Member of Technical Staff - GPU Performance Engineer

Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.

Our models and workflows require performance work that generic frameworks don’t solve. You’ll design and ship custom CUDA kernels, profile at the hardware level, and integrate research ideas into production code that delivers measurable speedups in real pipelines (training, post-training, and inference). Our team is small, fast-moving, and high-ownership. We're looking for someone who finds joy in memory hierarchies, tensor cores, and profiler output.

While San Francisco and Boston are preferred, we are open to other locations.

We need someone who:

• Authored custom CUDA kernels (not only calling cuDNN/cuBLAS)

• Strong understanding of GPU architecture and performance: memory hierarchy, warps, shared memory/register pressure, bandwidth vs compute limits

• Proficiency with low-level profiling (Nsight Systems/Compute) and performance methodology

• Strong C/C++ skills

• CUTLASS experience and tensor core utilization strategies

• Triton kernel experience and/or PyTorch custom op integration

• Experience building benchmark harnesses and perf regression tests

• Measurable improvement on at least one critical end-to-end pipeline (throughput and/or latency), validated by repeatable benchmarks

• At least one research-driven technique shipped as a production kernel and maintained over time

• Performance regressions are detectable early via benchmarks/guardrails, not discovered late