About this role
Qualifications
* Hands-on experience developing GPU kernels.
Nice to haves
* Prior work on kernel autoresearch frameworks is a **very strong positive**.
* Experience with ML compilers is a **strong positive**, but not required.
* Hardware performance analysis, memory hierarchies, parallel execution, and benchmarking.
* CUDA, Triton, GPU instruction sets, or compiler systems such as MLIR/LLVM and PyTorch backends.
* Rust, open-source compiler contributions, or work on other accelerators.
No degree requirement. Researchers, recent PhDs, and candidates with substantial relevant projects are welcome.
Company
Luminal (YC S25) builds an AI compiler and serving stack that optimizes models for faster, more efficient execution across accelerators. We're a small team working in person in San Francisco.
Role
Research and build faster kernels for AI workloads. You'll work closely with the compiler team, investigate performance bottlenecks, and take optimizations through correctness checks, benchmarking, and execution on real hardware.
Our focus includes:
* Agent-driven systems that write, test, and improve kernels.
* Automated kernel research and search-based performance optimization.
* More efficient execution for inference and LLM training.
Candidates who require a US visa are welcome to apply.