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James McQueen

Senior Manager, Applied Science
Amazon Customer Behavior Analytics

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Senior Applied Scientist, International Machine Learning
IN, KA, Bengaluru
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 algorithmic 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 and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major 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, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques 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
Manager, Applied Science, RBS Tech
IN, KA, Bengaluru
RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and visual), supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, product similarity, using GenAI, LLMs, NLP and Computer Vision. Key job responsibilities As an Applied Science Manager, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will Lead scientists on the team and oversee research and development projects at various stages ranging from initial exploration to deployment into production systems. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will create the environment in the team to file for patents and/or publish research work where opportunities arise. You will impact the large product strategy, identifies new business opportunities and provides strategic direction to the team.
Data Scientist, Security Issue Management
US, WA, Seattle
Are you interested in building Agentic AI solutions that solve complex builder experience challenges with significant global impact? The Security Tooling team designs and builds high-performance AI systems using LLMs and machine learning that identify builder bottlenecks, automate security workflows, and optimize the software development lifecycle—empowering engineering teams worldwide to ship secure code faster while maintaining the highest security standards. As a Data Scientist on our Security Tool team, you will focus on building state-of-the-art ML models to enhance builder experience and productivity. You will identify builder bottlenecks and pain points across the software development lifecycle, design and apply experiments to study developer behavior, and measure the downstream impacts of security tooling on engineering velocity and code quality. Our team rewards curiosity while maintaining a laser-focus on bringing products to market that empower builders while maintaining security excellence. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the forefront of both academic and applied research in builder experience and security automation, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams. This role offers a unique opportunity to work on projects that could fundamentally transform how builders interact with security tools and how organizations balance security requirements with developer productivity. Key job responsibilities Design and run rigorous experiments to evaluate and improve security tooling performance, builder experience, and adoption across hundreds of thousands of builders, multiple security tools, and diverse business verticals. Lead the end-to-end lifecycle of data science and ML models — from research and experimentation through production launch — including defining success metrics, obtaining stakeholder sign-off, and managing rollout. Conduct online and offline analyses to measure the real-world impact of security tooling improvements beyond adoption metrics, including downstream effects on vulnerability resolution, builder productivity, and organizational security posture. Develop and deploy production-grade machine learning and statistical models using Python, SQL, and related tools to automate insights, detect patterns, and drive decision-making across STF's security tool ecosystem. Perform large-scale exploratory data analysis on builder feedback, ticket resolution, tool usage, and customer satisfaction data to uncover patterns, identify opportunities, and inform product and tooling decisions. Translate complex research findings into clear insights and recommendations for technical and non-technical stakeholders at all levels, including STF leadership metric reporting and customer satisfaction publications. Contribute to Amazon's scientific community and the broader research field through collaboration and publication in top-tier venues. A day in the life Morning - Review overnight pipeline health — nudge systems, ticket classification models, and adoption dashboards running as expected - Join daily standup with the SDI team to align on priorities and flag blockers - Dive into exploratory analysis — investigating a spike in unresolved tickets or segmenting builder feedback to understand adoption gaps Midday - Partner with security tool owners (e.g., Shepherd, Talos, Scorecard) to review experiment results — did the latest nudge improve resolution rates? - Translate findings into actionable recommendations for leadership reviews or WBR updates - Analyze CSAT survey data to surface emerging dissatisfaction themes Afternoon - Write production code — building features for the classification pipeline, optimizing SQL for the metrics scorecard, or iterating on a model for predicting resolution timelines - Collaborate with STF stakeholders to define success metrics for an upcoming model launch - Document findings, update trackers, and queue next steps About the team 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.
Senior Applied Scientist, Catalog System Services Science
US, WA, Seattle
At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product relevant information at unprecedented scale with Frontier Models and Agents. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity ranging from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Enriching product information requires sophisticated models that reason across text, images, and structured data, all while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Catalog System Services Science team is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval. In essence, translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging frontier models, and agentic architectures to enrich catalog information at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment, processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor, building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community - publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research
Machine Learning Scientist / Applied Scientist, EU Prime and Marketing Analytics & Science (PRIMAS)
ES, M, Madrid
Are you interested in changing how Amazon does marketing — moving beyond platform-optimized broad reach to campaigns that find the right customer, at the right moment, using Amazon's unmatched 1P data? We are seeking an Applied Scientist to join PRIMAS (Prime & Marketing Analytics and Science). In this role, you will design and run the experiments that answer the foundational question for EU marketing: does adding 1P audience signal on top of Value-Based Optimization (VBO) improve marketing efficiency — and if so, for which customer cohorts, on which surfaces, and at what scale? Amazon's current marketing model is largely platform-led: we set objectives and let platforms optimize toward conversion. This approach works well for broad acquisition but systematically underserves lifecycle goals — it cannot distinguish between a Bargain Hunter who will never pay full price and a high-potential customer one nudge away from becoming a Prime member. This role sits at the center of changing that. You will build the 1P audiences, design the experiments that test them, and generate the evidence that guides how Amazon allocates hundreds of millions in marketing spend. Year 1 is an experimentation year. You will deploy 1P audiences across multiple surfaces and channels — Meta, Google, Amazon Display Ads — and measure incrementally against VBO baselines. The goal is not to replace platform optimization but to understand when and where the combination of 1P signal + VBO outperforms VBO alone, and to build the experimental infrastructure that makes this learning scalable. Key job responsibilities 1P Audience Development & Experimentation: - Build and validate 1P audience segments from Amazon behavioral, transactional, and lifecycle data - Design experiments that isolate the incremental effect of 1P audience signal over platform VBO baselines - Deploy audiences across activation surfaces and establish measurement standards that make cross-surface comparison valid Causal Measurement & Incrementality: - Apply causal inference methods to measure the true incremental lift of audience-based targeting vs. VBO - Develop power analysis frameworks and guardrails that enable rapid experimentation without underpowered or conflated tests - Deliver optimization recommendations grounded in experimental evidence: which cohorts respond, which surfaces deliver, which creative strategies drive behavior change Scaling the Learning: - Build reusable audience and measurement frameworks that can be deployed across campaigns and channels — year 1 experiments should produce infrastructure, not one-off analyses - Document experimental learnings in a way that informs both the 2026 roadmap and the business case for investing further in 1P audience capabilities in 2027+ - Partner with engineering and PMT to translate validated audience prototypes into production-ready solutions that scale beyond the experimentation phase About the team The PRIMAS team, is part of a larger tech tech team of 100+ people called WIMSI (WW Integrated Marketing Systems and Intelligence). WIMSI core mission is to accelerate marketing technology capabilities that enable de-averaged customer experiences across the marketing funnel: awareness, consideration, and conversion.
Psychometrician, AWS Training & Certification
US, VA, Arlington
The AWS Certification team is seeking a Psychometrician with experience working with criterion-referenced assessment programs to support a large global AWS Certification and Credentialing program. In this role, you will support all psychometric aspects of exam development and operation, including job analyses, standard setting, automated test assembly, item and test analyses, optimal item bank design, quality assurance, and project planning. You will work closely with a team of psychometricians, subject matter experts, certification exam program managers, publishing, delivery, security, and product management teams to support ongoing analyses of exam and credential data. To be successful in this position, you must be highly motivated, creative, detail oriented, and a self-starter who is able to think big, execute, ensure high quality, yet stay focused on the details. Key job responsibilities • Conduct Job Task Analysis (JTA) workshops and post-JTA survey analyses to define the blueprint and test specifications for new certifications or updates to existing certifications • Conduct standard setting studies to set the passing score for exams and credentials • Run item analysis to evaluate quality and performance of exam items • Use automated test assembly procedures to assemble forms or item pools • Work with content development to track item bank trends and optimize the health of item banks • Support the development of a cloud-based analytics and reporting system • Partake in development and performance analysis of credentials • Interpret and clearly communicate the results of analyses to stakeholders through written and oral reports • Follow the accreditation standards set by ISO/IEC:2012 17024 and the National Council for Certifying Agencies (NCCA) as they relate to valid psychometric practices • Contribute to the development and execution of the strategic goals regarding the AWS certification and credentialing program. • Consult with leadership, internal staff, external consultants, and industry leaders regarding advancement of current offerings
Applied Scientist, Sponsored Products Off-Search Auction
US, WA, Seattle
The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art 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. Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond!
Wireless Science Manager, Device Connectivity
US, CA, Sunnyvale
Come join the Device connectivity team in building the next generation of innovative wireless solution that create a magical experience on our products and services. We actively engage in strategic initiatives, foster partnerships with industry and academia, leverage foundational artificial intelligence and large language models to stay at the forefront of the technological advancements. We are seeking an experienced Applied Science Manager to lead and grow a team of applied scientists who are pushing the boundaries of AI/ML in wireless connectivity and sensing. In this role, you will combine deep technical expertise with strong people leadership to drive scientific innovation that directly impacts millions of customers worldwide. Key job responsibilities As a Applied Science Manager in the team, you will: Build, mentor, and develop a high-performing team of applied scientists, setting the technical bar through code reviews, design reviews, and hands-on contributions while fostering a culture of scientific excellence, innovation, and operational rigor. Define and drive the AI/ML science roadmap for wireless solutions by developing a deep understanding of Amazon's Devices and Services offerings, translating complex business problems into well-defined scientific challenges, identifying high-risk and high-impact technical directions, and guiding your team to deliver them from conception through production. Collaborate cross-functionally with engineering, product, and business partners to drive ML development from research through optimization and onto production devices, aligning science investments with product goals while meeting on-device performance, latency, and resource constraints. Balance exploratory research with production delivery timelines, ensuring the team maintains scientific rigor while meeting business commitments. Represent the team's AI innovations to both internal leadership and the external scientific community through leadership reviews, publications, patents, and conference presentations, providing clear articulation of science strategy, progress, and impact. About the team About the team Device Connectivity team is empowering possibilities through wireless innovation on our devices and through services, our vision is to design and develop transformative products and services that consistently exceed our customers' expectations.
Data Scientist, Demand Forecasting
US, WA, Bellevue
What does it take to build a foundation model that can forecast demand for hundreds of millions of products — including ones that have never been sold before? At Amazon, our Demand Forecasting team is tackling one of the most ambitious challenges in applied time series research: building large-scale foundation models that generalize across an enormous and diverse catalog of products, geographies, and business contexts. This is not incremental modeling work. We are redefining what's possible in demand forecasting. Our team operates at a scale that is unmatched in industry. We run experiments across millions of products simultaneously, pushing the boundaries of what foundation models can learn from vast, heterogeneous time series data. We are also exploring novel data generation techniques that augment our already unprecedented dataset — opening new frontiers in model generalization and forecasting for products with limited or no sales history. The models you build here will ship to production and directly influence hundreds of millions of dollars in automated inventory decisions every week, labor plans for tens of thousands of employees, and Amazon's financial outlook. Beyond operational impact, this team contributes to the broader scientific community and advances the state of the art in time series foundation models. If you are a scientist who wants to work at the frontier of time series research, at a scale no academic lab or startup can match, and see your work deployed to real-world impact — this is the team for you. Key job responsibilities - Design and run rigorous experiments at scale to evaluate and improve foundation model performance across hundreds of millions of products, geographies, and business verticals - Lead the end-to-end lifecycle of forecasting models — from research and experimentation through production launch — including defining success metrics, obtaining stakeholder sign-off, and managing rollout - Conduct online and offline labs to measure the real-world impact of forecast improvements beyond accuracy, including downstream supply chain, inventory, and financial outcomes - Develop and deploy production-grade deep learning and statistical models using Python, Scala, SQL, and related tools - Perform large-scale exploratory data analysis to uncover patterns, identify opportunities, and inform model development - Translate complex research findings into clear insights and recommendations for technical and non-technical stakeholders at all levels - Contribute to Amazon's scientific community and the broader research field through collaboration and publication in top-tier venues A day in the life No two days look the same, but most will involve some combination of deep technical work, cross-functional collaboration, and scientific thinking at a scale you won't find anywhere else. You might start the morning reviewing the results of an experiment running across hundreds of millions of products — analyzing whether a new foundation model variant is improving generalization on cold-start items, or whether a novel data generation approach is meaningfully shifting forecast quality. You'll dig into the numbers, form a hypothesis, and design the next iteration. Later in the day, you could be in a stakeholder review, walking business and engineering partners through a set of launch metrics — explaining not just forecast accuracy, but the downstream supply chain and financial impact your model is driving. Getting a model to production at Amazon requires rigor: you'll define success criteria, run online and offline labs to validate real-world impact, and build the case for sign-off across technical and business stakeholders. You'll write code — Python, Scala, SQL — to process and analyze data at a scale most scientists never encounter. You'll collaborate closely with scientists, engineers, and business teams, and contribute to research that has a real chance of being published and advancing the field. The work is hard, the problems are unsolved, and the impact is immediate. If you want to do research that ships — this is where you do it. About the team The Demand Forecasting team sits at the heart of Amazon's supply chain, building the science that determines what products are available, when, and at what cost — for hundreds of millions of customers around the world. Our mission is to push the frontier of what's possible in large-scale time series forecasting, and to deploy that science where it creates real, measurable impact. We are a team of scientists who care deeply about both research rigor and real-world outcomes. We don't just publish — we ship. And we don't just ship — we measure, iterate, and raise the bar. Our work spans the full lifecycle: from foundational research and large-scale experimentation to production deployment and downstream impact measurement across supply chain, inventory, and financial planning.
Airspace Management Simulation Applied Scientist, Prime Air Flight Sciences High Fidelity Methods
US, WA, Seattle
Here at Amazon, we embrace our differences. We are committed to furthering our culture of diversity and inclusion of our teams within the organization. How do you get items to customers quickly, cost-effectively, and—most importantly—safely, in less than an hour? And how do you do it in a way that can scale? Our teams of hundreds of scientists, engineers, aerospace professionals, and futurists have been working hard to do just that! We are delivering to customers, and are excited for what’s to come. Check out more information about Prime Air on the About Amazon blog (https://www.aboutamazon.com/news/transportation/amazon-prime-air-delivery-drone-reveal-photos). If you are seeking an iterative environment where you can drive innovation, apply state-of-the-art technologies to solve real world delivery challenges, and provide benefits to customers, Prime Air is the place for you. Come work on the Amazon Prime Air Team! We're looking for a Research Scientist with a background in developing simulations for traffic management algorithms, including expert knowledge in strategic deconfliction, tactical deconfliction, or detect-and-avoid systems. Managing a large number of concurrent autonomous drone flights that share airspace with other autonomous or manned aircraft is a challenging problem. Be part of the team building simulation tools and algorithms to solve this at scale. This role will contribute to a portfolio of simulation tools managing concurrent airspace traffic for aviation systems. This will include developing new methodologies in the areas of conflict detection and resolution, as well as developing related software systems that will be used in operation to enable package delivery at scale. The ideal candidate is comfortable with risk-taking and ambiguity and able to build consensus on critical, controversial technical decisions. If you enjoy the process of solving real-world problems that haven’t been solved at scale anywhere before, Prime Air is right for you. Along the way, we guarantee you’ll get opportunities to be a disruptor, prolific innovator, and a reputed problem solver and directly impact Amazon’s customers worldwide. Key job responsibilities The primary focus of this role will be on modeling traffic management frameworks that use a layered conflict detection and resolution strategy to ensure safe and efficient flight operations. This will include developing fundamental simulation infrastructure code, including discrete event simulation tooling. In addition, it will involve developing expert knowledge of the layers of mitigation and conducting in-depth scientific research on alternative solutions for conflict resolution. The candidate will contribute to significant and impactful systems that will provide value for Amazon customers and will drive these projects from the concept stage through development. This role will include substantial software development in prototyping and production environments.
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