Research Engineer, Infrastructure, Kernels
Thinking Machines Lab · San Francisco
CHECKED 13H AGO
Frontier labonsite$350k–$475k
At a glance
Highlights
- Build compute foundations powering large-scale language model training.
- Prototype new kernels and profile performance across hardware generations.
- Define numerical and parallelism strategies to scale next-generation AI systems.
What they offer
- Generous health, dental, and vision benefits
- Unlimited PTO
- Paid parental leave
- Relocation support as needed
Ideal candidate
Low-level performance enthusiast who loves bridging research and hardware efficiency. Collaborative, action-oriented engineer excited to optimize large-scale training and share insights with the ML systems community.
Requirements
- Bachelor's degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar
- Strong engineering skills with performant, maintainable code and debugging in complex codebases
- Understanding of deep learning frameworks and their underlying system architectures
- Thrive in a highly collaborative environment with cross-functional partners and experts
- Bias for action with initiative to work across stacks and teams to ship
- Proficiency in CUDA, CuTe, Triton, or other GPU programming frameworks
- Demonstrated ability to analyze, profile, and optimize compute-intensive workloads
- visa sponsorship
- We sponsor visas and are committed to working through the visa process together if you're the right fit.
- hiring process
- Evergreen role kept open on an ongoing basis; applications reviewed continuously as opportunities open; please do not reapply more than once every 6 months.
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