Software Engineer, Supercomputing

What You’ll Do

• Operate and automate large GPU clusters including provisioning, imaging, and capacity planning.
• Write software that abstracts cluster management and presents a unified interface for training and inference.
• Extend scheduling/orchestration (Kubernetes, Slurm, or similar) for topology‑aware placement, preemption, quotas, and fair‑share multi‑tenancy.
• Monitor and improve operational metrics of speed, reliability, and error recovery.
• Build reliable storage and artifact paths for datasets, checkpoints, and logs with clear retention and lineage.
• Partner with researchers to unblock scale runs and advise on parallelism and performance trade‑offs.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
• Proficiency in at least one backend language (Python or Rust).
• Experience operating large‑scale clusters and container orchestration systems (e.g. Kubernetes or Slurm).
• Comfort operating across the stack and owning projects end-to-end.
• 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:

• Strong systems background: Linux, networking, and infrastructure‑as-code.
• Familiarity with CUDA/NCCL and performance profiling for distributed training/inference.
• Prior work supporting large‑scale model training or inference environments.
• Understanding of deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and their underlying system architectures.
• Track record of working in fast-paced environments balancing care with urgency.

Compensation

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