Member of Technical Staff

About the Role

Join a small engineering team building AI infrastructure that improves the speed and efficiency of large language model inference. You will work across models, serving systems, kernels, and production hardware to deliver reliable, high-performance inference.

What You'll Do

• Ship support for new open-source models, tuning them for latency and throughput.

• Improve inference serving through techniques such as batching, KV cache optimization, speculative decoding, and quantization.

• Write and tune GPU kernels using CUDA, HIP, and Triton across heterogeneous hardware.

• Design, deploy, and operate clusters spanning multiple hardware vendors.

• Run production inference systems, improving reliability, observability, and cost efficiency.

• Work with customers to understand their needs and solve technical performance challenges.

What We're Looking For

• Approximately 1 to 6 years of relevant experience, with strong new graduates also considered.

• Strong computer science fundamentals, including knowledge of computer architecture, memory hierarchies, and operating system internals.

• A strong undergraduate background in computer science or a closely related field, with relevant coursework in operating systems, architecture, or compilers.

• Experience in backend, infrastructure, or systems engineering, ideally in a technically demanding environment.

• Familiarity with infrastructure at scale, such as data centers, clusters, or large GPU fleets.

• Experience or interest in inference optimization and GPU programming, including CUDA, Triton, or related tools.

• Effective customer communication skills, experience using AI coding agents to improve workflows, and evidence of exceptional technical work.

Compensation & Benefits

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

Location

On-site in San Francisco, California.