Member of Technical Staff

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.