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.

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.
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.

Senior ML Platform Engineer

Key skills