Member of Technical Staff

About the Role

Join a small AI infrastructure team building efficient inference for open-source language models. You will improve performance and reliability across diverse hardware, helping make production AI faster and more cost-effective.

What You'll Do

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

• Optimize inference serving through batching, KV cache management, 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 hardware vendors.

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

• Work with customers and teammates to identify problems and shape technical solutions.

What We're Looking For

• Typically 1 to 6 years of experience in backend, infrastructure, systems, or related engineering work, including early-career candidates with strong internships.

• Strong computer science fundamentals, including computer architecture, memory hierarchy, and operating system internals.

• Systems engineering experience beyond application-level or web development, with ownership of challenging technical work.

• Familiarity with CUDA, Triton, GPU programming, or comparable low-level systems technologies; experience with HIP is useful.

• Experience with infrastructure at scale, such as clusters, data centers, or large GPU fleets, and familiarity with inference optimization techniques.

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

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

Compensation & Benefits

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

Location

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