The San Francisco Tensor Company is looking to solve the problem of bottlenecks across hardware, cloud, and code optimization that slow progress in AI and high-performance computing by developing a Kernel Optimizer, Tensor Cloud, and Emma Lang.
Requirements
- Strong background in reinforcement learning, with hands-on experience training RL agents on real problems
- Experience building LLM agents, tool use, or other agentic systems
- Good familiarity with GPU programming concepts
- Solid proficiency in Python and PyTorch or JAX
- Ability to design and run solid, rigorous experiments
- Experience with ML compiler stacks (XLA, TVM, Triton, MLIR)
- Experience with RLHF, reward modeling, or preference learning
Responsibilities
- Design and implement RL-based systems for compiler optimization (things like phase ordering, tile size selection, scheduling decisions, and fusion strategies)
- Build agentic compilation systems that use LLMs to reason about code and apply transformations
- Develop reward models and the training infrastructure for our compiler optimization agents
- Create representations and embeddings of compiler IR that work well for learned optimization
- Design feedback loops that let the system improve continuously from real production workloads
- Work closely with compiler engineers to integrate these learned components into the full compilation pipeline
- Run experiments, dig into the results, and iterate on what works
Other
- Publish and open-source research when it makes sense
- Relocation assistance is offered
- Must be able to work in-person in San Francisco office
- Bachelor's, Master's, or Ph.D. degree (not specified)
- Bonus + equity + benefits are included in the compensation package
