Careers

The Amazon Web Services (AWS) Center for Quantum Computing (CQC) is seeking a Senior Applied Scientist to research and design cryogenic control hardware for large-scale quantum processors, with an emphasis on superconducting digital circuits. Controlling and reading out orders of magnitude more qubits than today's systems demands rethinking where classical control lives and what technology implements it; you will design and model superconducting digital logic (e.g., single-flux-quantum-family circuits) and related cryogenic electronics, evaluate them against the wiring, power, and thermal budgets of a scaled system, and co-design control approaches with the qubit devices they serve. You will carry designs from concept through simulation, layout, design review, and fabrication handoff, and your evaluations will inform which control-electronics directions the organization invests in. Key job responsibilities • Design and simulate superconducting digital circuits and cryogenic control/readout electronics for scalable qubit control • Develop and validate Josephson-junction circuit models, timing and margin analyses, and verification methodology for superconducting digital designs • Quantify system-level trade-offs — wiring count, power dissipation at each cryogenic stage, latency, footprint — across candidate control architectures, and communicate recommendations • Co-design control interfaces with qubit-device colleagues so control electronics and quantum devices are developed as one system • Deliver design artifacts through the organization's design-request and design-review processes, including tapeout-ready layouts and verification reports • Present results internally and, where appropriate, publish in peer-reviewed venues About the team The Enabling Technologies team develops technologies and relationships facilitating scaling, including alternative quantum processor architectures, superconducting qubit device packaging, and cryogenic control device electronics. We work alongside a co-located quantum processor design team focused on current-generation devices; the two teams share design and review processes, and moving ideas between them is an explicit part of our charter.

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 cryptographic library. 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 algorithm 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 who actively mentor and collaborate, so an early-career scientist gets both research depth and production-scale reach from day one.

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 cryptographic library. 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. - Specify the functional behavior of low-level cryptographic code (Rust, C, assembly) in formal notation and verify it using both automatic and interactive provers, such as HOL-Light, Verus and CBMC. - 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 algorithms (ML-KEM, ML-DSA, SLH-DSA) for production use. - Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues. - Publish patents and peer-reviewed articles and present your research both internally and externally A day in the life You take a cryptographic algorithm that needs to be provably correct and write the formal specification of its behavior, the code and the proof of correctness. You develop the proof in an interactive theorem prover, assisted by state-of-the-art AI models and iterate until the machine checks it end to end. Some days you are debugging a proof obligation; 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. About the team ACL owns AWS-LC (Amazon's FIPS-validated cryptographic library) and manages third-party cryptographic libraries. We build 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 AWS's Automated Reasoning Group on formal verification.

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 cryptographic library. 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. - Specify the functional behavior of low-level cryptographic code (Rust, C, assembly) in formal notation and verify it using both automatic and interactive provers, such as HOL-Light, Verus and CBMC. - 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 algorithms (ML-KEM, ML-DSA, SLH-DSA) for production use. - Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues. - Publish patents and peer-reviewed articles and present your research both internally and externally A day in the life You take a cryptographic algorithm that needs to be provably correct and write the formal specification of its behavior, the code and the proof of correctness. You develop the proof in an interactive theorem prover, assisted by state-of-the-art AI models and iterate until the machine checks it end to end. Some days you are debugging a proof obligation; 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. About the team ACL owns AWS-LC (Amazon's FIPS-validated cryptographic library) and manages third-party cryptographic libraries. We build 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 AWS's Automated Reasoning Group on formal verification.

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 algorithm 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 who actively mentor and collaborate, so an early-career scientist gets both research depth and production-scale reach from day one.

Amazon’s commitment to The Climate Pledge motivates us to work both inside and outside of our business operations to scale climate mitigation. Along with the decarbonization initiatives within our business operations, we have a robust strategy aligned with climate science for tackling climate change outside of our business. The Worldwide Sustainability (WWS) organization capitalizes on Amazon’s scale and speed to build a more resilient and sustainable company. We manage our social and environmental impacts globally, and drive solutions that enable our customers, businesses, and the world to become more sustainable. The Carbon Neutralization team is seeking a research scientist that will dive deep to drive strategy and create methodological approaches for impact evaluation in novel carbon dioxide removal and greenhouse gas emissions reduction segments. The Carbon Neutralization team is responsible for the science, strategy, and procurement of outside-value-chain greenhouse gas reduction and carbon dioxide removal credits to help reach net zero. To date, Amazon has made foundational investments in jurisdictional reductions in deforestation emissions, nature-based carbon removal via forest restoration, superpollutant abatement in refrigerants and rice cultivation, and technological carbon dioxide removal. Your work will help Amazon expand its portfolio of catalytic investments to scale essential climate solutions globally. The ideal candidate will be able to take a first principles approach to review and design scientific quantification methodologies that allow Amazon to confidently quantify the impact of outside-value-chain greenhouse gas reduction and carbon dioxide removal interventions. The candidate should have a deep systems understandings of the carbon cycle and global decarbonization and carbon removal pathways, and familiarity with various quantitative approaches to impact and uncertainty evaluation for project-based interventions. Key job responsibilities - Own science-based strategy and quality bar setting in a number of scalable carbon reduction and/or removal segments, including but not limited to: biomass carbon removal and storage (BiCRS), enhanced rock weathering, household fuel-switching, and/or superpollutant abatement. - Review and identify weaknesses in existing quantification approaches within voluntary and compliance carbon markets. - Develop novel methodological approaches to quantify the climate impact of, and monitor the ongoing performance of, specific segments and/or projects. - Build trust with external academics, civil society, and suppliers working in carbon reduction and removal. - Publish internal- and external-facing scientific publications demonstrating Amazon’s approach to quantification and credit quality. - Work closely with portfolio and project managers and a broader Amazon sustainability science and technology community to instill best scientific practices into Amazon’s neutralization investments. - Periodically travel to project sites and facilities around the US and world. About the team Diverse Experiences: World Wide Sustainability (WWS) 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. Inclusive Team Culture: It is 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 (diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth: We have a career path for you no matter what stage you’re in when you start here. 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.

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. About the team SPB Agent team's vision is to build a highly personalized and context-aware agentic advertiser guidance system that seamlessly integrates Large Language Models (LLMs) with sophisticated tooling, operating across all experiences. The SPB-Agent is the central agent that interfaces with advertisers across Ads Console, Selling Partner portals (Seller Central, KDP, Vendor Central), and internal Sales systems. We identify high-impact opportunities spanning from strategic product guidance to granular optimization and deliver them through personalized, scalable experiences grounded in state-of-the-art agent architectures, reasoning frameworks, sophisticated tool integration, and model customization approaches including fine-tuning, MCP, and preference optimization. This presents an exceptional opportunity to shape the future of e-commerce advertising through advanced AI technology at unprecedented scale, creating solutions that directly impact millions of advertisers.

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through cutting-edge generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. We are looking for an Applied Scientist III to set the scientific direction for the next generation of agentic AI applications that guide Amazon advertisers. In this role you will define, lead and build the science behind agentic systems that reason, plan, and act autonomously to manage and optimize ad campaigns based on a deep understanding of the advertiser and the marketplace. You will own the agentic architecture end to end, partnering closely with product and engineering leaders to translate a long-term science vision into concrete research and engineering roadmaps. Working backwards from the needs of millions of advertisers, you will take the lead on medium-to-large, ambiguous problems where neither the problem nor the solution is well defined, and deliver customer-facing products that help advertisers create, optimize, and grow their campaigns. You will invent new methods at the product level, and drive their adoption across multiple teams. This role combines science leadership, technical depth, product focus, and business understanding: you will raise the science bar, build consensus on approach across partners, and mentor scientists and engineers while remaining deeply hands-on with the hardest technical problems. Key job responsibilities As an Applied Scientist III on this team you will: - Define the science vision for the agentic campaign management system and, with product and engineering leaders, turn it into delivery roadmaps. - Build agentic systems that autonomously manage and optimize ad campaigns — encoding auction and marketplace dynamics (bidding, budget pacing, keyword and targeting decisions) while balancing advertiser ROI, shopper experience, and marketplace health. - Define and curate the datasets and signals needed to train and evaluate these agents — advertiser and campaign data, auction and bid/budget signals, impressions, clicks, conversions, and search-term/keyword performance. - Stay deeply hands-on: write production-quality, critical-path code and build core components that take agentic systems from prototype to launch. - Own the agentic architecture — planning, tool use and integration (e.g., MCP), long-horizon reasoning (e.g., ReAct, CoT/ToT), and multi-agent orchestration — and stay deeply hands-on, writing production-quality, critical-path code from prototype to launch. - Define the evaluation and safety methodology for agent workflows and drive its adoption as the bar for reliability and trust. - Drive the team's scientific agenda, mentor scientists and engineers, and represent the team in the internal and external scientific community. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. This team within Sponsored Products and Brands is focused on guiding and supporting millions of advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in guidance systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware agentic advertiser guidance system that leverages LLMs together with tools such as auction simulations, ML models, and optimization algorithms. This agentic framework, will operate across both chat and non-chat experiences in the ad console, scaling to natural language queries as well as autonomously manage campaigns based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular keyword recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning, MCP, and preference optimization), ensuring our systems are both scalable and adaptive.

Do you want to see your research directly impact how millions of customers discover, browse, and purchase products on Amazon — across Japan and the globe? Amazon's Japan Store Tech team owns the science and technology behind cross-border shopping — product discovery, search relevance, personalization, and content experiences spanning dozens of marketplaces. We tackle problems at massive scale: multi-language signals, multi-marketplace data, and region-specific customer behaviors, all served at low latency to millions of daily shoppers. We're looking for current Bachelor or Master students with a passion for applied science and machine learning to join us as an Applied Scientist in 2028 to shape the future of customer experiences at scale. For this position, our Japan Store Tech team is looking for students with a specialization in one or more of the following research areas: machine learning, deep learning, natural language processing (NLP), information retrieval, recommender systems, computer vision, large language models (LLMs), generative AI, causal inference, experimentation and A/B testing, optimization, and more! As an Applied Scientist, you'll develop novel models and algorithms, design and run experiments on live traffic, and own meaningful science contributions end-to-end. You'll also leverage and contribute to GenAI/LLM systems that power both customer-facing experiences and internal development tools. If you want to kickstart your science career at global scale — solving real customer problems alongside talented scientists and engineers in a collaborative, international environment — this is the place to start. Selection Communication Begins: October 5, 2026 Offer Extensions: December 2026 – March 2027 Key job responsibilities - Collaborate and communicate effectively with experienced cross-disciplinary Amazonians to design, develop, and deploy innovative machine learning models and scientific solutions that delight our customers, while participating in technical discussions to drive solutions forward. - Develop and implement scalable machine learning models and algorithms to improve product discovery, search relevance, personalization, or other customer-facing experiences. - Design and conduct experiments (offline and online) to validate hypotheses and measure the impact of proposed solutions. - Analyze large-scale datasets to identify patterns, generate insights, and inform model design decisions. - Leverage and contribute to the development of GenAI and LLM-powered tools to enhance customer experiences and development productivity while staying current with emerging technologies. - Write clean, maintainable, production-quality code following best practices. - Communicate research findings effectively through documentation, presentations, and technical papers. - Work in an agile environment and collaborate closely with software engineers to bring science solutions from prototype to production.

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 Senior 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. • Guide technical direction for specific research initiatives, ensuring robust performance in production environments. • Mentor fellow scientists while maintaining strong individual technical contributions. A day in the life • Develop and implement novel foundation model architectures, working hands-on with data and our extensive training and evaluation infrastructure • Guide and support fellow scientists in solving complex technical challenges, from trajectory planning to efficient multi-task learning • Guide and support fellow engineers in building scalable and reusable infra to support model training, evaluation, and inference • Lead 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 • Mentor team members while maintaining 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.