Performance Engineer, Inference Engine

<div class="content-intro"><h2><strong>About Anthropic</strong></h2> <p>Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.</p></div><h2>Performance Engineer, Inference Engine</h2> <h3>About the Role</h3> <p>Anthropic's inference engine is the software between the accelerator kernels and the routing layer. It manages the entire token path in between: batching requests, laying the model out across chips, managing memory for weights and activations, coordinating every forward pass, and managing model state across requests. Built in-house, it runs on all of our accelerator platforms, serving Claude to millions of users and running our research workloads.</p> <p>You will work on building and optimizing this system at Anthropic scale: improving throughput, cost, reliability, and latency across all accelerator and cloud platforms. You are intimately familiar with the hardware and bandwidth numbers (FLOPs, HBM, PCIe, RDMA, network links, etc.) and can model a problem quickly: where the time and bytes go, and what sets the bound. The role is deeply technical and high-impact, and suits engineers who enjoy working across accelerator programming, high-performance systems that seamlessly coordinate between host and device, and large-scale distributed systems. Familiarity with the transformer architecture is a plus.</p> <p>Some example recurring themes:</p> <ul> <li> <p><strong>Keep device utilization high.</strong> Accelerators should never be waiting due to other overheads.</p> </li> <li> <p><strong>Reuse instead of recompute.</strong> Keep model state cached and reuse it whenever that is cheaper than computing it again.</p> </li> <li> <p><strong>Measure, model, then change.</strong> We build the observability t