Careers

Alexa Connections is on a mission to become the world's most trusted communication agent — spanning calls, text, email, and the ever-expanding surfaces where people connect. We're building intelligence that keeps customers connected effortlessly while putting their trust and privacy first. As an Applied Scientist, you'll help build the smartest communications agent for people — an agent that understands intent, context, and relationships, and that acts on a customer's behalf to make every interaction feel effortless and genuinely helpful Key job responsibilities You'll research, develop, and deploy the next generation of on-device AI for communication, bringing small language models, agentic capabilities, and generative intelligence to the edge so that customers get responsive, and private experiences without relying on the cloud. In practice, that means designing on-device arbitration systems that intelligently route between local and cloud inference in real time, applying modern compression techniques like quantization, distillation, and low-rank adaptation to fit frontier-class capabilities into tight memory and latency budgets, and optimizing inference across on-device accelerators such as NPUs and mobile GPUs. You'll build privacy-first personalization that learns and adapts without user data ever leaving the device, and contribute to the science of edge communication intelligence, intent understanding, contact ranking, proactive connectivity, and conversational reasoning, all running locally. Throughout, you'll collaborate closely with hardware, systems, and product teams to co-design models with the target device in mind, and translate research advances into production features that push the state of the art in on-device AI. About the team We're building up the Alexa Connections science team, so you'll join early and grow alongside it. You'll work closely with experienced scientists and engineers invested in your development, with plenty of mentorship, room to broaden your skills, and the chance to own meaningful problems as you grow. It's a supportive place to do impactful work early in your career, shaping how millions of customers connect every day.

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

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

The Worldwide Design Engineering (WWDE) organization delivers innovative, effective and efficient engineering solutions that continually improve our customers’ experience. WWDE optimizes designs throughout the entire Amazon value chain providing overall fulfillment solutions from order receipt to last mile delivery. We are seeking a Sr. Simulation Scientist to assist in designing and optimizing the fulfillment network concepts and process improvement solutions using discrete event simulations for our World Wide Design Engineering Team. Successful candidates will be technical expert and natural self-starter who have the drive to apply simulation and optimization tools to solve complex flow and buffer challenges during the development of next generation fulfillment solutions. The Simulation Scientist is expected to deep dive into complex problems and drive relentlessly towards innovative solutions working with cross functional teams. Be comfortable interfacing and influencing various functional teams and individuals at all levels of the organization in order to be successful. Lead strategic modelling and simulation projects related to system simulation, optimization, AI powered simulation, advanced simulation tools development, coding simulation algorithms to drive process design decisions. Key job responsibilities - You influence the scientific strategy across multiple teams in your business area. You support go/no-go decisions, build consensus, and assist leaders in making trade-offs. You proactively clarify ambiguous problems, scientific deficiencies, and where your team’s solutions may bottleneck innovation for other teams. - Lead the design, implementation, and delivery of the simulation science solutions to perform system of systems discrete event simulations for significantly complex operational processes that have a long-term impact on a product, business, or function using FlexSim, Demo 3D, AnyLogic or any other Discrete Event Simulation (DES) software packages, AI powered simulation tools, PLC/IPC Emulations, Robotic system simulation and emulation - Lead strategic system modelling and simulation projects using DES tools, software, PLC/IPC emulations, to drive process design decisions - Be an exemplary practitioner in simulation data science discipline to establish best practices and simplify problems to develop discrete event simulations faster with higher standards - Identify and tackle intrinsically hard process flow simulation problems (e.g., highly complex, ambiguous, undefined, with less existing structure, or having significant business risk or potential for significant impact - Deliver artifacts that set the standard in the organization for excellence, from process flow control algorithm design to validation to implementations to technical documents using simulations - Be a pragmatic problem solver by applying judgment and simulation experience to balance cross-organization trade-offs between competing interests and effectively influence, negotiate, and communicate with internal and external business partners, contractors and vendors for multiple simulation projects - Provide simulation data and measurements that influence the business strategy of an organization. Write effective white papers and artifacts while documenting your approach, simulation outcomes, recommendations, and arguments - Lead and actively participate in reviews of simulation data science solutions. You bring clarity to complexity, probe assumptions, illuminate pitfalls, and foster shared understanding within simulation data science discipline - Pay a significant role in the career development of others, actively mentoring and educating the larger simulation data science community on trends, technologies, and best practices - Use advanced statistical /simulation tools and develop codes (python or another object oriented language) for research , simulation, and modeling algorithms - Lead and coordinate simulation efforts between internal teams and outside vendors to develop optimal solutions for the network, including equipment specification, material flow control logic, process design, and site layout - Deliver results according to project schedules and quality A day in the life The day-to-day activities include challenging and problem solving scenario with fun filled environment working with talented and friendly team members. The stakeholders includes Worldwide Design Engineering org verticals, Fulfillment Technology and Robotics team members. The team solve problems related to critical design automation of Material handling equipment and technology design solutions. Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan 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 WWDE Simulation Team’s mission is to apply advanced simulation tools and techniques to drive process flow design, optimization, and improvement for the Amazon Fulfillment Network. We take ownership and pride to come up with simulation based solutions to drive the designs and earn the customer trust. We always strive to invent new ways to simulate and optimize the design and to invent design solutions. We help design and operation engineers to make simulation driven decisions by producing detailed math models and generating key performance metrics.

Come be a part of a rapidly expanding $35 billion dollar global business. At Amazon Business, a fast-growing startup passionate about building solutions, we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. The Opportunity This is one of the most consequential science leadership roles in Amazon Business. As Applied Science Manager, you will directly influence over $40B in annual revenue by owning the science strategy that powers how we acquire, engage, monetize, and retain millions of business customers worldwide. Your models won't sit in notebooks. They will run in production, shaping real-time decisions across the full customer lifecycle for one of Amazon's fastest-growing businesses. You will operate at the intersection of Business Prime, Marketing Science, and Customer Acquisition three of the highest-visibility, highest-impact charters in the organization. Your work will be presented to VPs and SVPs, inform multi-billion-dollar investment decisions, and fundamentally reshape how Amazon Business goes to market. This is not an incremental optimization role. This is a build-the-future role where your science roadmap becomes the business strategy. What You'll Own Team Leadership & Talent Development: Build and lead a world-class team of applied scientists and research scientists. Set the technical vision, define the multi-year science roadmap, and create an environment where scientists ship models that move the needle on $40B+ in revenue. You will be the bar-raiser who attracts top talent and develops the next generation of science leaders at Amazon. Customer Lifecycle Intelligence ($40B+ Revenue Impact): Own the predictive modeling stack that powers customer identification, targeting, spend behavior forecasting, churn propensity, and declining engagement detection across every Business Prime segment. Your models will determine which customers we invest in, how we invest, and when directly impacting billions in customer lifetime value. Marketing Science & Measurement (Cross-Functional, Multi-Billion Dollar Allocation): Build the causal inference and attribution frameworks that determine how Amazon Business allocates hundreds of millions in marketing investment across paid, owned, and earned channels globally. You will be the single point of science truth for incrementality measurement, marketing mix modeling, and ROI optimization partnering with Marketing, Finance, CPS, and international teams to ensure every dollar drives maximum impact. Customer Acquisition Engine: Lead the science that accelerates new Business Prime member acquisition at scale. Build propensity models, audience optimization systems, and channel effectiveness frameworks that feed directly into acquisition engines serving 10+ global markets. Your work will be the difference between linear growth and exponential growth. Benefits Adoption & the "Aha Moment": Build behavioral segmentation and recommendation systems that identify, for each customer vertical and organizational profile, the exact moment and feature combination that converts a trialist into a loyalist. This is the science of delight at scale. Trust & Safety at Scale: Develop fraud detection models, abuse identification systems, and eligibility classifiers that protect the integrity of the Business Prime ecosystem safeguarding billions in revenue from bad actors. Experimentation & Causal Inference (Organization-Wide): Establish the experimentation frameworks and causal inference methodologies used across the entire Amazon Business organization. Design experiments that measure incremental impact of product launches, pricing changes, and marketing interventions creating the scientific foundation for how leadership makes high-stakes decisions. Why This Role Is Different This is not a role where you optimize a feature in isolation. You will sit at the center of a complex, cross-functional operating model partnering daily with 7+ senior leaders (including Directors and VPs) across Business Prime, CPS Sales, Central Marketing, SSR, GTMO, Finance, and International teams. Your science will be the connective tissue that aligns these organizations around a shared, data-driven growth strategy. You will have direct senior leadership visibility — presenting findings, recommendations, and roadmaps to VP audiences regularly. The insights your team generates will shape OP1/OP2 planning, Board-level narratives, and multi-year investment theses. What We're Looking For The successful Applied Science Manager will have an bias for action in a startup environment, with people leadership skills, a proven ability to build and manage high-performing science teams, define and prioritize research agendas that map to business outcomes, and build methodology and tools that are statistically grounded. You influence product and business strategy through science translating complex analytical findings into crisp, actionable recommendations that senior leaders act on immediately. We are seeking someone who thrives in a fast-paced, high-energy, and fun work environment where we deliver value incrementally and frequently. You are a highly technical leader who knows your subject matter deeply, can elevate the technical bar of your team, and is energized by ambiguity and new problem spaces. You know how to deliver results and show a desire to develop yourself, your colleagues, and your career.

How does Amazon decide which fulfillment center ships your order, which truck carries it, and how to keep promises across hundreds of millions of packages daily? How does it decide how many trucks and how much labor are required to ship orders across the network? SCOT Fulfillment Optimization (FO) owns the optimization and forecasting science behind these decisions. We are seeking Applied Scientists to join the FO Science & Tech team in Barcelona (alternatively: Luxembourg or London) with a strong academic background in optimization, machine learning, and/or time-series forecasting. • You will design and build state-of-the-art machine learning and optimization models that power Amazon's fulfillment decisions at an unprecedented scale across two core scientific pillars: • Large-Scale Optimization and Planning: Designing planning systems for order assignment and resource utilization, while balancing multi-objective cost-speed tradeoffs to enable controllers to steer millions of shipments per hour optimally. • Demand Forecasting & Predictive ML: Developing time-series forecasts for customer demand, incorporating contextual information (weather, sales, order properties), and modeling uncertainty for core planning systems. Basic qualifications • PhD in Operations Research, Applied Mathematics, Computer Science, or related field (or equivalent experience) • Strong programming skills (Python preferred; experience with optimization solvers a plus) • Research experience in one or more: • Large-scale mathematical programming (LP, MIP, decomposition methods) • Combinatorial optimization (assignment, scheduling, network flows) • Multi-objective optimization and control • Large-scale time-series forecasting (GenAI models, probabilistic forecasting, uncertainty quantification) • Causal inference (spatiotemporal causal modeling, offline policy evaluation) Preferred qualifications • Experience building optimization systems that run in production at scale • Being comfortable with ambiguity and fast iteration cycles • Publications in relevant venues Key job responsibilities Design and implement optimization and forecasting models for large-scale fulfillment problems, from order assignment to network flow control. Build research prototypes end-to-end: from problem formulation through scalable implementation to production validation. Analyse complex tradeoffs (cost, speed, capacity, accuracy) and translate findings into actionable recommendations for leadership and operations teams. Collaborate with engineers to bring science solutions into production systems serving millions of customer orders daily. A day in the life You formulate an optimization or forecasting problem on a whiteboard with teammates, then prototype it in Python with real data by the afternoon. You run experiments against production-scale datasets, iterate on the model, and present results to stakeholders who will use them to make network decisions next week. Some days you dive deep into solver performance; other days you're explaining a Pareto frontier to an operations leader. You collaborate with large engineering and product teams to bring your solutions into systems serving millions of customers. Alongside fast-turnaround prototypes, you own long-term research bets, the kind that reshape how Amazon's fulfillment network operates at scale. Your work goes live. About the team SCOT Fulfillment Optimization Science & Tech (FO SnT) is the applied research team behind Amazon's fulfillment decision-making systems. We decide how orders get assigned to warehouses, how capacity is allocated across the network, and how cost and speed tradeoffs are managed in real time, at global scale. Our models influence billions of euros in annual operational spend. They protect sites from overload during peak, reduce transportation costs and CO2 emissions, and ensure customers receive their packages when promised. Leadership relies on our science to make investment decisions worth hundreds of millions. We are practitioners of large-scale optimization: MIP formulations, decomposition methods, approximation algorithms, and parallelisation. We use machine learning where it sharpens our decisions, including forecasting, learned heuristics, and multi-armed bandits. We pick the right tool for the problem, not the fashionable one. You will work alongside Senior and Principal scientists, and collaborate with Amazon Scholars and academic partners who bring frontier research into our applied problems. We code our prototypes to be production-ready and collaborate with large engineering teams to ship systems, not papers. Above all, we have fun solving hard real-world problems at real-world speed, failing, learning, and shipping along the way.

Our goal is to be Earth's most customer-centric company, where customers can find and discover anything they might want to buy online. With our years of experience creating delight by fulfilling products from electronics to everyday essentials, our next challenge is offering customers fresh and delicious groceries—from seasonal fruits to your favorite cheeses. Our mission is to create the most intuitive and seamless grocery shopping experience by combining customer knowledge with LLM-based techniques that can deliver timely and relevant recommendations that make grocery shopping easy. Are you passionate about applying cutting-edge machine learning to solve real-world customer problems? Do you want to build models that impact hundreds of millions of customers globally? This is a unique opportunity to innovate at the intersection of personalization science, large language models, and grocery shopping As an Applied Scientist on the Grocery Personalization team, you will develop novel machine learning models and features that transform how customers discover and purchase groceries. You'll work on problems ranging from LLM-based techniques for understanding customer preferences and product relationships, transformer-based models for intent prediction, to large-scale real-time ranking systems that personalize the entire grocery shopping journey. Key job responsibilities - Innovate new features and models that have huge impact on the customer experience. Help customers find the right grocery products and content throughout their journey. - Leverage the use of advanced LLM-based techniques to create customer grocery shopping experience at Amazon's scale - for all Amazon customers across all countries in realtime - Be able to operate on a multidisciplinary team across science, product, design, and engineering to see through ideas from inception, prototype, to launch in the hands of all Amazon's customers - Drive the science roadmap for the team About the team Amazon’s Personalization organization is a small, high-performing group that leverages Amazon’s expertise in machine learning, big data, and distributed systems to deliver the best shopping experiences for our customers. We work full stack, from foundational backend systems to future-forward user interfaces. Our team’s culture is centered on rapid prototyping, rigorous experimentation, and data-driven decision-making. We run hundreds of experiments each year and our work has revolutionized e-commerce with features such as “Customers Who Bought Also Bought” and “Recommended for You”. We care deeply about our customers, as well as the well-being and growth of our team members. Amazon’s internal surveys regularly recognize us as one of the best engineering organizations to work for in the company, with visible high-impact work, low operational load, respectful work-life balance, and continual opportunity to learn and grow. This is a track record we are proud of and will continue to uphold. We are looking for creative and innovative leaders with a similar penchant for deeply-technical problem solving and the ability to lead, mentor, and deliver while upholding Amazon’s leadership principles.

Our organization in Amazon Robotics builds robots that perform contact-rich manipulation safely and reliably in complex, unstructured environments, at Amazon scale. Our scientists and engineers push the boundaries of robotic manipulation to handle enormous object diversity, bringing deep expertise across planning, control, perception, and machine learning. We learn from real-world data at a scale that few teams in robotics can access. We are seeking an experienced Senior Applied Scientist to help guide a small team advancing reinforcement learning for manipulation. We are creating robots that learn how to push, flip, rearrange, and dexterously insert items with unparalleled robustness, speed, and reliability. Our goal is to deploy robots that will work across Amazon's global network and can handle the full diversity of items that Amazon sells. You will set the technical direction for how we learn these behaviors, from simulation training through reliable execution on physical robots, and you will demonstrate new manipulation capabilities on real hardware at scale. This team's mission reaches beyond any single product: to invent and apply manipulation capabilities that generalize to many future robotics applications. The robots our organization already deploys at scale give you a rare proving ground to collect data, run experiments, and get new policies onto real hardware faster than almost anywhere in the field. You will raise the bar for scientific rigor and engineering quality, and mentor other scientists as the team grows. Key job responsibilities - Set the technical direction for learning non-prehensile and contact-rich manipulation policies, from testing the latest advances in the field through demonstrated capability on hardware. - Oversee the development of reinforcement learning approaches that address the long tail of diverse, demanding manipulation conditions. - Own the path from simulation training to reliable, real-time execution on physical robots, making evidence based calls on where learned approaches should replace engineered ones. - Demonstrate new manipulation capabilities on real robots at scale, and turn one-off results into repeatable methods. - Establish the standards, evaluation practices, and data-informed improvement loops that the team builds on. - Mentor scientists and engineers, and raise the bar for applied science rigor and engineering quality. - Partner across control, perception, and hardware to integrate learned behaviors into working systems. - Represent Amazon in academia through publications and scientific presentations. A day in the life Amazon offers a full range of benefits that support 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

We are seeking an Applied Scientist to build production machine learning systems that solve complex business problems at scale. You will design, develop, and deploy ML solutions that directly impact millions of users and drive strategic decision-making across the organization. In this role, you will work on challenging problems spanning predictive modeling, natural language processing, recommendation systems, and generative AI applications. You will collaborate with cross-functional teams to translate ambiguous business challenges into rigorous technical solutions. Key job responsibilities - Design and deploy large-scale machine learning systems in production environments - Develop innovative ML solutions using state-of-the-art techniques including deep learning, NLP, and generative AI - Create ML solutions that personalize manager on-boarding and development experiences — identifying individual capability gaps, recommending tailored learning pathways, and measuring development effectiveness across diverse manager populations and contexts - Partner to build causal inference models and experimental frameworks to measure impact - Collaborate with product managers, engineers, and business leaders to define technical roadmaps - Communicate complex technical concepts to diverse audiences, from technical peers to senior leadership About the team The People eXperience and Technology Central Science Team (PXTCS) uses economics, applied science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.

The Amazon Center for Quantum Computing (CQC) team is looking for a passionate, talented, and inventive Research Engineer specializing in hardware design for cryogenic environments. The ideal candidate should have expertise in 3D CAD (SolidWorks), thermal and structural FEA (Ansys/COMSOL), hardware design for cryogenic applications, design for manufacturing, and mechanical engineering principles. The candidate must have demonstrated experience driving designs through full product development cycles (requirements, conceptual design, detailed design, manufacturing, integration, and testing). Candidates must also have a strong background in both cryogenic mechanical engineering theory and implementation. Working effectively within a cross-functional team environment is critical. Key job responsibilities The CQC collaborates across teams and projects to offer state-of-the-art, cost-effective solutions for scaling the signal delivery to quantum processor systems at cryogenic temperatures. Equally important is the ability to scale the thermal performance and improve EMI mitigation of the cryogenic environment. You will work on the following: - High density novel packaging solutions for quantum processor units - Cryogenic mechanical design for novel cryogenic signal conditioning sub-assemblies - Cryogenic mechanical design for signal delivery systems - Simulation-driven designs (shielding, filtering, etc.) to reduce sources of EMI within the qubit environment. - Own end-to-end product development through requirements, design reports, design reviews, assembly/testing documentation, and final delivery A day in the life As you design and implement cryogenic hardware solutions, from requirements definition to deployment, you will also: - Participate in requirements, design, and test reviews and communicate with internal stakeholders - Work cross-functionally to help drive decisions using your unique technical background and skill set - Refine and define standards and processes for operational excellence - Work in a high-paced, startup-like environment where you are provided the resources to innovate quickly About the team The Amazon Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. Inclusive Team Culture Here at Amazon, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred 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. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Export Control Requirement Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility.