Member of Technical Staff

About the Role

Join a small team building AI infrastructure that improves the performance and efficiency of open-source model inference. You will work across models, serving systems, kernels, and production infrastructure to help deliver reliable, cost-effective inference across diverse hardware.

What You'll Do

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

• Optimize serving systems through techniques such as batching, KV caching, speculative decoding, and quantization.

• Write and tune 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 per token.

• Work with customers as well as the engineering team to identify problems and guide solutions.

What We're Looking For

• One to six years of experience, with strong systems, backend, infrastructure, or low-level engineering work.

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

• Experience owning complex systems end to end, notable technical achievements, or a record of rapid progression.

• Strong undergraduate computer science fundamentals and experience at a high-caliber technology organization or early-stage startup.

• Familiarity with CUDA, HIP, Triton, Python, or GPU programming; experience with inference optimization and large-scale infrastructure is valuable.

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

• Available to start immediately and work on-site in San Francisco.

Compensation & Benefits

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

Location

On-site in San Francisco, California, United States.