HPC Infrastructure Engineer - GPU Clusters

ElevenLabs is an AI research and product company transforming how we interact with technology. 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 $22B - multiples of 11, always.

We have expanded from voice into three main platforms:

• ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale.

• ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages.

• ElevenAPI gives developers access to our leading AI audio foundational models.

Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you.

• 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

• 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

• 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