Member of Technical Staff

ABOUT THE ROLE Join a small AI infrastructure team building tools to improve the performance and efficiency of model inference across diverse hardware. This hands-on role spans low-level systems engineering, production operations, and customer collaboration. WHAT YOU'LL DO - Bring new open-source models into production with support tuned for latency and throughput. - Improve the inference serving stack, including batching, KV caching, 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 vendors. - Operate production inference systems, improving reliability, observability, and cost efficiency at scale. - Work with customers to understand performance needs and solve technical problems. WHAT WE'RE LOOKING FOR - Typically 1 to 6 years of experience in backend, infrastructure, systems, or related engineering, with strong low-level engineering skills. - Strong understanding of computer architecture, memory hierarchy, and operating system internals. - Experience with CUDA, HIP, Triton, or other GPU programming, and familiarity with Python and accelerator platforms such as TPU or AWS Trainium. - Background deploying or operating large-scale infrastructure, clusters, or GPU fleets is valuable. - Familiarity with machine learning inference optimization techniques such as quantization, batching, KV caching, or speculative decoding is valuable. - Strong computer science fundamentals, a record of solving demanding technical problems, and experience using AI coding tools to improve workflows. - Comfort working directly with customers and contributing across a broad range of engineering challenges. COMPENSATION & BENEFITS Base salary is $200,000 to $300,000 annually. Visa sponsorship is available. LOCATION On-site in San Francisco, California.