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.