Research Engineer, Infrastructure, Kernels at ThinkingMachines

Department: Research Infrastructure (ML Infrastructure and Training Stack)

Compensation

$350,000 – $475,000/yr

Setup: On-site
Location: San Francisco, California
Type: Full-time
Level: not_specified
Posted: Aug 4, 2026

Description

Thinking Machines is hiring an Infrastructure Research Engineer to design, optimize, and maintain the compute foundations for large-scale language model training. The role focuses on developing high-performance ML kernels, improving low-precision arithmetic, profiling and optimizing GPU and accelerator workloads, and collaborating with researchers and systems architects. It is an evergreen role based in San Francisco, with an expected annual salary of $350,000–$475,000 USD and visa sponsorship.

Required Skills

CUDA
CuTe
Triton
PyTorch
JAX
Low-precision arithmetic
Tensor parallelism
Pipeline parallelism
Workload profiling

Additional Information

Minimum Education: Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or a similar field.
Required Previous Experiences: Demonstrated ability to analyze, profile, and optimize compute-intensive workloads.
Preferred Qualifications: Experience training or supporting large-scale language models with tens of billions of parameters or more.
Track record of improving research productivity through infrastructure design or process improvements.
Experience developing or tuning kernels for deep learning frameworks such as PyTorch, JAX, or custom accelerators.
Familiarity with tensor parallelism, pipeline parallelism, or distributed data processing frameworks.
Experience implementing low-precision formats or contributing to related compiler stacks.
Contributions to open-source GPU, ML systems, or compiler optimization projects.
Prior research or engineering experience in numerical optimization, communication-efficient training, or scalable AI infrastructure.
Translation Company Name: Thinking Machines Lab