Research Engineer, Infrastructure, Training Systems

Highlights

• Design distributed training systems scaling across thousands of GPUs and nodes.
• Maximize throughput and efficiency so every GPU cycle drives scientific progress.
• Build reusable frameworks to improve reliability and scalability of training.

What they offer

• Generous health, dental, and vision benefits
• Unlimited PTO
• Paid parental leave
• Relocation support as needed

Ideal candidate

Systems-minded engineer energized by large-scale ML who takes end-to-end ownership to make training fast and reliable. Collaborative and action-biased, you care about removing bottlenecks so researchers can focus on science.

Requirements

• 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