Performance Engineer, Inference Engine

EngineersOfAI; San Francisco (CA); In loco; USD 180.000 - 240.000; Tempo pieno

Descrizione del lavoro

Anthropic in San Francisco is seeking a Performance Engineer for the Inference Engine to optimize throughput, latency, and resource use across accelerator and cloud platforms. You will work on building high-performance systems, aligning memory and compute, and ensuring robustness of model serving at scale.

You should be strong in systems programming (Rust, C++, or similar), with experience profiling and optimizing distributed software, plus a hands-on approach to measuring impact and iterating

Competenze

• Working mental model of LLM inference across accelerator compute, memory and interconnect
• Proven ability to ramp quickly in deep systems and ship changes

Mansioni

• Improve throughput, cost, reliability, and latency across accelerator and cloud platforms
• Model, measure, and optimize performance; develop observability to identify gaps
• Keep device utilization high and reuse model state to reduce recompute
• Ensure safety and robustness of the inference engine across platforms

Conoscenze

Rust

C++

LLM inference

Performance profiling

Strumenti

GPU programming

OS internals

Descrizione del lavoro

About Anthropic

Anthropics 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.

Performance Engineer, Inference Engine

About the Role

Anthropics 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.

You will work on building and optimizing this system at Anthropics 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.

Some example recurring themes:

• Keep device utilization high. Accelerators should never be waiting due to other overheads.

• Reuse instead of recompute. Keep model state cached and reuse it whenever that is cheaper than computing it again.

• Measure, model, then change. 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.

• Tokens you can trust. Ensuring model quality matters more than efficiency. We build the infrastructure to ensure Claude maintains its intelligence across platforms and over time.

• Safety on every token. 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.

Minimum Qualifications

• 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

• Proven quick learner: ramped fast in deep, unfamiliar systems and shipped consequential changes quickly

• Strong systems programming (Rust, C++, or similar), with care for code quality and tests

• Analytical about performance: observe and profile first, form a hypothesis, test it, then change the code and measure again

• Low ego: ask the naive question, take feedback well, pick up slack outside your job description

• Enjoy pair programming (we love to pair!) and care about the societal impacts of your work

Preferred Qualifications

• Experience inside an LLM serving engine and a sense of where its abstractions strain

• GPU/Accelerator programming

• OS internals

• Language modeling with transformers

• Experience building an allocator, cache, scheduler, or high-bandwidth transport

• Fluency in Rust

• Experience making systems reproducible: determinism, replay, property-based tests

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role's On Target Earnings ("OTE&