ML Platform Engineer

📋 Description

• Design and improve platform systems for ML training, evaluation, deployment, and production serving.
• Build scalable infra and tools to improve reliability, efficiency, and cost of ML workloads.
• Develop automation workflows and internal tools to reduce operational complexity for researchers and engineers.
• Architect and maintain systems enabling deployment, monitoring, and operation of ML models across research and product environments.
• Improve scheduling, monitoring, debugging, and resource management for GPU-based and cloud infra.
• Enhance observability, automation, reliability, developer experience, and platform usability.
• Create abstractions and tools enabling engineers to work more effectively with ML systems.
• Collaborate with research and product teams to turn challenges into scalable platform capabilities.
• Contribute to architectural decisions, technical strategy, and long-term platform evolution.
• Take ownership of open-ended engineering challenges, balancing scalability, simplicity, and reliability.

🎯 Requirements

• Strong experience building/operating production systems focused on reliability, scalability, performance, and maintainability.
• Systems mindset to reason about bottlenecks, failure scenarios, interfaces, resource use, and long-term needs.
• Hands-on with cloud infrastructure, Linux, and infrastructure automation.
• Experience operating distributed production systems, including Kubernetes-based workloads.
• Strong Python or backend programming skills.
• Experience building internal platforms, tooling, or developer abstractions used by engineers.
• Understanding of ML infrastructure, model serving, or data-intensive workloads.
• Experience with GPU-based systems or large-scale computing resources.
• Familiarity with observability, monitoring, and debugging for distributed systems.
• Knowledge of Terraform, Datadog, GitHub Actions, or similar tools.
• Ability to work in ambiguous environments, take ownership, and solve complex problems independently.
• Pragmatic engineering approach, delivering valuable solutions without unnecessary complexity.

🎁 Benefits

• Fully remote work environment with flexibility across Europe.
• Opportunity to work on advanced AI infrastructure powering large-scale enterprise applications.
• High ownership role with influence over platform architecture and technical direction.
• Collaborative environment with close interaction between engineering, research, and product teams.
• Opportunity to solve complex challenges involving ML systems, automation, and distributed infrastructure.
• Professional growth opportunities within a fast-moving AI-focused organization.