Careers

Alexa Smart Home is re-imagining how customers interact with and control their smart home devices. We process hundreds of millions of customer actions weekly across diverse device types, manufacturers, and APIs — and we're looking for a Data Scientist II to help us drive customer experience improvements, uncover insights, and build intelligent AI/ML-powered solutions at massive scale. As a Data Scientist on the Alexa Smart Home team, you will develop machine learning models, analytical frameworks and Gen-AI powered solutions that improve product quality, inform strategic decisions, and enable proactive detection of customer experience issues across the smart home ecosystem. You will work with large-scale behavioral and interaction datasets, design experimentation frameworks, and partner with engineering, product, and science teams to deliver data-driven solutions that directly impact millions of Alexa customers worldwide. This role offers the opportunity to work on cutting-edge problems — from building AI/ML solutions that predict and prevent customer-facing issues, to designing analytics systems that surface actionable insights for improving smart home reliability. Key job responsibilities • Design and implement AI/ML models, anomaly detection systems, and statistical frameworks to analyze customer interactions, identify emerging issues, and drive measurable improvements across the smart home ecosystem. • Own the full lifecycle of model development — from exploratory analysis and feature engineering through deployment, monitoring, and continuous improvement. • Partner with product, engineering, and science teams to translate complex business and technical problems into scalable, production-ready data science solutions. • Design and run rigorous experiments (A/B testing, causal analysis) to measure the impact of product and quality improvements and guide strategic decisions. • Develop scalable analytical solutions for impact measurement, KPI development, metric integrity validation, and long-term business monitoring. • Explore and apply GenAI and LLM-based approaches to accelerate insight generation, automate analytical workflows, and solve novel smart home challenges. • Communicate findings and recommendations to senior leadership through clear data narratives, written documents, and presentations that influence product and business strategy. • Collaborate with colleagues across science and engineering disciplines for fast turnaround proof-of-concept prototyping at scale. • Contribute to the broader science community by mentoring analysts, improving data workflows and tooling, and sharing technical work in internal and external forums. A day in the life • Deep dive into smart home interaction metrics — analyzing trends, failure categories, and model performance across dashboards and datasets. • Developing code: building packages in Python, writing SQL queries, and deploying ML solutions that Smart Home teams consume. • Leading or joining working sessions with Product Managers to refine problem statements for new initiatives. • Exploring new features and model architectures, leveraging AWS services and LLMs to solve smart home-specific challenges. • Partnering with engineers to align on solution designs and ensure data science insights translate into robust, production-ready systems. • Owning or co-owning WBR/MBR documents reviewed with Smart Home leadership. About the team The Alexa Smart Home Decision Sciences team provides the data and analytical foundation that powers insights across our rapidly growing smart home ecosystem. We work with data from millions of connected devices, customer interactions, and partner integrations to help shape product strategy, improve customer experience, and drive business growth. We operate in a collaborative environment where innovation and customer obsession are at the core of everything we do.

Amazon's Middle Mile Science group is looking for an Applied Scientist to build machine learning and optimization models for large-scale transportation planning systems. This includes the development of dynamic pricing and network planning models to improve operations and services for our external freight customers. The Middle Mile Science group develops optimization and machine learning systems that power Amazon's freight transportation network, from network design and pricing to real-time load planning and capacity utilization. The scale of Amazon's fulfillment operations requires robust transportation networks that minimize cost while meeting all customer deadlines. Real-time execution depends on state-of-the-art optimization and artificial intelligence to coordinate thousands of operators and drivers. This includes shipper-facing and carrier-facing marketplace algorithms as well as network planning and optimization tools. Amazon often finds that existing techniques do not match our unique business needs, driving the innovation of new approaches and algorithms. As an Applied Scientist focusing on external freight within middle mile transportation, you will work closely with business leaders, engineers, and fellow scientists to design and build scalable products operating across multiple transportation modes. You will create experiments and prototypes of new machine learning and optimization applications, present research findings to senior leadership, and implement your models within production systems. You will write production-quality code designed for scalability and maintainability, and make decisions that affect how we build and integrate algorithms across our product portfolio. About the team Our Middle Mile Marketplace Science team builds the algorithms for Amazon’s rapidly growing freight marketplace. Amazon contracts with 3P shippers and a network of independent carriers, using a mix of contract structures with varying service and risk profiles. Our work focuses on mechanisms and learning algorithms to optimize pricing and matching in this complex marketplace, and continually improve the experience for carriers and shippers. This is an area with many challenging problems and a huge business impact for Amazon!

The Amazon Web Services (AWS) Center for Quantum Computing in Pasadena, CA, is looking to hire an Applied Scientist on the Device Team, focused on packaging and environmental R&D for quantum devices. You will join a multi-disciplinary team of theoretical and experimental physicists, materials scientists, and hardware and software engineers working at the forefront of quantum computing. You should have deep expertise in the design, modeling, and testing of packaging and off-chip environments for microelectronic or quantum devices, with a strong understanding of how packaging and environmental factors impact qubit performance at cryogenic operating conditions. Candidates with a track record of original scientific contributions in packaging, microwave engineering, or quantum cryogenic hardware will be preferred. We are looking for candidates with strong scientific and engineering principles, resourcefulness and a bias for action, superior problem solving, and excellent communication skills. Working effectively within a team environment is essential. As an applied scientist at CQC, you will be expected to drive packaging and environmental R&D from concept through hardware demonstration and stay abreast of advances in quantum hardware packaging and cryogenic measurement science. Key job responsibilities In this role, you will develop packaging and environmental R&D for superconducting quantum devices. Your responsibilities will span four interconnected areas: Packaging R&D: Design, model, and test packaging solutions for superconducting quantum devices. Build electromagnetic and mechanical models to predict and optimize packaging performance, and characterize packaging at cryogenic temperatures. Environmental Test Standards: Design and develop test standards for characterizing the DC and microwave qubit environment, ensuring rigorous and reproducible assessment of off-chip environmental contributions to qubit decoherence and loss. Measurement Automation: Automate data collection for environmental test standards and develop automated reporting pipelines to enable efficient, scalable characterization across devices and configurations. Environmental Improvement: Collaborate with hardware engineers, PCB designers, and circuit designers to identify and mitigate the most significant sources of environmental noise and integration uncertainty, driving improvements in qubit performance through off-chip environment optimization. You will contribute to multi-year roadmap planning for packaging and environmental capabilities aligned with CQC objectives, and help establish the scientific foundation for this capability area within the Device team. About the team About the team Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon's Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS's services and features apart in the industry. As a member of the UC organization, you'll support the development and management of Compute, Database, Storage, Internet of Things (IoT), Platform, and Productivity Apps services in AWS. Within AWS UC, Amazon Dedicated Cloud (ADC) roles engage with AWS customers who require specialized security solutions for their cloud services. Inclusive Team Culture Here at AWS, 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 (diversity) conferences, inspire us to never stop embracing our uniqueness. Diverse Experiences AWS 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. 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.

Amazon Industrial Robotics is seeking exceptional applied science talent to develop AI and machine learning systems that will enable the next generation of advanced manufacturing capabilities at unprecedented scale. We're building revolutionary software infrastructure that combines cutting-edge AI, large-scale optimization, and advanced manufacturing processes to create adaptive production control systems. As a Senior Applied Scientist, you will develop and improve machine learning systems that enable real-time manufacturing flow decisions. You will leverage state-of-the-art optimization and ML techniques, evaluate them against representative manufacturing scenarios, and adapt them to meet the robustness, reliability, and performance needs of production environments. You will invent new algorithms where gaps exist. You'll collaborate closely with software engineering, manufacturing engineering, robotics simulation, and operations teams, and your outputs will directly power the systems that determine what to build next, where to allocate resources, and how to maximize throughput. The ideal candidate brings deep expertise in optimization and machine learning, with a proven track record of delivering scientifically complex solutions into production. You are hands-on, writing significant portions of critical-path scientific code while driving your team's scientific agenda. If you're passionate about inventing the intelligent manufacturing systems of tomorrow rather than optimizing those of today, this role offers the chance to make a lasting impact on the future of automation. Key job responsibilities - Identify and devise new scientific approaches for constraint identification, dispatch optimization, WIP release control, and predictive flow intelligence when the problem is ill-defined and new methodologies need to be invented - Lead the design, implementation, and successful delivery of scientifically complex solutions for real-time manufacturing flow optimization in production - Design and build ML models and optimization algorithms including constraint prediction, starvation risk forecasting, and dispatch optimization - Write a significant portion of critical-path scientific code with solutions that are inventive, maintainable, scalable, and extensible - Execute rapid, rigorous experimentation with reproducible results, closing the gap between simulation and real manufacturing environments - Build evaluation benchmarks that measure model performance against manufacturing outcomes including constraint utilization and throughput rather than traditional ML metrics alone - Influence your team's science and business strategy through insightful contributions to roadmaps, goals, and priorities - Partner with manufacturing engineering, robotics simulation, and applied intelligence teams to ensure scientific approaches are grounded in operational reality - Drive your team's scientific agenda and role model publishing of research results at peer-reviewed venues when appropriate and not precluded by business considerations - Actively participate in hiring and mentor other scientists, improving their skills and ability to deliver - Write clear narratives and documentation describing scientific solutions and design choices

Are you interested in shaping the future of Advertising and B2B Sales? We are a growing science and engineering team with an exciting charter and need your passion, innovative thinking, and creativity to help take our products to new heights. Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products are strategically important to our businesses driving long term growth. We break fresh ground in product and technical innovations every day! Within the Advertising Sales organization, we are building a central AI/ML team and are seeking top science talent to build new, science-backed services to drive success for our customers. Our goal is to transform the way account teams operate by creating actionable insights and recommendations they can share with their advertising accounts, and ingesting Generative AI throughout their end-to-end workflows to improve their work efficiency. As a part of our team, you will bring deep expertise in Generative AI and quantitative modeling (forecasting, recommender systems, reinforcement learning, causal inferencing or generative artificial intelligence) to build and refine models that can be implemented in production. You will contribute to chart new courses with our ad sales support technologies, and you have the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers. You will be part of a team of fellow scientists and engineers taking on iterative approaches to tackle big, long-term problems. Why you will love this opportunity: Amazon has invested heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon's Retail and Marketplace businesses. We deliver billions of ads impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences; this is your opportunity to work within the fastest growing businesses across all of Amazon! Define a long-term scientific vision for our advertising sales business, driven from our customers' needs, translating that direction into specific plans for scientists, engineers and product teams. This role combines scientific leadership, organizational ability, technical strength, product focus, and business understanding. Key job responsibilities - Conceptualize and lead state-of-the-art research on new Machine Learning and Generative Artificial Intelligence solutions to optimize all aspects of the Ad Sales business - Guide the technical approach for the design and implementation of successful models and algorithms in support of expert cross-functional teams delivering on demanding projects - Conduct deep data analysis to derive insights to the business, and identify gaps and new opportunities - Run regular A/B experiments, gather data, and perform statistical analysis - Work closely with software engineers to deliver end-to-end solutions into production - Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving About the team Sales AI is a central science and engineering organization within Amazon Advertising Sales that powers selling motions and account team workflows via state-of-the-art of AI/ML services. Sales AI is investing in a range of sales intelligence models, including the development of advertiser insights, recommendations and Generative AI-powered applications throughout account team workflows.

About the Organization AWS is on a mission to transform how businesses operate by delivering intelligent, cloud-powered applications. Our Applied AI Solutions organization accelerates customer success through intuitive, differentiated technology that solves enduring business challenges — blending vision with real-world expertise to build turnkey solutions that are easy to adopt and built to scale. Within this organization, we are building the next generation of secure, intelligent workspaces — environments purpose-built for human-AI collaboration at enterprise scale. The Role We are looking for a Senior Applied Scientist to build the predictive intelligence powering capacity management for our workspace platform — developing machine learning systems that forecast demand, optimize resource allocation, and enable cost-efficient scaling at massive scale. This role requires someone who can translate complex business requirements into production ML systems, designing algorithms that balance customer experience with operational efficiency across a large and diverse fleet of capacity pools. What You'll Do • Architect and implement ML foundations for capacity management, building models that continuously learn and optimize across multiple dimensions including geography, platform, and instance type. • Develop demand forecasting systems that anticipate usage patterns hours to weeks in advance, enabling proactive capacity decisions at scale. • Build anomaly detection systems that identify capacity risks before they impact customers, improving service reliability and resilience. • Design optimization algorithms that make high-frequency, automated decisions balancing two critical forces: ensuring a flawless customer experience where every operation succeeds, while maximizing cost efficiency through intelligent resource utilization and placement strategies. • Apply advanced ML techniques including time-series forecasting, reinforcement learning, and causal inference to measure the true impact of capacity decisions on customer experience and cost. • Engineer features from large-scale datasets spanning usage signals, session patterns, and infrastructure telemetry — capturing complex interactions across diverse workload types. • Partner closely with product and engineering teams to translate product vision into scientific solutions, deploying models that process millions of predictions daily with sub-second latency requirements. What Success Looks Like • ML systems that enable the service to remain profitable while capacity-related customer impacts become increasingly rare. • Measurable business impact through reduced capacity waste, improved cost efficiency, and elimination of customer-impacting capacity events. • Scientific innovation that unlocks significant cost savings through predictive resource commitment strategies and intelligent automated decision-making. • Models that maintain the safety margins needed to absorb demand volatility without customer impact. • An ML foundation that enables distributed, autonomous decision-making while maintaining consistent quality at scale. What We're Looking For • Deep expertise in machine learning, with hands-on experience building and deploying production ML systems. • Strong background in time-series forecasting and handling demand volatility across diverse workload patterns. • Experience with reinforcement learning for dynamic resource allocation and causal inference for impact measurement. • Ability to work with large-scale datasets and engineer features that capture complex, multi-dimensional interactions. • Strong systems thinking — able to design end-to-end ML pipelines that operate reliably at scale with low-latency requirements. • Excellent collaboration skills — comfortable partnering with product managers, engineers, and business stakeholders to drive scientific solutions from concept to production. • A track record of measurable business impact through applied ML research and deployment. Key job responsibilities 1/ Work independently on ambiguous problems: Independently work on capacity forecasting problems that are not well defined or structured, identifying and framing new research challenges associated with broad problem areas, delivering with limited guidance. 2/ Influence across multiple teams: Drive alignment on ML approaches and capacity strategies across product, engineering, and operations teams. Actively mentor and develop others on the team. 3/ Deliver end-to-end production solutions: Develop and deliver complete solutions including scientific contributions that are deployed in production. Make technical trade-offs balancing long-term invention with short-term delivery Lead on medium-to-large business problems: Take the lead on capacity management challenges that deliver significant benefits to customers and the business through improved forecasting accuracy and cost optimization. 4/ Drive team scientific agenda: Shape the direction of ML research for capacity management, proposing new approaches and securing buy-in from leadership. 5/ Set the example: Your solutions, code, designs, and scientific artifacts should set a great example to others.

Amazon Quick Suite is an enterprise AI platform that transforms how organizations work with their data and knowledge. Combining generative AI-powered search, deep research capabilities, intelligent agents and automations, and comprehensive business intelligence, Quick Suite serves tens of thousands of users. Our platform processes thousands of queries monthly, helping teams make faster, data-driven decisions while maintaining enterprise-grade security and governance. From natural language interactions with complex datasets to automated workflows and custom AI agents, Quick Suite is redefining workplace productivity at unprecedented scale. We are seeking a Data Scientist II to join our Quick Data team, focusing on evaluation and benchmarking data development for Quick Suite features. Our mission is to engineer high-quality datasets that are essential to the success of Amazon Quick Suite. From human evaluations and Responsible AI safeguards to Retrieval-Augmented Generation and beyond, our work ensures that Generative AI is enterprise-ready, safe, and effective for users at scale. As part of our diverse team—including data scientists, engineers, language engineers, linguists, and program managers—you will collaborate closely with science, engineering, and product teams. We are driven by customer obsession and a commitment to excellence. Key job responsibilities In this role, you will leverage data-centric AI principles to assess the impact of data on model performance and the broader machine learning pipeline. You will apply Generative AI techniques to evaluate how well our data represents human language and conduct experiments to measure downstream interactions. Specific responsibilities include: * Design and develop comprehensive evaluation and benchmarking datasets for Quick Suite AI-powered features * Leverage LLMs for synthetic data corpora generation; data evaluation and quality assessment using LLM-as-a-judge settings * Create ground truth datasets with high-quality question-answer pairs across diverse domains and use cases * Lead human annotation initiatives and model evaluation audits to ensure data quality and relevance * Develop and refine annotation guidelines and quality frameworks for evaluation tasks * Conduct statistical analysis to measure model performance, identify failure patterns, and guide improvement strategies * Collaborate with ML scientists and engineers to translate evaluation insights into actionable product improvements * Build scalable data pipelines and tools to support continuous evaluation and benchmarking efforts * Contribute to Responsible AI initiatives by developing safety and fairness evaluation datasets About the team Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, 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. 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. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.

Amazon Health Services (One Medical) About Us: At Health AI, we're revolutionizing healthcare delivery through innovative AI-enabled solutions. As part of Amazon Health Services and One Medical, we're on a mission to make quality healthcare more accessible while improving patient outcomes. Our work directly impacts millions of lives by empowering patients and enabling healthcare providers to deliver more meaningful care. Role Overview: We're seeking an Applied Scientist to join our dynamic team in building state of the art AI/ML solutions for healthcare. This role offers a unique opportunity to work at the intersection of artificial intelligence and healthcare, developing solutions that will shape the future of medical services delivery. Key job responsibilities • Lead end-to-end development of AI/ML solutions for Amazon Health organization, including Amazon Pharmacy and One Medical • Research, design, and implement state-of-the-art machine learning models, with a focus on Large Language Models (LLMs) and Visual Language Models (VLMs) • Optimize and fine-tune models for production deployment, including model distillation for improved latency • Drive scientific innovation while maintaining a strong focus on practical business outcomes • Collaborate with cross-functional teams to translate complex technical solutions into tangible customer benefits • Contribute to the broader Amazon Health scientific community and help shape our technical roadmap

Amazon Quick Suite is an enterprise AI platform that transforms how organizations work with their data and knowledge. Combining generative AI-powered search, deep research capabilities, intelligent agents and automations, and comprehensive business intelligence, Quick Suite serves tens of thousands of users. Our platform processes thousands of queries monthly, helping teams make faster, data-driven decisions while maintaining enterprise-grade security and governance. From natural language interactions with complex datasets to automated workflows and custom AI agents, Quick Suite is redefining workplace productivity at unprecedented scale. We are seeking a Data Scientist II to join our Quick Data team, focusing on evaluation and benchmarking data development for Quick Suite features, with particular emphasis on Research and other generative AI capabilities. Our mission is to engineer high-quality datasets that are essential to the success of Amazon Quick Suite. From human evaluations and Responsible AI safeguards to Retrieval-Augmented Generation and beyond, our work ensures that Generative AI is enterprise-ready, safe, and effective for users at scale. As part of our diverse team—including data scientists, engineers, language engineers, linguists, and program managers—you will collaborate closely with science, engineering, and product teams. We are driven by customer obsession and a commitment to excellence. Key job responsibilities In this role, you will leverage data-centric AI principles to assess the impact of data on model performance and the broader machine learning pipeline. You will apply Generative AI techniques to evaluate how well our data represents human language and conduct experiments to measure downstream interactions. Specific responsibilities include: * Design and develop comprehensive evaluation and benchmarking datasets for Quick Suite AI-powered features * Leverage LLMs for synthetic data corpora generation; data evaluation and quality assessment using LLM-as-a-judge settings * Create ground truth datasets with high-quality question-answer pairs across diverse domains and use cases * Lead human annotation initiatives and model evaluation audits to ensure data quality and relevance * Develop and refine annotation guidelines and quality frameworks for evaluation tasks * Conduct statistical analysis to measure model performance, identify failure patterns, and guide improvement strategies * Collaborate with ML scientists and engineers to translate evaluation insights into actionable product improvements * Build scalable data pipelines and tools to support continuous evaluation and benchmarking efforts * Contribute to Responsible AI initiatives by developing safety and fairness evaluation datasets About the team Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, 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. 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. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.

As a Sr. Applied Scientist, you will be responsible for assessing and optimizing the thermal performance of our new and emerging category of devices - Amazon Leo customer terminals. AMZ Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to un-served and under-served communities around the world. The team is a multidisciplinary group of engineers and scientists engaged in a fast paced mission to deliver new products. The team faces a challenging task of balancing cost, schedule, and performance requirements. You should be comfortable collaborating in a fast-paced and often uncertain environment, and contributing to innovative solutions, while demonstrating leadership, technical competence, and meticulousness. Your deliverables will include development of thermal solutions, concept design, feature development, product architecture and system validation through to manufacturing release. You will support creative developments through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques. Key job responsibilities In this role, you will: - Establish temperature thresholds for device and component level considering reliability requirements and use conditions - Apply domain scientific expertise towards developing innovative analysis and tests to study viability of concepts, materials or designs - Evaluate and optimize thermal solution requirements of electronics products - Use simulation tools like Star-CCM+ or FloTherm XT/EFD for analysis and optimization of product design - Validate design modifications for thermal concerns using simulation and actual prototypes - Leverage intimate knowledge of various materials and heat spreaders solutions to resolve thermal issues - Use of programming languages like Python and Matlab for analytical/statistical analyses and automation - Work closely with engineering teams to drive validation, optimization and implementation of hardware design or software algorithmic solutions to improve product and customer risks - Design and execute of tests using statistical tools to validate analytical models, identify risks and assess design margins - Track general business activity including device health in development phase and in field, and provide clear, compelling reports to management on a regular basis - Develop and apply design guidelines based on project learnings About the team Amazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced devices like Fire tablets, Fire TV and Amazon Echo. What will you help us create?