Every token a large language model generates depends on data reaching the right accelerator at the right moment. As AI models outgrow any single chip, the network between accelerators becomes the bottleneck that decides how fast — and how affordably — the world's largest models can serve real users. That network layer is what our team builds. We're looking for an engineer to work at the frontier of disaggregated inference: splitting LLM serving into separate prefill and decode pools and moving the model's KV cache between them at the limit of what the hardware allows. Get it right and users get answers in milliseconds; get it wrong and the fastest accelerators in the world sit idle waiting on data. You'll build components of the high-speed transfer path that make that difference, and you'll learn to measure success in how close we run to the theoretical peak of the machine. In this role you will: Build and optimize the low-level data-movement software that transfers KV cache and activations across accelerators, servers, and heterogeneous memory — over AWS's highest-performance network fabric. Profile real workloads, find the true bottleneck, and close the gap between "it works" and "it runs fast" — pushing components toward the hardware's limit. Work across the stack — from network transport up to the inference frameworks — learning from the teams building the chips, runtime, and models. Deliver features that ship to our largest clusters, for our largest customers, serving the largest AI models in production. What we're looking for: Strong C/C++ and a genuine interest in low-level, performance-critical systems — solid command of Linux, memory, and writing fast code. The instinct to ask "how fast could this go?" and the discipline to measure it. Exposure to high-speed networking, HPC interconnects, or GPU/accelerator systems (RDMA, InfiniBand, libfabric, UCX, NCCL, MPI) is a strong plus; embedded-systems experience is welcome. Prior AI/ML experience is not required — if you're a strong systems engineer eager to learn, we'll teach you the ML side. If you like solving genuinely hard problems, working alongside HPC and ML customers, iterating fast, and shipping at a scale few places can offer, come join us. You'll work alongside senior engineers and Principal Engineers who've built this layer from the ground up, with real room to grow your scope and technical depth — on a team at the leading edge of AI/ML infrastructure. About the team: You'd be joining Annapurna Labs, an integral part of AWS. Annapurna designs the hardware and software building blocks behind EC2 — every EC2 instance runs on hardware we designed. We specialize in the chips, systems, and software that optimize the AWS customer experience, and this team sits where AI meets the silicon and the network underneath it. A day in the life Annapurna Labs, a crucial part of AWS, is responsible for developing hardware and software components for EC2 infrastructure. Our team focuses on building networking solutions that for Machine Learning (ML) and High-Performance Computing (HPC) workloads on AWS. We have mixed discipline orgs, you’d be working side by side with infrastructure experts, hardware engineers, RTL engineers, scientists & architects. Our workforce spans the globe and is truly international, you’ll find yourself working side by side with individuals from numerous countries. We take mentorship seriously, you can both expect senior mentorship and will be expected to mentor new and junior engineers. The pace is fast as we work on the latest advancements of AI/ML, but we take the time to bond as a team and enjoy the successes. We offer flexibility in working hours, and respect WLB as a core org tenet. The team enjoys working with numerous principal-level engineers and closely with directors, career growth opportunities are certainly available. This is a role where you will always be encouraged to keep learning, the AI/ML field is fast moving and constantly evolving. About the team Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future. 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. About 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 (diversity) conferences, inspire us to never stop embracing our uniqueness. 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. 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.