Thinking Machines Lab

Research Engineer, Infrastructure, Numerics

San Francisco

H1B sponsorship available$350k-$475kDetected 78 days ago

Node.jsExpressMachine LearningDeep LearningPyTorchLLMsStatisticsLogisticsSystems EngineeringRoboticsElectrical EngineeringResearchCommunication

About the role

You will focus on improving the numerical foundations of our distributed training stack, from precision formats and kernel optimizations to communication frameworks that make training trillion-parameter models stable, scalable, and fast.
This role is ideal for someone who thrives at the intersection of research and systems engineering: a builder who understands both the math of optimization and the realities of distributed compute.
Note: This is an "evergreen role" that we keep open on an on-going basis to express interest.

Design and optimize distributed training infrastructure for large-scale LLMs, focusing on performance, stability, and reproducibility across multi-GPU and multi-node setups.
Implement and evaluate low-precision numerics (for example, BF16, MXFP8, NVFP4) to improve efficiency without sacrificing model quality.
Develop kernels and communication primitives that use hardware-level support for mixed and low-precision arithmetic.
Collaborate with research teams to co-design model architectures and training recipes that align with emerging numeric formats and stability constraints.
Contribute to the design of our internal orchestration and monitoring systems to ensure that thousands of distributed experiments can run efficiently and reproducibly.
Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.

Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases in areas such as floating-point numerics, low-precision arithmetic, and distributed systems.

we encourage you to apply if you meet some but not all of these:
Familiarity with distributed frameworks such as PyTorch/XLA, DeepSpeed, Megatron-LM.
Experience implementing FP8, INT8, or block-floating point (MX) formats and understanding their numerical trade-offs.
Publications, patents, or projects related to numerical optimization, communication-efficient training, or systems for large models.
Experience training and supporting large-scale AI models.

Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence.

Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.

Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
Bachelor's degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.

While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
We sponsor visas.

This listing is sourced directly from Thinking Machines Lab's careers page and normalized into a canonical job model.