Kernel Infrastructure Engineer for Large-Scale ML

Bison; San Francisco (CA); On-site; USD 400,000 - 500,000; Full time

Benefits offered by this job

Health, dental, and vision benefits

Unlimited PTO

Paid parental leave

Relocation support

Job summary

Thinking Machines in San Francisco is hiring an infrastructure research engineer to design, optimize, and maintain the compute foundations for large-scale language model training. You will develop high-performance ML kernels (CUDA, CuTe, Triton) and improve the distributed compute stack for scalable AI.

You’ll collaborate with researchers to bridge algorithmic design with hardware efficiency, prototype new kernel implementations, profile across GPU generations, and help define numerical and

Qualifications

• Bachelor’s degree or equivalent in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.

Responsibilities

• Design and implement custom ML kernels (e.g., CUDA, CuTe, Triton) for core LLM operations such as attention, matrix multiplication, gating, and normalization, optimized for modern GPU and accelerator architectures.
• Design and think through compute primitives to reduce memory bandwidth bottlenecks and improve kernel compute efficiency.
• Collaborate with research teams to align kernel-level optimizations with model architecture and algorithmic goals.
• Develop and maintain a library of reusable kernels and performance benchmarks that serve as the foundation for internal model training.
• Contribute to infrastructure stability and scalability, ensuring reproducibility, consistency across precision formats, and high utilization of compute resources.
• Document and share insights through internal talks, technical papers, or open-source contributions to strengthen the broader ML systems community.

Skills

Strong engineering skills

Collaboration

Profiling & optimization

CUDA/CuTe/Triton

Deep learning frameworks (PyTorch/JAX)

Debug complex codebases

Education

Bachelor’s degree or equivalent in CS/EE/ML/Physics

Tools

CUDA

CuTe

Triton

PyTorch

JAX

XLA

TVM

Job description

Thinking Machines in San Francisco is hiring an infrastructure research engineer to design, optimize, and maintain the compute foundations for large-scale language model training. You will develop high-performance ML kernels (CUDA, CuTe, Triton) and improve the distributed compute stack for scalable AI.

You’ll collaborate with researchers to bridge algorithmic design with hardware efficiency, prototype new kernel implementations, profile across GPU generations, and help define numerical and