San Francisco Tensor Company · San Francisco, United States
We're building the fastest GPU compiler in the world. Most compilers have to preserve correctness at every transform, constraining how far they can search, while we prove correctness at the end instead, allowing us to search a far wider space, with agents, with RL, with anything that works and still guarantee the result. It's why we hold #1 on NVIDIA's own kernel benchmark across hundreds of production kernels.
We're hiring a Member of Technical Staff for AI-Driven Compilation to own the search itself. Proving correctness at the end is what makes the search legal and your job is to make it good.
You'll design and implement RL systems that search over a massive program space: instruction selection, schedules, tile sizes, fusion strategies and phase ordering
You'll build agentic compilation loops that use LLMs to reason about IR, propose transformations and learn from measured results
You'll design the search and credit-assignment machinery around an exact reward built from measured latency on real hardware and a formal correctness proof
You'll create representations and embeddings of compiler IR that hold up under learned optimization
You'll build the training infrastructure for compiler optimization agents, including rollout throughput and distributed evaluation on real silicon
You'll push transfer and cold-start performance so that the search works on unfamiliar targets and new ISAs from the first trial
You'll close the loop with production workloads so that the compiler keeps improving from what customers actually run
You'll work directly with our compiler and kernel engineers to land learned components in the shipping pipeline
You'll run rigorous experiments, dig into the results, iterate on them and publish or open-source some of them
Someone with a strong background in reinforcement learning with hands-on experience training agents
Someone with experience building LLM agents, tool use and other agentic systems
Someone with familiarity in GPU programming concepts and a willingness to get down to the ISA
Someone proficient in PyTorch or JAX
Someone with the ability to design and run experiments that produce trustworthy results
Nice to Have: Someone with experience in ML compiler stacks (XLA, TVM, Triton, MLIR) or LLVM backends
Someone with a background in program synthesis, superoptimization, combinatorial search, formal methods, SMT solvers or verified compilation
Someone familiar with RLHF, reward modeling or preference learning
Someone with research contributions in RL or learned optimization
Someone familiar with GPU performance optimization, profiling and microbenchmarking