Requirements

• Strong experience building or operating production systems with a focus on reliability, scalability, and maintainability.
• A systems mindset: you naturally think in terms of bottlenecks, failure modes, interfaces, resource usage, and long-term operability.
• Solid hands-on experience with cloud infrastructure, Linux, and infrastructure automation.
• Experience with Kubernetes and operating distributed workloads in production.
• Strong coding skills, ideally in Python or similar languages used for backend systems and tooling.
• Strong judgment around where automation adds leverage, and where human control and reliability matter most.
• Experience building internal platforms, developer tooling, or infrastructure abstractions used by other engineers.
• Comfort working in ambiguous environments and taking ownership of open-ended technical problems.
• A pragmatic approach: you care about solving the right problem well, not over-engineering.

What you'll be doing

• Design and improve the platform systems that support model training, evaluation, and production serving.
• Build infrastructure and tooling that make ML workloads more reliable, scalable, and cost-efficient.
• Develop internal tools and workflows that are easy to operate both by humans and by agents.
• Work on the architecture behind how models are deployed, served, and operated across research and product environments.
• Improve how we schedule, monitor, and debug workloads running on GPUs and cloud infrastructure.
• Develop internal tools and abstractions and agentic systems that reduce operational overhead for researchers and engineers.
• Drive improvements across observability, automation, reliability, and developer experience.
• Collaborate closely with researchers and product engineers to understand pain points and turn them into robust platform capabilities.
• Contribute to technical direction and make pragmatic architectural tradeoffs as the platform grows.

Particularly relevant experience

• Operating ML infrastructure or model serving systems in production.
• Supporting research or data-intensive workloads.
• Working with GPU-based systems or other performance-sensitive infrastructure.
• Experience with observability and debugging in distributed systems.
• Familiarity with Terraform, Datadog, GitHub Actions, or similar tools.

Bonus points for

• Experience building agentic or LLM-powered internal tools.
• Experience with workflow orchestration systems such as Temporal.
• Experience working at the boundary between research and production engineering.
• Familiarity with performance optimization, scheduling, or resource allocation problems.
• Experience building lightweight product or developer-facing tools.