Research Engineer, Infrastructure, Numerics at ThinkingMachines

Department: Research Infrastructure (ML Infrastructure and Training Stack)

Compensation

$350,000 – $475,000/yr

Setup: Hybrid
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 and build core systems for efficient large-scale model training, with a focus on numerics. The role involves optimizing distributed training infrastructure, implementing low-precision formats, developing kernels and communication primitives, benchmarking parallelism strategies, and contributing to orchestration and monitoring systems. It is based in San Francisco, California, with an expected annual salary of $350,000–$475,000 USD and visa sponsorship.

Required Skills

Distributed training
Low-precision numerics
Kernel optimization
Communication frameworks
PyTorch and JAX
Floating-point numerics
Distributed systems

Additional Information

Minimum Education: Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or a similar field.
Preferred Qualifications: Familiarity with distributed frameworks such as PyTorch/XLA, DeepSpeed, and Megatron-LM.
Experience implementing FP8, INT8, or block-floating point formats and understanding their numerical trade-offs.
Prior contributions to open-source deep learning infrastructure such as PyTorch, DeepSpeed, or XLA.
Publications, patents, or projects related to numerical optimization, communication-efficient training, or systems for large models.
Experience training and supporting large-scale AI models.
Track record of improving research productivity through infrastructure design or process improvements.