HPC Infrastructure Engineer - GPU Clusters

ElevenLabs; Greater London; Hybride; GBP 120 000 - 180 000; Plein temps

Avantages offerts par ce poste

Remote-friendly

Global team

Office options: London, New York, San 

Francis

Résumé du poste

ElevenLabs is hiring a remote-first Infrastructure Engineer to own our GPU compute infrastructure, from provisioning to maintenance, across bare-metal and rented capacity. You’ll optimize training throughput and reliability, writing automation in Python/Bash and managing the NVIDIA stack.

You’ll collaborate with a lean, global team, with opportunities to work from our offices or remotely. We value builders who own problems end to end and ship high-quality infrastructure for research.

Qualifications

• 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

Responsabilités

• 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

Connaissances

Linux server admin

GPU compute environments

Automation scripting

End-to-end ownership

Performance troubleshooting

Security best practices

Outils

Ansible

Terraform

Slurm

CUDA drivers

NCCL

DCGM

InfiniBand/RoCE networking

Python

Bash

PromQL

Description du poste

About ElevenLabs

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.

How we work

• High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy.

• Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you.

• AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations.

• Excellence everywhere: Everything we do should match the quality of our AI models.

• Global team: We prioritize your talent, not your location.

What we offer

• 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.

About the role

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.

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

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

Location

This role is remote and can be executed globally. If you prefer, you can work from our offices in London, New York, San Francisco, and Warsaw.

#LI-Remote

We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.