Luminal

Luminal optimizes AI models to accelerate and simplify model deployment using a search-based compiler. The AI stack needs to be rethought from the ground up to achieve this. As demand for inference grows, teams will need to run models across a wider range of hardware, not just the platforms with the most mature software support. Luminal makes AI workloads faster, more portable, and easier to deploy by automatically optimizing models for the ideal hardware. Our mission is to make state-of-the-art AI production-ready on any compute platform. We have already closed multiple contracts with non-Nvidia hardware platforms. Luminal is backed by Y Combinator and Felicis as well as top-tier angels such as Paul Graham (founder of Y Combinator), Guillermo Rauch (founder of Vercel) and many others. What You’ll Do Design and build core compiler infrastructure in Rust Build search-based optimization systems for discovering faster kernels Develop backend code generation for multiple targets (NVIDIA, Trainium, AMD, TPU, etc.) Implement compiler passes for fusion, scheduling, memory planning, and kernel selection Profile real models, identify bottlenecks, and improve latency and throughput Help shape Luminal’s engineering culture from the ground up What we’re looking for Experience with compiler frameworks: LLVM, MLIR, or other custom compilers Hands-on experience with at least one of: PTX/SASS, GCN/RDNA assembly, or other GPU ISAs Familiarity with ML compilers: torch.compile (or custom PyTorch backend), XLA, TVM, etc. Nice to haves Background in distributed systems or multi-device compilation Experience with Egg/Egglog is a STRONG plus Experience with Rust Experience in developing and deploying AI models in production environments Role Description This is a full-time on-site role for a Founding Compiler Engineer located in downtown San Francisco. You will be responsible for assisting the design of the core compiler. Day-to-day tasks will include writing CUDA kernels, conducting model performance reviews, and shitposting on social media.

Qualifications CUDA + GPU inference optimization vLLM, SGLang, or TensorRT-LLM experience KV caching, paged attention, batching, token streaming, etc. Distributed compute (with GPUs is a super plus) No degree required Company Luminal (YC S25) builds an AI compiler and serving stack that makes models 10x faster and production ready with one line. Role Founding, on site in downtown SF. Ship low latency, high throughput model serving on Luminal Cloud. Day to day responsibilities: Deploy and tune models with optimizations like KV caching, paged attention, sequence packing, etc. Conducting model performance reviews Improve scheduler, batcher, autoscaling; profile latency, cost, utilization Sometimes write kernels and, yes, occasional tasteful shitposting