Founding Engineer - ML Performance

uRun

San Francisco (CA)

On-site

USD 120,000 - 160,000

Full time

14 days+

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Benefits offered by this job

Health, dental, and vision

401(k) participation

Flexible spending accounts

Paid time off

Access to AI tools

MacBook Pro and AirPods

Job summary

uRun, located in San Francisco, is seeking a founding ML Performance Engineer to drive AI infrastructure performance. In this role, you will write custom CUDA kernels and optimize model inference for real-time applications, significantly impacting performance across the stack.

The ideal candidate will possess deep knowledge of CUDA, experience with AI workloads, and a strong capacity for optimization. The position offers a competitive salary, equity, and top-tier tools for an exceptional contributor.

Qualifications

Deep, hands-on CUDA expertise with production kernels.
Strong background in model inference at scale.
Fluency in hardware-aware algorithm design.

Responsibilities

Write custom CUDA kernels for performance improvements.
Optimize model inference targeting sub-50ms latency.
Profile and benchmark full inference pipeline.

Skills

CUDA expertise

Model inference optimization

GPU memory hierarchy knowledge

Benchmarking and profiling

Tools

PyTorch

TensorRT

Job description

The problem we saw

Most AI infrastructure is built for batch: send a query, wait, get a response, reset. Powerful, but transactional. AI is becoming interactive — sessions that hold state, models that stay alive between turns, generation that responds as it runs — and the infrastructure to deliver that at scale doesn't really exist yet.

The bottleneck isn't the models anymore. It's the infrastructure underneath them.

What we're building to fix it

uRun is the inference cloud for interactive AI: the compute layer that makes real-time, stateful inference possible at scale. We came out of stealth in April 2026, are backed by top-tier investors, and are founded by Keegan McCallum, who scaled inference infrastructure for some of the most demanding generative AI workloads in production.

We're an infrastructure company. We build the layer that model labs, builders, and research teams ship on top of.

Where you come in

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, push GPU utilization to its limits, and own inference latency end-to-end across the stack. You will work directly with the founding team on the hardest performance problems in production AI infrastructure — and your fingerprints will be 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

What skills you need for the journey

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

Things that will give you an edge

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

What you'll get in return

Competitive salary and meaningful equity in an early-stage AI infrastructure company. The band above is our target; for an exceptional candidate we'll go higher. Equity is real — you're early, and the grant reflects that.

Health, dental, and vision
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

How we work (and what that feels like day-to-day)

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, and you'll often be the person who decides what "secure enough, for now" means. That suits some people and not others, and we'd rather you know that before you apply.