Research Engineer, Infrastructure, Training Systems

Job summary

Responsibilities

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…

• 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.

What they're looking for

• 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.

Tech stack

• PyTorch
• JAX