Connectionism: Research Blog by Thinking Machines Lab. Backed by a16z, Accel and GV.
About the role
We’re looking for an infrastructure research engineer to design, optimize, and maintain the compute foundations that power large-scale language model training. You will develop high-performance ML kernels (e.g., CUDA, CuTe, Triton), enable efficient low-precision arithmetic, and improve the distributed compute stack that makes training large models possible.
What they're looking for
- Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar
- Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases
- Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures
- Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts
- A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships
- Proficiency in CUDA, CuTe, Triton, or other GPU programming frameworks
More about this role
The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it.
We’re looking for an infrastructure research engineer to design, optimize, and maintain the compute foundations that power large-scale language model training. You will develop high-performance ML kernels (e.g., CUDA, CuTe, Triton), enable efficient low-precision arithmetic, and improve the distributed compute stack that makes training large models possible.
This role is perfect for an engineer who enjoys working close to the metal and across the research boundary. You’ll collaborate with researchers and systems architects to bridge algorithmic design with hardware efficiency. You’ll prototype new kernel implementations, profile performance across hardware generations, and help define the numerical and parallelism strategies that determine how we scale next-generation AI systems.
Note: This is an "evergreen role" that we keep open on an on-going...
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