Member of Technical Staff

Join a small AI infrastructure team working to make model inference faster and more energy and cost efficient. You will optimize the systems that serve open-source language models across heterogeneous hardware, with ownership spanning kernel performance through production operations.

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

• Optimize inference serving with techniques such as batching, KV caching, speculative decoding, and quantization.

• Write and tune GPU kernels using CUDA, HIP, and Triton for a range of accelerator platforms.

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

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

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

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

• Typically 1 to 6 years of experience in backend, infrastructure, systems, or low-level engineering, including experience at scale or in a fast-moving startup.

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

• Experience with production infrastructure such as data centers, clusters, or large GPU fleets.

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

• Experience with CUDA, Triton, or other GPU programming, and familiarity with Python, HIP, or accelerator platforms such as TPU or AWS Trainium.

• Evidence of strong technical ownership, rapid learning, or exceptional performance in demanding engineering work; strong computer science fundamentals are valued.

• Comfort using AI agents and coding tools to improve engineering workflows, and communicating with customers.

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