Requirements

• Have run large-scale Linux server or GPU environments in production and enjoy both building and operating
• Know the NVIDIA stack well — drivers, CUDA, NCCL, DCGM — or have deep systems experience and learn hardware stacks fast
• Are comfortable with bare-metal environments, server hardware, and high-speed networking
• Write solid automation in Python and/or Bash, with IaC tools like Ansible or Terraform
• Are happy digging into noisy data (metrics, logs, PromQL) to find what’s actually wrong
• Like owning real scope end to end and being the person others rely on
• Don't consider any task above or beneath you — datacenter trips included

Nice to have

• Experience supporting ML training workloads from the infra side (distributed training failure modes, checkpointing patterns)
• Experience evaluating and working with GPU cloud providers
• Parallel filesystems (WEKA, VAST, etc) or large-scale object storage
• BMC/IPMI/Redfish automation, PXE provisioning at scale
• Power and cooling awareness for dense GPU deployments

What you'll be doing

• Operate and improve our GPU fleet end to end: provisioning, scheduling, monitoring, upgrades, capacity planning
• Build automation that keeps the fleet healthy without human intervention — node health checks, automated draining and remediation, burn-in pipelines for new capacity
• Own the stack beneath the training code: OS images, NVIDIA drivers, CUDA, container runtimes, NCCL, high-speed networking (InfiniBand/RoCE)
• Run and tune job scheduling (Slurm or similar) so researchers get compute fairly and fast
• Build and maintain high-performance storage for datasets and checkpoints
• Hunt down performance problems: stragglers, degraded links, thermal issues, flaky GPUs — and fix the class of problem, not just the instance
• Evaluate rented GPU capacity: benchmark it, validate it, hold providers to their SLAs
• Hands-on hardware work when it’s needed: racking, cabling, diagnostics, coordinating with datacenter staff and vendors
• Keep clusters secure by default: access control, network isolation, secrets

Perks and benefits

• Innovative culture: Be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing boundaries
• Growth paths: Dynamic team with countless opportunities to drive impact beyond immediate role
• Learning & development: Annual discretionary stipend for professional development
• Social travel: Annual discretionary stipend to meet up with colleagues
• Annual company offsite: Entire team meets yearly in a new location
• Co-working: Monthly co-working stipend if not near main hubs