ML Platform Engineer

📋 Description Design and improve the platform systems for training, evaluation, and production serving. Build infrastructure and tooling for reliable, scalable ML workloads. Develop internal tools and workflows operable by humans and agents. Shape deployment architecture across research and product environments. Improve scheduling, monitoring, and debugging of GPU and cloud workloads. Drive observability, automation, reliability, and developer experience. 🎯 Requirements Strong experience building/operating production systems focused on reliability, scalability, and maintainability. Systems mindset: bottlenecks, failure modes, interfaces, resource usage, operability. Hands-on experience with cloud infrastructure, Linux, and infrastructure automation. Experience with Kubernetes and operating distributed workloads in production. Strong coding skills, ideally Python or similar for backend systems. Experience building internal platforms, developer tooling, or infrastructure abstractions. Comfort working in ambiguous environments and taking ownership of open-ended technical problems.