Five ways Amazon is preparing for the energy demands of the future

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.

As our society relies on technology more than ever, from consumer electronics to large-scale infrastructure in both public and private sectors, global energy demands are continuing to grow. At Amazon, we’re working to meet the future energy demands of our customers and our business while remaining committed to our Climate Pledge to become net-zero carbon by 2040. We know the path forward is changing, and our work to decarbonize our operations won’t be linear, so we’re constantly experimenting, learning, and evolving.

Here are five ways Amazon is innovating to ensure our energy needs are met with sustainability and efficiency in mind:

  1. Matching 100% of the electricity consumed by our operations with renewable energy

    Amazon achieved our goal to match all of the electricity consumed by our operations with 100% renewable energy. We originally pledged to reach this goal in 2030, and achieved it seven years early in 2023. In order to achieve this ambitious goal, we’ve invested in more than 600+ solar and wind projects around the world, representing more than 34 gigawatts (GW) of new energy capacity once operational. Once all of these projects become operational, they’re expected to produce enough energy to power more than 8.3 million average U.S. homes each year. We’ve also been the world’s largest corporate purchaser of renewable energy every year since 2020.

    Looking ahead, increasing energy demands—including those driven by generative AI—will require us to add more sources of carbon-free energy, including nuclear, offshore wind, green hydrogen, and more. In addition, Amazon’s $2 billion Climate Pledge Fund is investing in the next generation of emerging low-carbon energy technologies, including green ammonia, renewable transportation fuels, portable battery storage, and other innovations that can help power the future energy needs of our business and the transition to a lower-carbon economy.

  2. Advocating for grid modernization

    In most cases, when we invest in new carbon-free energy projects like wind and solar farms, that energy goes into the public power grids in those areas—meaning those communities are also benefitting. We want electric grids where we operate to be net-zero carbon for everyone. But right now, grids across the country are limited in how much energy they can accept. While we’re working with utilities and energy companies to fast-forward new solar, wind, and other carbon-free energy projects to meet rising demand, the grid also needs to be modernized so it can handle that demand. More than 70% of the U.S. grid is 25 years old or older, and across the United States, there are currently more than 2.6 million megawatts (MW) of renewable energy and storage projects waiting to come online—nearly double the current amount of U.S. generation capacity, or enough carbon-free energy to power 63.3 million US homes. To help address this, Amazon teams are engaging with energy regulators and other officials at the federal and state levels to help support grid modernization, remove permitting obstacles, and deploy grid-enhancing technologies.

    We’re also working with grid operators and utilities to help ensure grid modernization is funded in the markets where we do business. Like all ratepayers, Amazon pays transmission costs for the energy we use, and those rates are established by utility regulators. Those costs help cover infrastructure upgrades and other grid modernization needs required to support the needs of all energy users.

  3. Deploying renewable energy where it’s needed most

    To have the biggest impact, we focus most of our investments on the grids where our operations are concentrated, as well as regions where the existing grid is most carbon intensive. For example, we have a large AWS presence in Oregon, so we’re working with utility company Umatilla Electric to procure new sources of renewable energy, such as electricity purchased from a local wind farm, to help increase the amount of renewable energy to the local grid.

    We also make a point to invest in renewables projects in places that otherwise rely heavily on fossil fuels like coal, oil, and natural gas to power their grids, such as India, Poland, and the southeastern U.S. To support this effort, Amazon cofounded the Emissions First Partnership, a coalition of companies committed to modernizing the greenhouse gas accounting standards for the power sector, which will encourage companies to invest in renewable and carbon-free energy in more carbon-intensive grids. Both of these approaches help reduce energy-related emissions and match the electricity used by our operations with renewable energy, a win-win for people and the planet.

  4. Taking steps to run our data centers more efficiently

    We’re constantly reevaluating how our data centers operate and determining ways to help them run on less energy and be more efficient. And as the world scales our use of AI, it's important to also minimize its environmental footprint. A new study by Accenture shows that an effective way to do that is by moving IT workloads from on-premises infrastructure to AWS data centers around the globe. The research estimates AWS’s infrastructure is up to 4.1 times more efficient than on-premises, and when workloads are optimized on AWS, the associated carbon footprint can be reduced by up to 99%. This type of impact is possible by AWS optimizing our data center design, investing in purpose-built chips, and innovating with new cooling technologies. For example, we’ve taken steps to design our data centers to use natural air flow to lower server temperatures, which can heat up while in use. This allows us to use less air conditioning when possible. We’ve also designed our AWS machine learning chips, which power millions of workloads daily, to be more energy efficient. For example, AWS’s Graviton processor delivers high performance with high levels of energy efficiency. Graviton4 provides up to 30% better computing performance, 50% more cores, and 75% more memory bandwidth than current-generation Graviton3 processors, delivering the best price performance and energy efficiency for a broad range of workloads running on Amazon EC2. We’re also keeping technologies in use longer by increasing the lifespan of our servers from five to six years.

  5. Incorporating sustainability practices into the design and construction of our buildings

    We’re working to design our corporate buildings in ways that support the transition to net-zero carbon, including by constructing new buildings using low-carbon concrete and electrified HVAC systems. One of our newest Same-Day fulfillment centers in California is set to make history as the world’s first fulfillment facility to achieve Zero Carbon Certification status. The electricity used by our HQ2 headquarters is matched with 100% renewable energy from a local solar farm and has achieved the highest level of LEED green building certification. We’ve also installed energy-efficient lighting, low-flow water fixtures, and implemented recycling and composting in our corporate offices. In Europe and in the U.S., our data centers are transitioning to renewable diesel in our generators, which can result in as much as 90% fewer greenhouse gas emissions over the fuel’s life cycle compared to diesel.

    Amazon is continuing to work toward transitioning our business and our society to a cleaner energy future.

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Amazon 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 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. 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 an Applied Scientist, you will develop and improve machine learning systems that help robots perceive, reason, and act in real-world environments. You will leverage state-of-the-art models (open source and internal research), evaluate them on representative tasks, and adapt/optimize them to meet robustness, safety, and performance needs. You will invent new algorithms where gaps exist. You’ll collaborate closely with research, controls, hardware, and product-facing teams, and your outputs will be used by downstream teams to further customize and deploy on specific robot embodiments. Key job responsibilities As an Applied Scientist in the Foundations Model team, you will: - Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization. - Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments. - Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes. - Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes. - Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies. About the team 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
US, CA, San Francisco
Amazon is seeking an exceptional Sr. Applied Scientist to lead the development of perception systems that harness the power of radar and thermal imaging — enabling robots to perceive and operate reliably in conditions where conventional vision alone falls short. In this role, you will develop ML-driven perception pipelines for non-traditional sensing modalities, pushing the boundaries of what robots can see, understand, and act upon in challenging real-world environments. At Amazon, we leverage advanced robotics, machine learning, and artificial intelligence to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence. As a Sr. Applied Scientist in Multi-Modal Perception, you will apply deep computer vision expertise alongside classical signal processing techniques for radar and thermal imaging — modalities that provide robustness in adverse conditions and sensing capability beyond the visible spectrum. You will develop ML-based methods to extract semantic and geometric information from radar point clouds, radar tensors, and thermal imagery, and fuse these with camera and depth data to build perception systems that are reliable, comprehensive, and ready for deployment at scale. Your work will unlock new capabilities for our robots — enabling reliable detection, classification, and scene understanding in low-visibility conditions, cluttered environments, and scenarios where traditional RGB-based perception is insufficient. You will lead research that translates cutting-edge advances in deep learning and computer vision to these underexplored but high-impact sensing modalities. Join us in building the next generation of multi-modal perception systems that will define the future of autonomous robotics at scale. Key job responsibilities - Lead the research, design, and development of ML-based perception pipelines for radar and thermal/infrared imaging modalities - Develop deep learning models for object detection, classification, segmentation, and tracking using radar data (point clouds, range-Doppler maps, radar tensors) and thermal imagery - Design and implement multi-modal fusion architectures that combine radar, thermal, camera, and depth data for robust, all-condition perception - Develop novel representations and feature extraction methods tailored to the unique characteristics of radar and thermal sensors (sparsity, noise profiles, spectral properties) - Build end-to-end perception systems — from raw sensor data processing and calibration to model training, evaluation, and real-time deployment - Collaborate closely with Hardware, Navigation, Planning, and Controls teams to define sensor configurations and deliver integrated autonomy solutions - Establish benchmarks, datasets, and evaluation frameworks for radar and thermal perception - Mentor scientists and engineers; foster a culture of scientific rigor, innovation, and high-impact delivery - Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Our team is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.