Member of Technical Staff

About the Role

Join an early-stage AI infrastructure team building systems that improve the performance and efficiency of open-source model inference. This role spans low-level optimization and production operations, helping deliver fast, reliable inference across diverse hardware.

What You'll Do

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

• Improve inference serving through batching, KV cache management, 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 providers.

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

• Work with customers to understand needs and solve infrastructure performance problems.

What We're Looking For

• Approximately 1 to 6 years of experience in backend, infrastructure, or systems engineering, including low-level work beyond application development; strong early-career candidates are welcome.

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

• Experience operating infrastructure at scale, such as clusters, data centers, or GPU fleets.

• Familiarity with machine learning inference optimization, including quantization, batching, KV caches, or speculative decoding.

• Experience with CUDA, Triton, or other GPU programming, with Python and exposure to technologies such as HIP, TPU, or AWS Trainium useful.

• Strong CS fundamentals, evidence of rapid learning or ownership of challenging systems, and experience using AI coding tools to improve workflows.

• Comfort working directly with customers and taking broad ownership in a small team.

Compensation & Benefits

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

Location

On-site in San Francisco, California.