Senior ML Ops Engineer

📋 Description

• Build and maintain ML infra end-to-end (CI/CD, orchestration, training pipelines).
• Own model deployment and serving with low latency and high availability.
• Develop core MLOps tools: feature stores, model registries, monitoring.
• Operationalize infra for ML: Kubernetes autoscaling and GPU provisioning.
• Improve platform reliability: observability and automated remediation.
• Empower Data Scientists with standardized golden workflows.

🎯 Requirements

• Experience building and operating ML platforms in production.
• Strong knowledge of Docker, Kubernetes, Linux internals, and model serving at scale.
• Familiarity with ML lifecycle tooling: feature stores, registries, drift monitoring.
• Experience owning production systems: SLOs, observability (Prometheus, Grafana, Datadog), incidents.
• Comfort writing production-quality Python or similar language.
• Ownership mindset; data-driven decisions; clear communication.

🎁 Benefits

• Work from almost anywhere up to 20 days/year
• SpringHealth therapy and HeadSpace subscription
• Company-wide week off; No meeting Fridays
• Paid parental leave; Paid volunteer time
• Development dollars and leadership development
• Office in Friedrichshain, Berlin; travel discounts; transit subsidies