Design and improve platform systems for model training, evaluation, and 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.
Architect deployment, serving, and operation of models across research and product.
Improve scheduling, monitoring, and debugging of GPU and cloud workloads.
Drive observability, automation, reliability, and developer experience improvements.

🎯 Requirements

Production systems reliability, scalability, and maintainability.
Systems mindset: bottlenecks, failure modes, interfaces, resource usage.
Cloud infrastructure, Linux, and infrastructure automation.
Kubernetes and distributed workloads in production.
Strong Python or similar backend languages for tooling.
Familiarity with Terraform, Datadog, GitHub Actions, or similar tools.

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