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Thinking Machines LabThinking Machines LabSan Francisco, CA

Research Engineer, Infrastructure, Training Systems

Designs and optimizes distributed training systems scaling across thousands of GPUs for large AI models. Requires strong systems engineering, PyTorch/JAX expertise, and collaborative mindset to boost research productivity.

350k – 475k/yr
On-siteDevOps / SRE

About the role

What You’ll Do

  • 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.
  • Establish standards for reliability, maintainability, and security, ensuring systems are robust under rapid iteration.
  • Collaborate with researchers and engineers to build scalable infrastructure.
  • Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure.

Skills and Qualifications

Minimum qualifications:

  • Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
  • Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases.
  • Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
  • Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
  • A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

Preferred qualifications:

  • Past experience working on distributed training for the world’s largest models to make them stable, reliable, and performant.
  • Track record of improving research productivity through infrastructure design or process improvements.
  • Contributions to open-source ML infrastructure such as PyTorch, XLA, Megatron-LM, or DeepSpeed.

Logistics

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

Benefits: Generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Skills

PyTorchJAXDistributed TrainingGpusDeepspeedMegatron-LmXlaKubernetesCUDAMl Frameworks

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