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Skills mentionedCUDAPythonC++
About the Company
We're building autonomous research agents for recursive self-improvement (multi-agent systems that propose, run, and analyze machine learning experiments). We're a small, well-funded team that works onsite in San Francisco.
About the Role
You'll write and optimize the GPU kernels and supporting systems software that makes our training and inference workloads fast. This is deep, low-level work (performance counters, memory bandwidth, warp-level scheduling) applied to the specific shapes and patterns our models actually use.
We hire kernel engineers because the gap between "this works" and "this is fast on the hardware we have" is enormous, and that gap directly bounds what our researchers can try. You'll close that gap.
What You'll Do
Write and optimize GPU kernels (CUDA, ROCm, Triton, or similar) for training and inference workloads: attention variants, MoE layers, custom activations, communication primitives
Profile real workloads with hardware counters and translate findings into specific kernel-level optimizations
Co-design kernels with the research teams, when the kernel and the algorithm need to change together, you participate in both
Integrate optimized kernels into our training and serving stacks; benchmark before and after; verify the win is real end-to-end
Maintain kernel quality over time as hardware, frameworks, and workloads shift underneath
Spread kernel-level fluency across the team; we want this expertise shared, not siloed
What We're Looking for
4+ years writing performant GPU kernels (CUDA, ROCm, Triton, or production-grade equivalent)
