kernel
ABOUT THE ROLE
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.
WHAT YOU'LL DO
- 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.
WHAT WE'RE LOOKING FOR
- 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,