Member of Technical Staff

About the Role

Join a small AI infrastructure team working to improve the speed and efficiency of large language model inference. You will build and operate systems that optimize models across a range of hardware, with ownership spanning low-level engineering and production operations.

What You'll Do

• Deliver day-one support for new open-source models, tuning them for latency and throughput.

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

• Write and optimize GPU kernels using CUDA, HIP, and Triton across diverse accelerator platforms.

• Design, deploy, and operate heterogeneous clusters across hardware vendors.

• Run production inference across mixed hardware fleets, improving reliability, observability, and cost per token.

• Work directly with customers as part of solving infrastructure and performance challenges.

What We're Looking For

• Typically 1 to 6 years of experience in backend, infrastructure, systems, or related engineering work; strong early-career candidates are welcome.

• Strong understanding of computer architecture, memory hierarchy, and operating system internals beyond application-level development.

• Experience with low-level or systems engineering, plus familiarity with Python and GPU programming tools such as CUDA or Triton; HIP experience is also relevant.

• Knowledge of ML inference optimization, including quantization, batching, KV caches, or speculative decoding.

• Experience deploying or operating large-scale infrastructure, clusters, data centers, or GPU fleets.

• Strong computer science fundamentals, demonstrated ownership of complex systems, and an interest in using AI coding tools to improve engineering workflows.

• Comfort working directly with customers and contributing across a broad range of technical problems.

Compensation & Benefits

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

Location

On-site in San Francisco, California.