About the Role

Join a small AI infrastructure team building tools to improve the performance and efficiency of model inference across diverse hardware. This hands-on role spans low-level systems engineering, production operations, and customer collaboration.

What You'll Do

Bring new open-source models into production with support tuned for latency and throughput.

Improve the inference serving stack, including batching, KV caching, speculative decoding, and quantization.

Write and tune GPU kernels using CUDA, HIP, and Triton across a range of accelerator platforms.

Design, deploy, and operate heterogeneous clusters across infrastructure vendors.

Operate production inference systems, improving reliability, observability, and cost efficiency at scale.

Work with customers to understand performance needs and solve technical problems.

What We're Looking For

Typically 1 to 6 years of experience in backend, infrastructure, systems, or related engineering, with strong low-level engineering skills.

Strong understanding of computer architecture, memory hierarchy, and operating system internals.

Experience with CUDA, HIP, Triton, or other GPU programming, and familiarity with Python and accelerator platforms such as TPU or AWS Trainium.

Background deploying or operating large-scale infrastructure, clusters, or GPU fleets is valuable.

Familiarity with machine learning inference optimization techniques such as quantization, batching, KV caching, or speculative decoding is valuable.

Strong computer science fundamentals, a record of solving demanding technical problems, and experience using AI coding tools to improve workflows.

Comfort working directly with customers and contributing across a broad range of engineering challenges.

Compensation & Benefits

Base salary is $200,000 to $300,000 annually. Visa sponsorship is available.

Location

On-site in San Francisco, California.