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)
Hardware-level fluency: memory hierarchy, occupancy, register pressure, tensor cores, warp scheduling
Profiling fluency (Nsight, ncu, or comparable tools) and the discipline to measure before changing
Track record of shipping kernel-level optimizations that moved a measurable metric in a real system
Strong systems expertise: you understand how kernels live inside larger frameworks and how integration choices affect end-to-end performance
Comfortable reading framework-level Python and C++ around your kernels
NICE TO HAVE
Open-source contributions to kernel libraries, compilers, or ML frameworks
Experience with multiple accelerator architectures (different GPU families, TPUs, custom ASICs), preferably AMD GPUs
Familiarity with collective communication primitives (NCCL or equivalent)
Compiler or runtime background
THIS ROLE IS PROBABLY NOT FOR YOU IF
You haven't gotten your hands dirty at the kernel level: this isn't a higher-level systems role rebranded
You want to stay narrowly in one library; we expect breadth across the kernel surface our models actually use
Performance work without measurable end-to-end impact frustrates you
Skills Required
- 4+ years writing performant GPU kernels using CUDA, ROCm, Triton, or a production-grade equivalent
- Fluency with memory hierarchy, occupancy, register pressure, tensor cores, and warp scheduling
- Profiling experience with Nsight, ncu, or comparable tools
- Track record of shipping kernel-level optimizations with measurable impact in a real system
- Strong systems expertise integrating kernels into larger frameworks and optimizing end-to-end performance
- Ability to read framework-level Python and C++ surrounding GPU kernels
- Open-source contributions to kernel libraries, compilers, or machine learning frameworks
- Experience with multiple accelerator architectures, preferably AMD GPUs
- Familiarity with collective communication primitives such as NCCL
- Compiler or runtime background
What We Do
MakerMaker.AI is an innovative company building autonomous research agents focused on recursive self-improvement. They develop sophisticated multi-agent systems that can independently propose, run, and analyze complex machine learning experiments. By creating agents that have the ability to build other agents, MakerMaker.AI seeks to accelerate the development of artificial intelligence and push the boundaries of autonomous research in machine learning.
