We look for infrastructure engineers who are excited to tackle unsolved problems. Everything we do - training, evaluation, serving - runs on our GPU fleet. Your mission is to design, build, and operate the supercomputing environment underneath it all, delivering performant, reliable, and cost-efficient compute to ensure research is able to iterate rapidly at scale.
Responsibilities- Design, deploy, and operate large distributed GPU clusters end to end: provisioning, imaging, upgrades, and capacity planning
- Extend scheduling and orchestration systems (e.g. Kubernetes, Slurm) for topology-aware placement, preemption, quotas, and multi-tenancy across training and inference workloads
- Build software that abstracts cluster management and presents a unified, self-serve interface to researchers and engineers
- Own cluster storage and artifact paths for checkpoints and logs, with clear retention and lineage
- Monitor and continuously improve reliability and error recovery; build the observability to catch failures before researchers do
- Partner with researchers to unblock large-scale runs and advise on performance and placement trade-offs
What we're looking forWe value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
- Experience operating large-scale GPU clusters and container orchestration frameworks (e.g. Kubernetes, Slurm, Docker)
- Strong systems background: Linux, networking, storage, infrastructure-as-code
- Knowledge of cloud platforms (GCP, AWS, or Azure) and their ML/AI service offerings
- Understanding of monitoring, logging, observability, and version control best practices for ML systems
- Familiarity with CUDA/NCCL and performance profiling for distributed workloads
- Owns deliverables end-to-end, from requirements through autonomous execution
