Research Engineer, Infrastructure, Kernels
Thinking Machines Lab · San Francisco
Frontier labonsite$400k–$500k
At a glance
Highlights
- Design compute foundations powering large-scale language model training.
- Bridge algorithmic design with hardware efficiency alongside researchers.
- Help define numerical and parallelism strategies for next-gen AI scaling.
What they offer
- Health, dental and vision benefits
- Unlimited PTO
- Paid parental leave
- Relocation support as needed
Ideal candidate
A low-level systems thinker who loves working close to the metal and thrives collaborating with researchers to make large-scale training faster and more efficient.
Requirements
- 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 and their underlying system architectures
- Thrive in a highly collaborative environment with cross-functional partners
- Bias for action with mindset to take initiative across stacks and teams to make sure something ships
- Proficiency in CUDA, CuTe, Triton, or other GPU programming frameworks
- Demonstrated ability to analyze, profile, and optimize compute-intensive workloads
- visa sponsorship
- Visa sponsorship available
- hiring process
- Evergreen role reviewed on an ongoing basis, with outreach as new opportunities open.
Lab profile
About Thinking Machines Lab
Builds frontier AI that extends human will and judgment through customizable models and interfaces.
What they build
- Inkling frontier models
- Tinker model customization
- Human-AI communication interfaces
Known for
- Frontier model training
- Post-training and reinforcement learning
- Evaluation infrastructure
- GPU supercomputing and reliability
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