How Amazon achieved its 100% renewable energy goal

Investing in 500+ solar and wind projects, bringing carbon-free energy to dirty grids, and buying Renewable Energy Certificates all played a role.

Amazon recently announced that we met our 100% renewable energy goal, seven years early. Making progress like that—especially with operations as complex as ours—isn’t easy, so we wanted to share more about how we were able to get it done. We used a variety of mechanisms, which included investing in new solar and wind projects, supporting projects in countries that still rely heavily on fossil fuels to power their grids (where renewable energy projects are needed the most), and purchasing Renewable Energy Certificates (RECs).

Here's a bit more about seven key steps we took to achieve our goal:

  1. Investing in 240+ utility-scale projects, which deliver new sources of energy to the grid

    We signed agreements with developers and energy companies where we agreed to buy the renewable energy produced by hundreds of utility-scale solar and wind projects, which help make those projects possible and brings large amounts of new renewable energy online. The agreements – known as Power Purchase Agreements (PPAs) – give the developer confidence to buy materials, purchase land, and move forward with the construction. Once built, these projects provide new sources of renewable energy to the grid, helping decarbonize the grid mix – benefitting everyone who uses those grids (including us). In many cases, these projects would not have moved forward if it weren’t for our investment.

  2. Enabling 270 onsite solar projects on Amazon buildings, which bring renewable energy directly to our buildings or the local grid

    We invested in 270 solar arrays on the rooftops and grounds of Amazon fulfillment centers, Whole Foods Market stores, and other facilities. Many of these projects power the Amazon facilities directly, helping defray the amount of energy the buildings need from the grid. Other projects, like the rooftop solar array at the Amazon Air Hub at the Cincinnati/Northern Kentucky International Airport (CVG), sends power to the local grid, helping power nearby homes and businesses.

  3. Bringing new renewable energy to regions where our operations are located

    The majority of the utility-scale projects we invest in connect to grids where we have operations, including our data center regions. For example, in the IAD Region in Northern Virginia, Amazon has invested in 19 solar projects capable of producing a total of 1.3 gigawatts (GW) of renewable energy for the local grid. In our PDX Region in Oregon, Amazon recently invested in Leaning Juniper, a wind farm that is being repowered with enhanced turbine technology to help it produce more energy. In our DUB Region in Ireland, Amazon is collaborating with Bord na Mona Energy park in County Offaly to co-locate data centers with wind projects, and to invest in up to 800 megawatts (MW) of renewable projects across the country.

  4. Bringing new solar and wind projects to dirty grids

    While a lot of our projects bring carbon-free energy to the grids where we have operations, we’re also focused on making the grid cleaner and more reliable for everyone. An emissions-first approach – which prioritizes supporting renewable energy projects in locations and countries that still rely heavily on fossil fuels – is the fastest way to decarbonize global grids. We’ve done this by investing in projects in countries including Poland, Greece, South Africa, and India, where we enabled 50 solar and wind projects – enough to power 1.1 million homes in New Delhi each year. To demonstrate why location matters, by investing in seven utility-scale renewable energy projects in India, instead of a country like Sweden where the grid is already renewable-heavy, we’ve reduced carbon emissions by 18 times. And to encourage other corporate renewable energy buyers to do the same, we created the Emissions First Partnership alongside many other large buyers of renewable energy, including General Motors, Intel, Meta, and Rivian.

  5. Using grids that already have a large amount of renewable energy

    We’re often able to locate projects on grids that already have a large amount of renewable energy, and as part of our renewable energy methodology, we count the renewable energy mix in the grid toward our goal. This means in a country like the U.S., where the grid already runs on 31% renewable energy, we’re able to reach 100% by investing in additional solar and wind projects to cover the remaining 69% of our energy consumption.

  6. Purchasing Renewable Energy Certificates (RECs)

    No company with complex and growing operations is able today to only consume renewable energy – there simply aren’t enough sources in enough locations, and it takes a while for new projects to come online. Like most companies with ambitious climate goals like ours, we also purchase RECs. RECs are used to legally substantiate claims of renewable energy use, and are used by many companies and government agencies, and other entities, like local utilities. They also help make renewable energy investment more inclusive by allowing medium and small businesses to invest in renewable energy projects without having to purchase an entire wind or solar farm, allowing them to also play a role in addressing climate change.

    When Amazon or any other buyer purchases renewable energy, there’s no way to guarantee that the same electron generated by an Amazon-backed project will be the same electron consumed by our data centers, fulfillment centers, or other operations. RECs allow us to account for these electrons, which are still flowing through the grid, and helping power other needs, like energy to run homes and businesses.

  7. Using different kinds of RECs

    Amazon purchases “bundled” and “unbundled” RECs, which we use as a temporary measure. Bundled RECs are purchased alongside our investment in a wind or solar project, and act as a bridge until the project comes online. This is important, because due to grid constraints, many projects are delayed around the world. Across the United States, there are currently more than 260,000 megawatts (MW) of renewable energy and storage projects waiting to connect to the grid—enough carbon-free energy to power 63.3 million US homes.

    Unbundled RECs are purchased independent of a project. The revenue typically supports an existing project, helping with repairs, upgrades and other needs. Both forms of RECs are a fundamental component of the global renewable energy market – and are key to our society’s transition to a carbon-free energy future. Many current renewable energy technologies would not have been financed nor have become operable without the support of REC purchases, according to the American Council on Renewable Energy.

  8. Amazon’s commitment to sustainability

    We are focused on reaching our Climate Pledge to be net-zero carbon by 2040. We know our path won’t be linear, and we’re continuing to innovate in order to add new sources of carbon-free energy to grids around the world.

    We believe in transparency for our customers, employees and partners, and release a voluntary annual Sustainability Report that highlights the work and progress we’re making to become a more sustainable company, across every business in which we operate. This report, including our achievement of our 100% renewable energy goal, is verified by independent external third-party assurors.

    From investing in new carbon-free energy projects to advocating for grid modernization and collaborating with key stakeholders around the world, Amazon is working toward a cleaner energy future.

Research areas

Related content

  • Staff writer
    October 21, 2025
    Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
  • Staff writer
    February 2, 2026
    Advancing AI requires more than breakthrough models. It depends on communities of builders and researchers who experiment, test assumptions, and share what they learn. That belief is guiding how Amazon engages developers and academics around Amazon Nova, Amazon’s portfolio of AI offerings including the Nova models, Nova Forge and Nova Act.
  • Staff writer
    December 29, 2025
    From foundation model safety frameworks and formal verification at cloud scale to advanced robotics and multimodal AI reasoning, these are the most viewed publications from Amazon scientists and collaborators in 2025.
US, MA, N.reading
Amazon Industrial Robotics Group is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics Group we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. The ideal candidate will contribute to research and implementation that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Implement and optimize control algorithms for robot locomotion - Support development of behaviors that enable robots to traverse diverse terrain - Contribute to methods that integrate stability, locomotion, and manipulation tasks - Help create dynamics models and simulations that enable sim2real transfer of algorithms - Collaborate effectively with multi-disciplinary teams on hardware and algorithms for loco-manipulation
US, CA, Sunnyvale
Amazon Industrial Robotics Group is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine innovative AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As a Senior Applied Scientist, you will lead the development of machine learning systems that help robots perceive, reason, and act in real-world environments. You will set technical direction for adapting and advancing state-of-the-art models (open source and internal research) into robust, safe, and high-performing “robot brain” capabilities for our target tasks, environments, and robot embodiments. You will drive rigorous capability profiling and experimentation, lead targeted innovation where gaps exist, and partner across research, controls, hardware, and product teams to ensure outputs can be further customized and deployed on specific robots. Key job responsibilities - Lead technical initiatives for foundation-model capabilities (e.g., visuomotor / VLA / video-action worldmodel-action policies), from problem definition through validated model deliverables. - Own model readiness for our embodiment class: drive adaptation, fine-tuning, and optimization (latency/throughput/robustness), and define success criteria that downstream teams can build on. - Establish and evolve capability evaluation: define benchmark strategy, metrics, and profiling methodology to quantify performance, generalization, and failure modes; ensure evaluations drive clear roadmap decisions. - Drive the data + training strategy needed to close key capability gaps, including data requirements, collection/curation standards, dataset quality/provenance, and repeatable training recipes (sim + real). - Invent and validate new methods when leveraging SOTA is insufficient—new training schemes, model components, supervision signals, or sim↔real techniques—backed by strong empirical evidence. - Influence cross-team technical decisions by collaborating with controls/WBC, hardware, and product teams on interfaces, constraints, and integration plans; communicate results via design docs and technical reviews. - Mentor and raise the bar: guide junior scientists/engineers, set best practices for experimentation and code quality, and drive a culture of rigor and reproducibility.
US, CA, Sunnyvale
Amazon Industrial Robotics Group is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine innovative AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As a Senior Applied Scientist, you will lead the development of machine learning systems that help robots perceive, reason, and act in real-world environments. You will set technical direction for adapting and advancing state-of-the-art models (open source and internal research) into robust, safe, and high-performing “robot brain” capabilities for our target tasks, environments, and robot embodiments. You will drive rigorous capability profiling and experimentation, lead targeted innovation where gaps exist, and partner across research, controls, hardware, and product teams to ensure outputs can be further customized and deployed on specific robots. Key job responsibilities - Lead technical initiatives for foundation-model capabilities (e.g., visuomotor / VLA / video-action worldmodel-action policies), from problem definition through validated model deliverables. - Own model readiness for our embodiment class: drive adaptation, fine-tuning, and optimization (latency/throughput/robustness), and define success criteria that downstream teams can build on. - Establish and evolve capability evaluation: define benchmark strategy, metrics, and profiling methodology to quantify performance, generalization, and failure modes; ensure evaluations drive clear roadmap decisions. - Drive the data + training strategy needed to close key capability gaps, including data requirements, collection/curation standards, dataset quality/provenance, and repeatable training recipes (sim + real). - Invent and validate new methods when leveraging SOTA is insufficient—new training schemes, model components, supervision signals, or sim↔real techniques—backed by strong empirical evidence. - Influence cross-team technical decisions by collaborating with controls/WBC, hardware, and product teams on interfaces, constraints, and integration plans; communicate results via design docs and technical reviews. - Mentor and raise the bar: guide junior scientists/engineers, set best practices for experimentation and code quality, and drive a culture of rigor and reproducibility.
US, WA, Seattle
We are looking for a passionate Applied Scientist to help pioneer the next generation of agentic AI applications for Amazon advertisers. In this role, you will design agentic architectures, develop tools and datasets, and contribute to building systems that can reason, plan, and act autonomously across complex advertiser workflows. You will work at the forefront of applied AI, developing methods for fine-tuning, reinforcement learning, and preference optimization, while helping create evaluation frameworks that ensure safety, reliability, and trust at scale. You will work backwards from the needs of advertisers—delivering customer-facing products that directly help them create, optimize, and grow their campaigns. Beyond building models, you will advance the agent ecosystem by experimenting with and applying core primitives such as tool orchestration, multi-step reasoning, and adaptive preference-driven behavior. This role requires working independently on ambiguous technical problems, collaborating closely with scientists, engineers, and product managers to bring innovative solutions into production. Key job responsibilities - Design and build agents to guide advertisers in conversational and non-conversational experience. - Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO). - Curate datasets and tools for MCP. - Build evaluation pipelines for agent workflows, including automated benchmarks, multi-step reasoning tests, and safety guardrails. - Develop agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning. - Prototype and iterate on multi-agent orchestration frameworks and workflows. - Collaborate with peers across engineering and product to bring scientific innovations into production. - Stay current with the latest research in LLMs, RL, and agent-based AI, and translate findings into practical applications. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Campaign Strategies team within Sponsored Products and Brands is focused on guiding and supporting 1.6MM advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in guidance systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware agentic advertiser guidance system that leverages LLMs together with tools such as auction simulations, ML models, and optimization algorithms. This agentic framework, will operate across both chat and non-chat experiences in the ad console, scaling to natural language queries as well as proactively delivering guidance based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular keyword recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning, MCP, and preference optimization), ensuring our systems are both scalable and adaptive.
US, WA, Bellevue
Amazon LEO is Amazon's low Earth orbit satellite network. Our mission is to deliver fast, reliable internet connectivity to customers beyond the reach of existing networks. From individual households to schools, hospitals, businesses, and government agencies, Amazon LEO will serve people and organizations operating in locations without reliable connectivity. The Amazon LEO Global Business Operations (GBO) team drives data-driven decision-making across sales, marketing, operations, product, engineering, finance, and legal functions. We build scalable business intelligence solutions and data infrastructure to solve complex, ambiguous problems with LEO-wide impact. We are looking for a talented Research Scientist to contribute to LEO's long-term vision and strategy for capacity simulations and inventory optimization. This effort will be instrumental in helping LEO execute on its business plans globally. As one of our valued team members, you will be obsessed with matching our standards for operational excellence with a relentless focus on delivering results. Key job responsibilities In this role, you will: Collaborate with product, business development, sales, marketing, operations, finance, and various technical teams (engineering, science, R&D, simulations, etc.) to support the implementation of capacity simulations and inventory optimization solutions. Develop and prototype scalable solutions to optimization problems for operating and planning satellite resources. Support technical roadmap definition efforts by building models to predict future inventory availability and key operational and financial metrics across the network. Design experiments and simulations to evaluate optimization improvements and understand how they interact with each other. Analyze large amounts of satellite and business data to identify simulation and optimization opportunities. Communicate insights and recommendations to technical and non-technical audiences to support decision-making across LEO. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be 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.
US, CA, Sunnyvale
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Our organization is building world-class teams with deep expertise in large-scale recommender systems. This role sits at the intersection of AI research and direct business impact, where recommendation quality directly influences business outcomes and customer satisfaction. You'll be joining a team focused on foundational models for recommender systems and working on production systems that serve millions of customers and shape the future of personalized entertainment experiences. We're seeking talent who can deliver measurable impact on our core business metrics while advancing the state-of-the-art in personalization and recommendation technology. Key job responsibilities - Develop AI solutions for various Prime Video Search & Recommendation systems using Deep Learning, Reinforcement Learning, Optimization Methods, and most importantly, GenAI - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end - Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses - Effectively communicate technical and non-technical ideas with teammates and stakeholders - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals About the team The Prime Video - Personalization & Discovery Science team owns science solution to power search experience on various devices, from sourcing, relevance, & ranking (to name a few). We are on a mission to deliver an AI-first customer experience. At the heart of this transformation are our recommendation systems -- core, customer-facing components that serve as primary drivers of engagement & growth.
CA, ON, Toronto
Are you interested in shaping the future of Advertising and B2B Sales? We are a growing team with an exciting AI-first 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 Applied Science talent to help us build new, science-backed services that drive success for our customers. Our goal is to transform the way account teams operate by creating AI agents that help optimize their end-to-end workflows, and developing actionable insights and recommendations they can share with their advertising accounts As an Applied Scientist on the team with a specific focus on creating autonomous AI agents that can operate accurately at large scale, you will bring deep expertise in Natural Language Processing (inc. tokenization, syntactic parsing, named entity recognition (NER), sentiment analysis, text classification), Large Language Models (inc. foundation model fundamentals, post-training, reward modeling, RAG, transformer architecture), Deep Learning, Reinforcement Learning and/or Recommender Systems. You have the scientific and technical skills to build and refine models that can be implemented in production and you continuously measure the performance of your system to drive continuous improvements. 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. You are fluently able to leverage the latest Generative AI systems and services to accelerate and improve your work while maintaining high quality in your work outputs. Key job responsibilities Scientific Modeling - Conceptualize and lead state-of-the-art research on new Reinforcement Learning, Deep Learning, NLP, LLM, (Generative) Artificial Intelligence and Recommender System solutions to create AI agents and optimize all aspects of the Ad Sales business - Lead the technical approach for the design and implementation of successful models and algorithms in support of expert cross-functional teams delivering on demanding projects - Run regular A/B experiments, gather data, and perform statistical analysis - Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving - Publish scientific findings in reports and papers that can be shared internally and externally Product Development Support - Partner with software engineering and product management teams to support product and service development, define success metrics and measurement approaches, and help drive adoption of innovative new features for our services. - Lead requirements gathering sessions with product teams and business stakeholders - Maintain scientific documentation and knowledge for product initiatives Collaboration & Communication - Work closely with software engineers to deliver end-to-end solutions into production - Translate complex scientific findings into actionable business recommendations for stakeholders and senior management - Provide clear, compelling reports and presentations on a regular basis with respect to your models and services - Communicate with internal teams to showcase results and identify best practices. 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.
CA, ON, Toronto
Are you interested in shaping the future of Advertising and B2B Sales? We are a growing team with an exciting AI-first 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 Applied Science talent to help us build new, science-backed services that drive success for our customers. Our goal is to transform the way account teams operate by creating AI agents that help optimize their end-to-end workflows, and developing actionable insights and recommendations they can share with their advertising accounts As an Applied Scientist on the team with a specific focus on creating autonomous AI agents that can operate accurately at large scale, you will bring deep expertise in Natural Language Processing (inc. tokenization, syntactic parsing, named entity recognition (NER), sentiment analysis, text classification), Large Language Models (inc. foundation model fundamentals, post-training, reward modeling, RAG, transformer architecture), Deep Learning and/or Reinforcement Learning . You have the scientific and technical skills to build and refine models that can be implemented in production and you continuously measure the performance of your system to drive continuous improvements. 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. You are fluently able to leverage the latest Generative AI systems and services to accelerate and improve your work while maintaining high quality in your work outputs. Key job responsibilities Scientific Modeling - Conceptualize and lead state-of-the-art research on new NLP, LLM and (Generative) Artificial Intelligence solutions (inc. post-training, fine-tuning, reward modeling) to optimize all aspects of the Ad Sales business - Lead the technical approach for the design and implementation of successful models and algorithms in support of expert cross-functional teams delivering on demanding projects - Run regular A/B experiments, gather data, and perform statistical analysis - Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving - Publish scientific findings in reports and papers that can be shared internally and externally Product Development Support - Partner with software engineering and product management teams to support product and service development, define success metrics and measurement approaches, and help drive adoption of innovative new features for our services. - Lead requirements gathering sessions with product teams and business stakeholders - Maintain scientific documentation and knowledge for product initiatives Collaboration & Communication - Work closely with software engineers to deliver end-to-end solutions into production - Translate complex scientific findings into actionable business recommendations for stakeholders and senior management - Provide clear, compelling reports and presentations on a regular basis with respect to your models and services - Communicate with internal teams to showcase results and identify best practices. 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.
US, WA, Bellevue
Alexa+ is Amazon’s next-generation, AI-powered virtual assistant. Building on the original Alexa, it uses generative AI to deliver a more conversational, personalized, and effective experience. As an Applied Scientist II on the Alexa Sensitive Content Intelligence (ASCI) team, you'll be part of an elite group developing industry-leading technologies in attribute extraction and sensitive content detection that work seamlessly across all languages and countries. In this role, you'll join a team of exceptional scientists pushing the boundaries of Natural Language Processing. Working in our dynamic, fast-paced environment, you'll develop novel algorithms and modeling techniques that advance the state of the art in NLP. Your innovations will directly shape how millions of customers interact with Amazon Echo, Echo Dot, Echo Show, and Fire TV devices every day. What makes this role exciting is the unique blend of scientific innovation and real-world impact. You'll be at the intersection of theoretical research and practical application, working alongside talented engineers and product managers to transform breakthrough ideas into customer-facing experiences. Your work will be crucial in ensuring Alexa remains at the forefront of AI technology while maintaining the highest standards of trust and safety. We're looking for a passionate innovator who combines strong technical expertise with creative problem-solving skills. Your deep understanding of NLP models (including LSTM and transformer-based architectures) will be essential in tackling complex challenges and identifying novel solutions. You'll leverage your exceptional technical knowledge, strong Computer Science fundamentals, and experience with large-scale distributed systems to create reliable, scalable, and high-performance products that delight our customers. Key job responsibilities In this dynamic role, you'll design and implement GenAI solutions that define the future of AI interaction. You'll pioneer novel algorithms, conduct ground breaking experiments, and optimize user experiences through innovative approaches to sensitive content detection and mitigation. Working alongside exceptional engineers and scientists, you'll transform theoretical breakthroughs into practical, scalable solutions that strengthen user trust in Alexa globally. You'll also have the opportunity to mentor rising talent, contributing to Amazon's culture of scientific excellence while helping build high-performing teams that deliver swift, impactful results. A day in the life Imagine starting your day collaborating with brilliant minds on advancing state-of-the-art NLP algorithms, then moving on to analyze experiment results that could reshape how Alexa understands and responds to users. You'll partner with cross-functional teams - from engineers to product managers - to ensure data quality, refine policies, and enhance model performance. Your expertise will guide technical discussions, shape roadmaps, and influence key platform features that require cross-team leadership. About the team The Alexa Sensitive Content Intelligence (ASCI) team owns the Responsible AI and customer feedback charters in Alexa+ and Classic Alexa across all device endpoints, modalities and languages. The mission of our team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, (3) build customer trust through generating appropriate interactions on sensitive topics, and (4) analyze customer feedback to gain insight and drive continuous improvement loops. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video.
US, CA, Palo Alto
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. About the team The SPB-Agent is the central agent that interfaces with advertisers in Ads Console, Selling Partner portals (Seller Central, KDP, Vendor Central), and internal Sales systems across all agentic experiences (conversational and others). SPB Agent team's vision is to build a highly personalized and context-aware agentic advertiser guidance system that seamlessly integrates Large Language Models (LLMs) with sophisticated tooling, operating across all experiences. We identify high-impact opportunities spanning from strategic product guidance to granular optimization and deliver them through personalized, scalable experiences grounded in state-of-the-art agent architectures, reasoning frameworks, sophisticated tool integration, and model customization approaches including fine-tuning, MCP, and preference optimization. This presents an exceptional opportunity to shape the future of e-commerce advertising through advanced AI technology at unprecedented scale, creating solutions that directly impact millions of advertisers.