Research Engineer Infrastructure Training Systems

About the role

We’re looking for an infrastructure research engineer to design and build the core systems that enable scalable, efficient training of large models for deployment and research. 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.

Responsibilities

• 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.

Qualifications

• Minimum qualifications: Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, 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.
• 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.

Skills

• 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

Location: This role is based in San Francisco, California.
Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
Visa sponsorship: 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.
Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Equal Employment Opportunity

Thinking Machines Lab will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the California Fair Chance Act, the San Francisco Fair Chance Ordinance, and any other applicable state or local fair chance ordinance or law.