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 to see where the gaps are, model the impact of potential improvements, deploy them, and go around again, with Claude speeding up every turn of that loop.</p> </li> <li> <p><strong>Tokens you can trust.</strong> Ensuring model quality matters more than efficiency. We build the infrastructure to ensure Claude maintains its intelligence across platforms and over time.&nbsp;</p> </li> <li> <p><strong>Safety on every token.</strong> We work closely with our safeguards and safety teams. The inference engine is the backbone behind our production safety systems, ensuring efficiency without compromising robustness.&nbsp;</p> </li> </ul> <h3>Minimum Qualifications</h3> <ul> <li> <p>A working mental model of LLM inference: how prefill and decode land on an accelerator's compute, memory, and interconnect, and what the host is doing meanwhile</p> </li> <li> <p>Proven quick learner: ramped fast in deep, unfamiliar systems and shipped consequential changes quickly</p> </li> <li> <p>Strong systems programming (Rust, C++, or similar), with care for code quality and tests</p> </li> <li> <p>Analytical about performance: observe and profile first, form a hypothesis, test it, then change the code and measure again</p> </li> <li> <p>Low ego: ask the naive question, take feedback well, pick up slack outside your job description</p> </li> <li> <p>Enjoy pair programming (we love to pair!) and care about the societal impacts of your work</p> </li> </ul> <h3>Preferred Qualifications</h3> <ul> <li> <p>Experience inside an LLM serving engine and a sense of where its abstractions strain</p> </li> <li> <p>GPU/Accelerator programming</p> </li> <li> <p>OS internals</p> </li> <li> <p>Language modeling with transformers</p> </li> <li> <p>Experience building an allocator, cache, scheduler, or high-bandwidth transport</p> </li> <li> <p>Fluency in Rust</p> </li> <li> <p>Experience making systems reproducible: determinism, replay, property-based tests</p> </li> </ul> <p><br><br></p><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>The annual compensation range for this role is listed below.&nbsp;</p> <p>For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.</p></div><div class="title">Annual Salary:</div><div class="pay-range"><span>$350,000</span><span class="divider">&mdash;</span><span>$850,000 USD</span></div></div></div><div class="content-conclusion"><h2><strong>Logistics</strong></h2> <p><strong>Minimum education: </strong>Bachelor’s degree or an equivalent combination of education, training, and/or experience</p> <p><strong>Required field of study:&nbsp;</strong>A field relevant to the role as demonstrated through coursework, training, or professional experience</p> <p><strong>Minimum years of experience: </strong>Years of experience required will correlate with the internal job level requirements for the position</p> <p><strong>Location-based hybrid policy:</strong> Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.</p> <p><strong data-stringify-type="bold">Visa sponsorship:</strong>&nbsp;We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</p> <h2><strong>How we're different</strong></h2> <p>We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.</p> <p>The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI &amp; Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.</p> <h2><strong>Come work with us!</strong></h2> <p>Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. <strong data-stringify-type="bold">Guidance on Candidates' AI Usage:</strong>&nbsp;Learn about&nbsp;<a class="c-link" href=" target="_blank" data-stringify-link=" data-sk="tooltip_parent">our policy</a> for using AI in our application process.</p></div>