Thinking Machines Lab

Research Engineer, Infrastructure, Training Systems

San Francisco

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

ExpressMachine LearningDeep LearningPyTorchStatisticsA/B TestingLogisticsRoboticsElectrical EngineeringResearch

About the role

Your goal is to make experimentation and training at Thinking Machines fast and reliable to ensure our research teams can focus on science, not system bottlenecks.
This role is ideal for someone who blends deep systems and performance expertise with a curiosity for machine learning at scale.
Note: This is an "evergreen role" that we keep open on an on-going basis to express interest.

Design, implement, and optimize distributed training systems that scale across thousands of GPUs and nodes for large-scale training workloads.
Develop high-performance optimizations to maximize throughput and efficiency.
Develop reusable frameworks and libraries to improve training reproducibility, reliability, and scalability for new model architectures.
Collaborate with researchers and engineers to build scalable infrastructure.
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

we encourage you to apply if you meet some but not all of these:
Past experience working on distributed training for the world's largest models to make them stable, reliable, and performant.
Contributions to open-source ML infrastructure such as PyTorch, XLA, Megatron-LM, or DeepSpeed.

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.