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ElevenLabs is an AI research and product company that has raised $781M in funding with a $22B valuation. The company serves millions of users and thousands of businesses with three main platforms: ElevenAgents (voice and chat agents at scale), ElevenCreative (speech, music, image, and video generation across 70+ languages), and ElevenAPI (AI audio foundational models).
As an Infrastructure Engineer, you will build and operate the platform that all ElevenLabs teams depend on. You'll own infrastructure serving millions of users and thousands of businesses, working directly with research, product, security, and safety engineers to launch new infrastructure, scale it as needs grow, and lead migrations and platform improvements.
Key responsibilities include:
- Running and scaling Kubernetes clusters at scale, including GPU clusters for inference and training, across cloud and on-premises environments
- Setting up compute, networking, permissions, and access for other teams as they grow
- Hosting and operating data systems that product teams depend on
- Building tooling, automations, and services in Python that make infrastructure self-serve for the rest of the company
- Taking on large efforts such as cluster builds and cloud migrations, seeing them through end to end
The role is hands-on from day one in a flat team structure. You'll need strong opinions about doing things right while maintaining the pragmatism to move fast when parts of the stack aren't perfect.
REQUIREMENTS:
- Deep, hands-on experience running Kubernetes in production at scale
- Strong experience with GCP, including designing, building, and migrating cloud infrastructure end to end as an individual contributor
- Strong Python skills and a habit of solving infrastructure problems with code and automation
- Experience operating stateful systems (databases, Kafka) on behalf of other engineering teams
- Solid fundamentals in infrastructure as code, security and permissions, and pragmatism to prioritize what matters in a fast-moving environment
BONUS:
- Experience with GPU clusters or ML training/inference infrastructure (e.g., vLLM)
- On-premises/bare-metal experience
- Deep networking experience
- Open-source contributions in the cloud-native community
