We build the stage, not the show. We're an infrastructure company, a developer-tools company, and a production partner for model labs, and focus is a deliberate choice we've made and hold to. Day-to-day, that means a small team, a high bar, and real ownership. You won't wait for permission or inherit a backlog of someone else's decisions. In a founding security role, the function is what you make it. It also means ambiguity: priorities shift, not everything is documented. You'll often be the person who decides what "secure enough, for now" means.
Founding Engineer - ML Performance
Location
United States
Posted
7 days ago
Salary
$250K - $395K / year
Seniority
Mid Level
Job Description
Founding Engineer - ML Performance
uRun
Role Description Performance is uRun's core differentiator. We're not chasing incremental gains — we're building infrastructure that runs 10–100x faster than the status quo. As our ML Performance Engineer, you will be the person who makes that true. This is a founding technical hire. You will: - Write custom CUDA kernels, pushing GPU utilization to its limits. - Own inference latency end-to-end across the stack. - Work directly with the founding team on the hardest performance problems in production AI infrastructure. - Have your fingerprints on everything we ship. What you'll actually be doing day-to-day: - Write custom CUDA kernels that unlock performance headroom unavailable through off-the-shelf frameworks. - Optimize model inference end-to-end, targeting sub-50ms latency across our inference platform. - Drive 10x performance improvements across the stack: memory bandwidth, kernel fusion, operator scheduling, and beyond. - Implement zero-copy distributed memory optimizations across multi-GPU and multi-node environments. - Own GPU utilization and memory management, squeezing every available FLOP out of the hardware we run. - Profile, benchmark, and instrument the full inference pipeline to find and eliminate bottlenecks systematically. - Set the performance engineering bar for the team: define what fast looks like and build the tooling to measure it. Qualifications - Deep, hands-on CUDA expertise: you have written custom kernels in production, not just called into cuBLAS. - Strong background in model inference and post-training optimization at scale. - Fluency in GPU memory hierarchy, warp scheduling, kernel fusion, and hardware-aware algorithm design. - Experience profiling and benchmarking complex inference pipelines: you know where the time goes and how to get it back. - Able to operate at the frontier with minimal guidance — you identify the problem, design the approach, and ship the fix. Requirements - Public work in GPU optimization or inference efficiency — open source contributions, a published paper, or a side project that shows your depth (vLLM, Flash-Attention, TensorRT-LLM, PyTorch, or equivalent). - Experience with hardware-aware optimization frameworks: CuTe, Triton, TileLang, or similar. - Familiarity with distributed memory and communication primitives: NCCL, InfiniBand, NVLink, RoCE. - Contributions to or deep familiarity with PyTorch Distributed, Ray core, or similar systems. - Experience optimizing for video generation or other high-throughput, latency-sensitive generative workloads. - Prior work at an inference-focused company or research lab pushing the boundary of what GPU hardware can do. Benefits - Competitive salary and meaningful equity in an early-stage AI infrastructure company. - Health, dental, and vision — full coverage. - 401(k) — company-supported retirement savings. - FSA/HSA — flexible spending accounts for healthcare costs. - Paid time off — we trust you to manage your time. - Top-tier tooling — access to the best AI tools available: Claude, Codex, Kimi, and whatever else helps you move faster. - MacBook Pro and AirPods — the hardware you need, on us.
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