Thinking Machines Lab

Thinking Machines Lab

Research Engineer, Infrastructure, Numerics

San Francisco

H1B sponsorship available$350k-$475kDetected 73 days ago
Node.jsExpressMachine LearningDeep LearningPyTorchLLMsStatisticsLogisticsSystems EngineeringRoboticsElectrical EngineeringResearchCommunication

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odds of building a lasting career here

59Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70

Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.

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Files H-1B transfers

16 transfer filings in the last year, covering 16 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.

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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.

Responsibilities

  • 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.

Requirements

  • 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.
  • 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.
  • Experience training and supporting large-scale AI models.

Nice to have

  • we encourage you to apply if you meet some but not all of these:

Compensation

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

Benefits

  • 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.

Company info

  • The mission of Thinking Machines is to build AI that extends human will and judgment.

Visa & Work Authorization

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