Member of Technical Staff

About the Role

Join a small AI infrastructure team building efficient inference systems for open-source language models. You will improve performance across diverse hardware, helping make production AI serving faster and more cost-effective.

What You'll Do

• Bring new open-source models into production with tuning for latency and throughput.

• Optimize inference serving, including batching, KV caching, speculative decoding, and quantization.

• Write and tune GPU kernels using CUDA, HIP, and Triton across varied accelerator platforms.

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

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

• Work with customers to understand needs and help solve infrastructure challenges.

What We're Looking For

• Typically 1 to 6 years of experience, with relevant early-career candidates also considered.

• Strong systems or infrastructure engineering experience, in addition to any application or full-stack work.

• Understanding of computer architecture, memory hierarchy, and operating system internals.

• Experience with Python and familiarity with CUDA, HIP, Triton, or other GPU programming tools.

• Knowledge of inference optimization techniques such as quantization, batching, KV caching, or speculative decoding.

• Experience deploying or operating infrastructure at scale, such as clusters or large GPU fleets, is valuable.

• Evidence of taking on challenging systems problems, learning quickly, and using AI coding tools to improve workflows.

• Comfort communicating with customers and working across a broad range of technical responsibilities.

Compensation & Benefits

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

Location

On-site in San Francisco, California, five days per week.