Senior ML Ops Engineer

📋 Description

• Build and maintain ML infra end-to-end: CI/CD, orchestration, automated training.
• Own model deployment and serving with low latency and high availability.
• Develop core MLOps capabilities: feature stores, model registries, monitoring.
• Operationalize infra for ML: Kubernetes autoscaling, GPU provisioning, self-service tools.
• Improve reliability and observability; define SLOs and automate uptime.
• Empower Data Scientists with standardized workflows to speed ML lifecycle.

🎯 Requirements

• Experience building and operating ML platforms in production.
• Docker, Kubernetes, Linux, and model serving at scale.
• ML lifecycle tooling: feature stores, registries, drift monitoring.
• Own prod systems: SLOs, observability (Prometheus, Grafana, Datadog).
• Production-quality Python or similar language.
• Modernize infra with reliability, risk, and cost focus.
• Own outcomes; communicate clearly with data.

🎁 Benefits

• Work from almost anywhere up to 20 days/year
• Mental health support: therapy and HeadSpace
• No meeting Fridays
• 6 weeks paid vacation + a day off for your birthday
• Paid parental leave
• Paid volunteer time