Founding Engineer - ML Performance

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.