AI Infrastructure Kernel Engineer for Large-Scale Training

Thinking Machines Lab Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 350,000 - 475,000; Full time

Benefits offered by this job

Health, dental, and vision benefits

Unlimited PTO

Parental leave

Relocation support

Job summary

Thinking Machines Lab Inc. in San Francisco seeks an infrastructure research engineer to design, optimize, and maintain the compute foundations powering large-scale language model training.

You will develop high-performance ML kernels (CUDA, CuTe, Triton) and improve the distributed compute stack for scalable AI systems. You’ll collaborate with researchers and systems architects, prototype kernel implementations, and profile performance across hardware generations to shape numerical and

Qualifications

• Bachelor’s degree or equivalent experience in a technical field.
• Strong engineering skills with maintainable code and debugging in complex codebases.
• Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their system architectures.
• Ability to collaborate with cross-functional teams and take initiative across stacks.

Responsibilities

• Design and implement custom ML kernels (CUDA, CuTe, Triton) for core LLM operations such as attention and matrix multiplication.
• Design and optimize compute primitives to reduce memory bandwidth bottlenecks and improve kernel efficiency.
• Collaborate with research teams to align kernel optimizations with model architecture goals.
• Develop and maintain a library of reusable kernels and performance benchmarks.
• Contribute to infrastructure stability, reproducibility, and high compute utilization.
• Document insights via talks, papers, or open-source contributions.

Skills

Kernel development

Performance profiling

GPU programming

Collaboration

Strong coding skills

Deep learning systems

Education

Bachelor’s degree in CS/EE/related

Tools

CUDA

CuTe

Triton

PyTorch/JAX

Job description

Thinking Machines Lab Inc. in San Francisco seeks an infrastructure research engineer to design, optimize, and maintain the compute foundations powering large-scale language model training.

You will develop high-performance ML kernels (CUDA, CuTe, Triton) and improve the distributed compute stack for scalable AI systems. You’ll collaborate with researchers and systems architects, prototype kernel implementations, and profile performance across hardware generations to shape numerical and