HPC Infrastructure Engineer - GPU Clusters

Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible.

Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities.

Learning & development: ElevenLabs proactively supports professional development through an annual discretionary stipend.

Social travel: We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose.

Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy.

Co-working: If you’re not located near one of our main hubs, we offer a monthly co-working stipend.

We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $11B - multiples of 11, always.

We have expanded from voice into three main platforms:

Every model we train runs on infrastructure this role owns. We operate NVIDIA GPU clusters across bare metal and rented capacity, and we're looking for an engineer to join our small research infrastructure team and make that compute fast, reliable, and boring - in the best sense. When the clusters just work, research moves faster. Your impact is measured directly in training throughput and researcher velocity.

This is a builder-operator role with real breadth: one week you're writing automation that eliminates a whole class of manual work, the next you're benchmarking a new provider's InfiniBand fabric or on-site bringing new hardware online. You'll have unusual scope and autonomy - we're a lean team where decisions are made by the people closest to the problem.

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

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

Small team, high trust, minimal bureaucracy. We automate aggressively so on-call is sane, and we fix root causes so the same page never fires twice. You'll work directly with the researchers whose jobs run on your clusters — short feedback loops, no ticket queues between you and impact.