5-7 years of software engineering experience in MLOps or ML platform engineering
Proficient with distributed training frameworks like PyTorch DDP or similar
Hands-on experience with experiment tracking and artifact management systems
Familiar with GPU cluster environments management (Slurm, Kubernetes, etc.)
Strong skills in reliability engineering, including monitoring and failure recovery
Responsibilities
Build and maintain training orchestration systems for GPU clusters
Develop tools for job configuration, tracking, and reproducibility
Create observability infrastructure for monitoring training runs
Optimize the research iteration loop for efficiency
Manage job scheduling for GPU cluster utilization
Build internal interfaces to accelerate researcher workflows
Collaborate with teams to enhance platform support
Benefits
High visibility and direct feedback from users on the platform
Opportunity to influence training velocity and reliability across experiments
Work on systems that scale for future advanced model training
Be part of a team that builds critical tools used by researchers and engineers
Full Job Description
We're looking for a Research Engineer to build and maintain the training platform that powers our model development - experiment orchestration, job management, observability, and the tooling that lets researchers move from idea to result as fast as possible.
What You'll Do
Build and maintain training orchestration systems for large-scale distributed model training across GPU clusters
