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Amazon Japan is seeking a Data Scientist to join our Cost-to-Serve Intelligence team — a group that answers the question: "Why does it cost what it costs to deliver a package, and how do we do it more efficiently?" You will design and run research studies that connect operational data to business decisions, helping leadership understand where to invest to reduce cost-to-serve across Japan's logistics network — ultimately enabling faster, cheaper delivery that improves the customer experience. At Amazon, you'll work alongside the latest AI and GenAI tools that are increasingly woven into how teams operate: from AI-powered capabilities that accelerate decision-making, to Generative AI that helps you focus on work that truly matters. You'll have opportunities and resources to develop AI fluency at your own pace, with continuous learning built into the culture. This is a science role with direct business impact. Your work will be presented to senior executives, sized in dollar terms, and used to prioritize multi-million-dollar operational investments. The cost savings you identify flow back to customers through lower prices and faster delivery. If you enjoy turning complex data into clear recommendations that people act on, this is the role. Key job responsibilities - Design and execute quantitative studies that explain why cost-to-serve moves — isolating root causes from noise and quantifying improvement opportunities - Bridge science to business decisions: translate statistical findings into investment recommendations, opportunity sizing, and initiative prioritization that leadership can act on - Partner cross-functionally with operations, finance, supply chain, and product teams to define research questions, validate findings, and ensure insights drive real-world action - Own the full research lifecycle — from problem framing and data exploration through methodology design, analysis, and stakeholder-ready deliverables - Apply a range of scientific methods (econometrics, statistical modeling, machine learning, AI-assisted analysis) matched to the problem at hand - Communicate findings effectively to both technical and non-technical audiences through structured documents, presentations, and data visualizations - Continuously improve the team's analytical toolkit — introducing new methods, automating repetitive analysis, and raising the bar on scientific rigor A day in the life You might start the morning in a sync with your Applied Scientist partner, reviewing outputs from a model that estimates how different operational levers impact cost-to-serve. Mid-morning, you join a working session with a partner team in supply chain or finance, aligning on what questions your next study should answer and what data you'll need. After lunch, you're building and validating a quantitative model — using Python, SQL, and AI-powered tools to test causal hypotheses, estimate coefficients, and ensure the model delivers reliable insights at scale. You then structure your findings into an actionable recommendation — quantifying the opportunity and proposing where to double down. You close the day preparing materials for a leadership review, translating your model outputs into a narrative that drives decisions. Your stakeholders span supply chain, operations, finance, and product. You are the person leaders come to when they need to understand why something changed, how much it matters, and what to do next. About the team We are a multi-disciplinary team of ~10 people — data scientists, applied scientists, product managers, and data engineers — who own Cost-to-Serve intelligence end-to-end: from the business questions through product strategy to the technical systems that deliver answers. We sit within JP Consumer Innovation and operate at the intersection of multiple organizations (operations, finance, supply chain, technology), giving us broad visibility and outsized influence on Amazon Japan's P&L. The work is high-visibility and high-impact. Our insights and products are consumed by VP-level executives and directly shape Japan-wide investment priorities. When we find a way to reduce cost-to-serve, that efficiency flows through to customers as faster delivery and better prices — the virtuous cycle at the heart of Amazon's flywheel. The culture is intellectually rigorous but collaborative — we publish internal science papers, present at company-wide summits, and run cross-functional knowledge-sharing sessions with hundreds of attendees. We value clear thinking over title, and mechanism over assertion. We are based in Tokyo and operate bilingually (English/Japanese).

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading 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. As for Brand Stores (e.g., amazon.com/lego and 1MM+ more), we are the exclusive destination for brand owners to showcase their content and product catalog through custom creative, seamlessly integrated with Amazon Stores and ads products, attracting millions of daily visits. We're reinventing how brands and shoppers connect. Using generative AI, we're building the next generation of brand-centric storefronts, reimagining store creation, optimization, performance analysis, and customer insights through state-of-the-art GenAI technologies. As a Senior Applied Scientist on the team, you'll own the science strategy and hands-on development of AI-powered brand experiences at Amazon scale. You'll operate at the intersection of frontier research and high-impact production systems, with the autonomy to shape what we build, how we build it, and where we invest next. This role combines science leadership, technical depth, product intuition, and business acumen. #GenAI Key job responsibilities If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, this is the role. * Build intelligent systems for Brand Stores. Develop AI-powered solutions leveraging generative models to optimize both advertiser and shopper experiences, measurably improving Brand Store performance. * Define a multi-year science vision and roadmap. Translate customer needs into actionable plans for applied scientists and engineering teams, blending science leadership, technical depth, product intuition, and business acumen. * Architect ahead of the GenAI curve. Anticipate where generative AI is heading over a multi-year horizon and position solutions to capitalize on compounding advances. * Experiment rigorously. Design and run A/B experiments grounded in deep data analysis to validate hypotheses and quantify impact. * Communicate with clarity. Translate complex technical ideas into compelling narratives for both technical and non-technical audiences. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading 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. The Brand Stores team within Sponsored Products and Brands is chartered to create agentic brand store building experience, automating brand store creation, personalization, and optimization across global marketplaces, serving millions of brand owners world-wide.

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.

As an Applied Scientist II in the Alexa Conversational Modelling Intelligence team within Alexa AI, you will drive model post-training for Large Language Models that power Alexa+. You'll adopt and adapt state-of-the-art techniques — including supervised fine-tuning, reinforcement learning, preference optimization, and knowledge distillation — running rigorous experiments and translating findings into production-ready solutions that directly improve the customer experience for millions of users worldwide. You will own the full model development cycle from data curation through training, evaluation, and deployment. Your day-to-day will involve developing evaluation methods and metrics, diagnosing model defects, optimizing model training pipelines, and iterating on recipes to move concrete quality and efficiency benchmarks. You'll write clean, reproducible code, contribute to shared tooling, and collaborate closely with scientists and engineers to bring models from experimentation to scale. You are technically curious, experiment-driven, and motivated by real customer impact. You are an expert in LLM post-training. You will also advance the state of the art by publishing at top-tier NLP/ML conferences (ACL, EMNLP, NeurIPS, ICML, ICLR) — contributing to the broader research community while grounding your work in measurable outcomes. Key job responsibilities * Own the full model development cycle — from data curation through training, evaluation, and deployment. * Develop and apply post-training techniques: supervised fine-tuning, reinforcement learning, preference optimization, and knowledge distillation. * Build evaluation methods and metrics, and diagnose model defects to target the highest-impact improvements. * Optimize model training pipelines and iterate on recipes to move concrete quality and efficiency benchmarks. * Write high-quality documentation on methods and experiment outcomes, and communicate findings clearly to stakeholders. A day in the life Post-training is one of the most active frontiers in LLMs right now. The field has moved from scaling pretraining to getting more out of models afterward through RL, reasoning recipes, and preference optimization. You'll work on these techniques directly, on a product used by millions of customers every day. A typical day: review overnight training runs and dashboards, dig into model defects to form hypotheses, then curate data and iterate on a recipe, improving shared tooling along the way. You'll sync with scientists and engineers to unblock the path to production, and write up your findings for stakeholders. It's fast-moving — a good idea can reach millions of customers within weeks. About the team The Alexa Conversational Modelling Intelligence team builds industry-leading LLM-based conversational technologies that customers love. Our mission is to push the envelope in LLMs for Alexa to deliver the best-possible customer experience. As an Applied Scientist, you'll contribute directly to that mission through model development and experimentation.

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Manager III, Economist Job Location: Sunnyvale, California Job Number: AMZ9803624 Position Responsibilities: Independently manage a team of economists and/or scientists in developing strategic economic analyses and demand estimation models. Translate business questions into econometric methodologies and causal inference analyses. Communicate economic insights to non-technical audiences to guide strategic-level, high-impact business decisions. Scale economic models through cross-functional collaboration with engineering teams. Establish scientific quality standards and research priorities. Drive operational efficiency and research excellence across the team. 40 hours / week, 8:00am-5:00pm, Salary Range: $201,300/year to $272,400/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000

As a Senior Applied Scientist specializing in lead scoring and deep learning modeling, you will tackle complex challenges in machine learning and deep learning to redefine how our business engages with customers. You will design and deploy high-impact models that drive customer segmentation, adaptive recommendations, and predictive lead and account prioritization. Leveraging your expertise in deep learning, representation learning, and general modeling, you'll help build solutions that directly influence business outcomes, collaborating with cross-functional teams to turn novel research into scalable, production-grade systems. Key job responsibilities * Design and deploy predictive lead scoring models to optimize customer acquisition, conversion, and retention strategies using advanced techniques like survival analysis, graph networks, or transformer-based architectures. * Architect end-to-end ML pipelines for large-scale deep learning models, including data preprocessing, distributed training, model optimization, and real-time inference. * Publish research, file patents, and stay ahead of industry trends in the marketing science, propensity modeling, and customer journey prediction domains. * Innovate in multi-modal modeling (text, graph, behavioral, and temporal data) to enhance scoring accuracy across account and lead levels. * Conduct rigorous A/B testing, causal inference, and counterfactual analysis to measure model impact and iterate rapidly. * Collaborate with MLOps engineers to streamline model deployment, monitoring, and retraining using tools like AWS SageMaker, or MLflow and other internal tools. * Participate in science reviews to raise the science bar in our organization. This includes reviewing your work and the work of others. * Mentor junior scientists on ML methodology, experimentation design, and production best practices. * Define offline and online evaluation frameworks; establish success metrics tied to business outcomes (conversion rates, pipeline generation). About the team The AWS Marketing Science team builds the ML models and measurement systems that drive marketing decisions across Amazon Web Services. We own incrementality and valuation, ROI measurement, marketing attribution, propensity scoring, account and lead clustering, and next-best-action models. Our work directly influences how AWS allocates marketing spend, targets accounts, and measures effectiveness across billions in pipeline.

Amazon's Identity Security & Abuse Prevention (ISAP) team is seeking an Applied Scientist to join our team. We discover, analyze, and quantify security risks across Amazon's identity and authentication landscape, transforming complex behavioral patterns into actionable intelligence that empowers teams to proactively defend against abuse and unauthorized access. In this role, you will design, build, and own machine learning systems that detect abuse patterns, classify threats, and automate enforcement across sensitive datasets spanning multiple Amazon verticals. You will independently frame ambiguous detection problems, develop novel approaches to abuse prevention, and deploy production ML systems that directly protect Amazon customers and sellers at scale. You will work at the intersection of applied science and security operations, translating complex abuse vectors into scalable detection capabilities. This is a high-ownership role where your models and systems run autonomously in production, making real-time decisions that prevent fraud and abuse. You will own both existing detection capabilities (improving precision, recall, and coverage of current models) and greenfield science (designing and deploying new detection systems for emerging threat vectors). You will lead experimental design, extend or invent methodologies for your domain, mentor junior scientists, and contribute to the team's scientific roadmap. You will partner with investigators, security engineers, and data engineers to build end-to-end detection and enforcement pipelines, and you will leverage GenAI, LLMs, and AI-agent architectures to advance our abuse prevention capabilities. Key job responsibilities - Design, develop, and deploy production ML systems for abuse pattern detection, anomaly detection, threat classification, and automated enforcement across multiple Amazon verticals - Independently frame ambiguous security and abuse problems into well-defined scientific questions, propose detection approaches, and drive them from hypothesis through production deployment - Own and improve existing detection models end-to-end: monitor for drift, diagnose degradation, retrain, and extend coverage as abuse patterns evolve - Build and maintain graph-based entity analysis, identity resolution, and modus operandi classification systems that link bad actors across accounts, devices, and behavioral signals - Design and execute rigorous experiments (A/B testing, offline evaluation, statistical validation) to measure model performance and quantify business impact - Architect and deploy GenAI and LLM-based solutions for investigation automation, case classification, and intelligent knowledge retrieval - Contribute to the team's scientific roadmap by identifying high-value detection opportunities, proposing new approaches, and driving prioritization of science investments - Publish research findings in internal Amazon papers and at external peer-reviewed conferences; contribute to the broader scientific community - Partner with investigators, security engineers, and data engineers to understand abuse patterns, translate operational insights into model features, and ensure detection systems drive real enforcement actions A day in the life Your morning might start with reviewing model performance dashboards for a classifier you deployed last month, noticing a subtle precision drop that suggests adversarial adaptation. You diagnose the drift, propose a feature addition to counter the new pattern, and kick off a retraining job. Mid-morning, you lead a design review on a new graph-based detection approach you developed to identify organized abuse rings operating across multiple verticals. After lunch, an investigator shares a newly identified modus operandi, and you explore the data to determine if the pattern is learnable at scale, sketching an experimental design. Late afternoon, you pair with a junior scientist on their anomaly detection model, helping them refine their evaluation methodology and avoid a common statistical pitfall. You close the day by drafting a section of a research paper on your entity resolution approach, preparing it for internal peer review. About the team The ISAP SafeGuard team mixes long-term, high-impact projects with near-term innovative solutions to prevent abuse across Amazon. We balance Bias for Action, Dive Deep, Invent and Simplify, and Customer Trust daily. Our team embraces new approaches, technology, and innovation while ensuring our solutions are scalable, accurate, and drive action. We work with some of the most sensitive data at Amazon, which requires thoughtful engineering, strict access controls, and a strong sense of responsibility. If you are energized by building ML systems that directly protect customers and sellers from bad actors, at scale, this is the team for you. Diverse Experiences Amazon Security 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 Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & 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, training, 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

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”.

Success in any organization begins with its people and having a comprehensive understanding of our workforce and how we best utilize their unique skills and experience is paramount to our future success. WISE (Workforce Intelligence powered by Scientific Engineering) delivers the scientific and engineering foundation that powers Amazon's enterprise-wide workforce planning ecosystem. Addressing the critical need for precise workforce planning, WISE enables a closed-loop mechanism essential for ensuring Amazon has the right workforce composition, organizational structure, and geographical footprint to support long-term business needs with a sustainable cost structure. We are looking for a Applied Scientist to join our ML/AI team to work on Advanced Optimization and LLM solutions. You will partner with Software Engineers, Data Engineers and other Scientists, TPMs, Product Managers and Senior Management to help create world-class solutions. We're looking for people who are passionate about innovating on behalf of customers, demonstrate a high degree of product ownership, and want to have fun while they make history. You will leverage your knowledge in machine learning, advanced analytics, metrics, reporting, and analytic tooling/languages to analyze and translate the data into meaningful insights. You will have end-to-end ownership of operational and technical aspects of the insights you are building for the business, and will play an integral role in strategic decision-making. Further, you will build solutions leveraging advanced analytics that enable stakeholders to manage the business and make effective decisions, partner with internal teams to identify process and system improvement opportunities. As a tech expert, you will be an advocate for compelling user experiences and will demonstrate the value of automation and data-driven planning tools in the People Experience and Technology space. Key job responsibilities - Engineering execution - drive crisp and timely execution of milestones, consider and advise on key design and technology trade-offs with engineering teams - Priority management - manage diverse requests and dependencies from teams - Process improvements – define, implement and continuously improve delivery and operational efficiency - Stakeholder management – interface with and influence your stakeholders, balancing business needs vs. technical constraints and driving clarity in ambiguous situations - Operational Excellence – monitor metrics and program health, anticipate and clear blockers, manage escalations To be successful on this journey, you love having high standards for yourself and everyone you work with, and always look for opportunities to make our services better. Internal job description

Do you want to create the greatest-possible worldwide impact in Robotics? Amazon has the world's most exciting treasure trove of robotics challenges. At Amazon Robotics we build high-performance, real-time robotic systems that can perceive, learn, and act intelligently alongside humans—at Amazon scale. Amazon Robotics invents and scales AI systems for robotics in fulfillment. Our mission is to enable robots to interact safely, efficiently, and fluently high density real-world fulfillment centers. Our AI solutions enable robots to learn from their own experiences, from each other, and from humans to build intelligence that feeds itself. We hire and develop subject matter experts in AI with a focus on 3D perception, computer vision, deep learning, and generative modeling. We target high-impact algorithmic unlocks in areas such as 3D scene understanding and completion, semantic occupancy prediction, multi-view 3D reconstruction, depth estimation, shape completion, and real-time inference - all of which have high-value impact for our current and future fulfillment networks. We are seeking an passionate, hands-on, seasoned Senior Applied Scientist who will be deep in code and algorithms; who is technically strong in building scalable 3D perception systems across semantic scene completion, encoder-decoder and transformer architectures (e.g., VoxFormer, MonoScene), voxelized occupancy prediction, panoptic and instance segmentation, depth estimation, point cloud processing, and multi-view fusion. As a Senior Applied Scientist, you will contribute to the research and development of advanced 3D perception pipelines that enable robots to reason about occluded and partially observed environments; your work along with other top-notch scientists and engineers will deliver the world's most scalable and robust robotic perception systems. You will drive ideas to products using paradigms such as 3D generative models, query-based transformers, masked autoencoder-style completion, and scalable pseudo-ground-truth data generation. As a Senior Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on challenging perception problems - such as completing 3D bin scenes from partial observations, integrating multi-camera inputs, and optimizing inference latency for edge deployment - distill requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth in 3D computer vision, scientific vision, project management skills, great communication skills, and a drive to achieve results in a collaborative team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven't been solved at scale anywhere before. Along the way, we guarantee you'll get opportunities to be a disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities - Architect, design, and implement 3D perception models - including encoder-decoder networks, query-based transformers, and generative architectures- for semantic occupancy prediction and scene completion on robotic platforms. - Own the end-to-end model lifecycle: develop scalable training pipelines, optimize inference latency for ARM-based edge processors, and deploy production models that meet real-time performance targets. - Design and scale pseudo-ground-truth data generation pipelines - both heuristic-based and learning-based (e.g., SAM3D, shape completion) to produce curated training samples using SageMaker infrastructure. - Drive multi-view perception integration by fusing multiple view camera inputs for robust 3D reconstruction in partially observed and occluded bin environments. - Influence the team's technical strategy and contribute to the long-term vision and roadmap for 3D perception in fulfillment robotics. - Partner with cross-functional stakeholders across engineering, science, and operations teams to define requirements, iterate on system design, and deliver end-to-end solutions from research prototype to production deployment. - Maintain high standards by participating in design and code reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. - Prototype and validate concepts through simulation, synthetic data evaluation, and live robotic workcell testing using 3D metrics (mIoU, IoU) and affordance-based evaluation frameworks. - Mentor applied scientists and engineers, raise the technical bar, and foster a culture of scientific rigor and rapid experimentation. A day in the life Amazon offers a full range of benefits for you and eligible family members, including domestic partners. 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 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! About the team https://www.youtube.com/watch?v=2X4CU3jmw-g The Vulcan Stow Perception team builds the visual intelligence that enables Amazon's next-generation robotic stow systems to understand and interact with densely packed fulfillment environments. We own the full perception stack—from raw sensor input to actionable 3D scene representations—powering robots that autonomously stow millions of items daily across Amazon's global network. Our team tackles some of the hardest unsolved problems in 3D robotic perception: completing occluded scenes from partial observations, generating real-time semantic occupancy predictions, fusing multi-camera inputs (pedestal and end-of-arm tool), and producing sub-250ms mesh reconstructions that drive downstream manipulation decisions. We operate at the intersection of research and production-scale deployment, building systems that must be both scientifically rigorous and operationally bulletproof. We are a tight-knit group of applied scientists and engineers who ship models that run on real robots in real fulfillment centers—not just papers or prototypes. Our culture values technical depth, rapid experimentation, and end-to-end ownership. If you want to push the boundaries of 3D computer vision, work with transformer and generative architectures at the frontier, and see your work directly impact how millions of packages reach customers - this is the team.