist in Region Flexibility, you will work to enable Amazon businesses to leverage new AWS regions and improve the efficiency and scale of our business. Our project spans across all of Amazon Stores, Digital and Others (SDO) Businesses and we work closely with AWS teams to advise them on SDO requirements. As innovators who embrace new technology, you will be empowered to choose the right highly scalable and available technology to solve complex problems and will directly influence product design. The end-state architecture will enable services to break region coupling while retaining the ability to keep critical business functions within a region. This architecture will improve customer latency through local affinity to compute resources and reduce the blast radius in case of region failures. We leverage off the sciences of data, information processing, machine learning, and generative AI to improve user experience, automation, service resilience, and operational efficiency. Key job responsibilities As an RFE Data Scientist, you will work closely with product and technical leaders throughout Amazon and will be responsible for influencing technical decisions and building data-driven automation capabilities in areas of development/modeling that you identify as critical future region flexibility offerings. You will identify both enablers and blockers of adoption for region flex, and build models to raise the bar in terms of understanding questions related to data set and service relationships and predict the impact of region changes and provide offerings to mitigate that impact. About the team The Regional Flexibility Engineering (RFE) organization supports the rapid global expansion of Amazon's infrastructure. Our projects support Amazon businesses like Stores, Alexa, Kindle, and Prime Video. We drive adoption of ever-evolving and AWS and non-AWS technologies, and work closely with AWS teams to improve AWS public offerings. Our organization focuses on robust and scalable solutions, simple to use, and delivered with engineering best practices. We leverage and build foundational infrastructure capabilities, tools, and datasets that enable Amazon teams to delight our customers. With millions of people using Amazon’s products every day, we appreciate the importance of making our solutions “just work”.

Are you excited about leveraging state-of-the-art Computer Vision algorithms and large datasets to solve real-world problems? Join Amazon as an Applied Scientist Intern and be at the forefront of AI innovation! As an Applied Scientist Intern, you'll work in a fast-paced, cross-disciplinary team of pioneering researchers. You'll tackle complex problems, developing solutions that either build on existing academic and industrial research or stem from your own innovative thinking. Your work may even find its way into customer-facing products, making a real-world impact. Please note: This internship is a duration of 6 months full time with a start date in Jan-March 2027. The successful intern is required to be based in Melbourne and relocation allowance will be provided if you are based outside of Melbourne. Key job responsibilities - Develop novel solutions and build prototypes - Work on complex problems in Computer Vision and Machine Learning - Contribute to research that could significantly impact Amazon's operations - Collaborate with a diverse team of experts in a fast-paced environment - Collaborate with scientists on writing and submitting papers to Tier-1 conferences (e.g., CVPR, ICCV, NeurIPS, ICML) - Present your research findings to both technical and non-technical audiences Key Opportunities - Collaborate with leading machine learning researchers - Access Amazon tools and hardware (large GPU clusters) - Address challenges at an unparalleled scale - Become a disruptor, innovator, and problem solver in the field of computer vision - Potentially deliver solutions to production in customer-facing applications - Opportunities to become an FTE after the internship Join us in shaping the future of AI at Amazon. Apply now and turn your research into real-world solutions!

The Amazon Cryptographic Libraries (ACL) team builds the cryptography that AWS services and a growing open-source community depend on, including AWS-LC, our FIPS-validated open-source libcrypto. As an Applied Scientist on the team, your primary focus will be formal verification: building machine-checked proofs that cryptographic implementations are correct. You will also contribute to algorithm implementation, assembly level optimization, and the adoption of post-quantum cryptography. You will work alongside senior scientists on the team, building deep expertise in an environment where your proofs and code ship to effectively every AWS service. This is a role where an early-career scientist gets both rigorous mentorship and immediate production-scale impact. Key job responsibilities - Develop and maintain machine-checked proofs of correctness for cryptographic implementations in AWS-LC, working alongside senior scientists. This is the core of the role. - Specify the functional behavior of low-level cryptographic code (C, assembly) in formal notation and verify it using interactive theorem provers (HOL Light, Isabelle/HOL, or similar). - Apply formal methods, program analysis, and rigorous testing to raise the assurance bar of a security-critical, widely deployed codebase. - Contribute to the implementation and optimization of cryptographic algorithms, including post-quantum constructions (ML-KEM, ML-DSA, SLH-DSA), for production use. - Collaborate with SDEs, security engineers, and partner teams to translate verified implementations into production-grade, FIPS-validated software. - Grow your expertise through publications, open-source contributions, and engagement with the broader formal-methods and cryptographic research communities. A day in the life You take a cryptographic primitive that needs to be provably correct. Working with a senior scientist or an ARG partner, you write the formal specification of its behavior, develop the proof in an interactive theorem prover, and iterate until the machine checks it end to end. Some days you are debugging a proof obligation that does not discharge; other days you are reading a paper on a new verification technique or helping refine an algorithm implementation so it is both fast and amenable to proof. Your proofs back code that is validated for FIPS and deployed across AWS, so you operate at an assurance bar most scientists never encounter this early in their career. About the team ACL owns AWS-LC (Amazon's FIPS-validated libcrypto), the Amazon Corretto Crypto Provider (ACCP), and managed third-party cryptographic libraries, the cryptographic foundation under nearly every AWS service and a growing set of external open-source projects. Applied Scientists on the team own algorithm-level and assembly performance work and partner deeply with Amazon's Automated Reasoning Group on formal verification. The team has senior scientists at L6 and L7 who actively mentor and collaborate, so an early-career scientist gets both research depth and production-scale reach from day one.

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced ML systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real-world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning team for India Consumer Businesses. Machine Learning, Big Data and related quantitative sciences have been strategic to Amazon from the early years. Amazon has been a pioneer in areas such as recommendation engines, ecommerce fraud detection and large-scale optimization of fulfillment center operations. As Amazon has rapidly grown and diversified, the opportunity for applying machine learning has exploded. We have a very broad collection of practical problems where machine learning systems can dramatically improve the customer experience, reduce cost, and drive speed and automation. These include product bundle recommendations for millions of products, safeguarding financial transactions across by building the risk models, improving catalog quality via extracting product attribute values from structured/unstructured data for millions of products, enhancing address quality by powering customer suggestions We are developing state-of-the-art machine learning solutions to accelerate the Amazon India growth story. Amazon India is an exciting place to be at for a machine learning practitioner. We have the eagerness of a fresh startup to absorb machine learning solutions, and the scale of a mature firm to help support their development at the same time. As part of the India Machine Learning team, you will get to work alongside brilliant minds motivated to solve real-world machine learning problems that make a difference to millions of our customers. We encourage thought leadership and blue ocean thinking in ML. Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions that attack India first (and other Emerging Markets across MENA and LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide

AWS Networking operates one of the largest and most complex networks on the planet. The team you'd join is responsible for the availability of that network — measuring how it performs for customers, predicting where it is most likely to degrade, and reshaping how we operate it as the workload grows. We are in the middle of a significant change in how network operations are run. Lessons from our recent work on automation, AI, and ML — including agentic systems that triage and mitigate incidents alongside engineers — are feeding into a broader rethink of where humans focus, where automation takes over, and how we measure whether either is working. We are looking for a Data Scientist to join the team in Sydney to drive the data science strategy behind that change. You will define the metrics that matter, own the evidence the team uses to make decisions, and measure whether each decision delivered the outcomes we expected. You'll be the data science voice on a team of senior network and software engineers — the person who decides what we measure, how we measure it, and what the numbers actually mean. Concretely, that means setting the analytical bar for the program, designing risk and reliability models against telemetry from millions of network devices, surfacing the patterns that drive customer-impact incidents, and turning that analysis into the dashboards and metrics our leaders use to set priorities. It also means owning the evaluations that tell us when a new piece of automation — including the agents we are rolling out to support engineers on the front line — is actually moving the needle on availability, and not just adding noise. If you are a scientist who wants to shape how a tier-one production network is run — using data to drive program strategy, not just to support it — at a scale no academic lab or startup can match, and you're at your best as the data science voice embedded in a team of engineers, this is the team for you. Key job responsibilities - Define and drive the data science strategy for the program — the metrics, the experiments, and what counts as evidence that a change worked - Lead the design and deployment of predictive risk and reliability models for network availability, using device failures, alarm telemetry, ticket data, and traffic signals - Own the evidence behind program decisions: where availability is at risk, where automation is ready to expand, where engineering effort has the highest leverage. Defend recommendations to senior technical and business audiences - Design and own the operational analytics and dashboards (Amazon QuickSight, Amazon CloudWatch, Python) used by senior leadership to track network health and the impact of operational change - Design and run experiments to evaluate the automation we are rolling out — including agentic systems supporting engineers on incidents — measuring whether each rollout improved availability - Drive data quality and classification improvements — event categorisation, root-cause attribution — so the program's metrics rest on solid ground - Build and own event-driven scoring pipelines (Python, SQL, AWS Lambda, Amazon S3, Amazon Athena) that keep the decide / measure / improve loop running - Bring statistical rigour to the engineers you partner with — review experiment designs, push back on unsupported assumptions, and raise the bar on how the team uses evidence A day in the life You might start the morning defining how the team will measure a new initiative — the success metrics, the counterfactual, the bar for calling it a win. By mid-morning you're with the engineering team turning a proposal into a decision: walking through trade-offs, pushing back where the data doesn't support an assumption. The afternoon is outcome measurement — refining the evaluation pipeline that tracks last week's rollout, updating the CloudWatch dashboard senior leadership uses to gate the next expansion, and prepping the data for an upcoming Director review. About the team We sit inside AWS Networking with a strong Sydney presence and a remit that spans network availability, the data and analytics that support it, and the automation we are building to change how operations are done. You'd be the data science voice in a small, senior team of network and software engineers in Sydney, partnering with the broader network engineering organisation across Seattle and Dublin. Small team, high autonomy, direct line to senior leadership, and a roadmap with real production impact rather than research demos.

Are you excited about leveraging and extending state-of-the-art Deep Learning, Information Retrieval, Natural Language Processing, Computer Vision algorithms to solve customer problems at the scale of Amazon? As an Applied Scientist Intern, you will be working in the Melbourne office in a fast-paced, cross-disciplinary team of experienced R&D scientists. You will take on complex problems, work on solutions that leverage existing academic and industrial research, and utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even deliver these to production in customer facing products. Key job responsibilities - Develop novel solutions and build prototypes - Work on complex problems in Deep Learning and Generative AI - Contribute to research that could significantly impact Amazon operations - Collaborate with a diverse team of experts in a fast-paced environment - Present your research findings to both technical and non-technical audiences - Collaborate with scientists on writing and submitting papers to top ML conferences, e.g. NeurIPS, ICML, ICLR, AISTATS, ACL ICCV, CVPR, KDD. Key Opportunities: - Work in a team of ML scientists to solve applied science problems at the scale of Amazon - Access to Amazon services and hardware - Potentially deliver solutions to production in customer-facing applications - Opportunities to be hired full-time after the internship Join us in shaping the future of AI at Amazon. Apply now and turn your research into real-world solutions!