about 4 hours ago
Berlin, GermanySenior
Responsibilities
- Build and maintain end-to-end ML infrastructure including CI/CD pipelines and automated training pipelines.
- Own model deployment and serving, ensuring low latency and high availability.
- Develop core MLOps capabilities like feature stores and model registries.
- Collaborate with Operations to enable Kubernetes autoscaling and GPU provisioning.
- Design resilient monitoring systems and define service-level objectives.
- Empower Data Scientists by creating standardized workflows for model development.
Requirements
- Experience building and operating ML platforms in production environments.
- Solid knowledge of containerization and orchestration (Docker, Kubernetes).
- Familiarity with ML lifecycle tooling and performance monitoring.
- Experience owning production systems and defining service-level objectives.
- Comfort writing production-quality code in Python or a comparable language.
- Ability to take ownership of technical outcomes and communicate effectively.
Benefits
- Work from (almost) anywhere for up to 20 days per year.
- Company-paid therapy sessions and subscription to HeadSpace.
- Company-wide week off a year for team recharge.
- Paid parental leave and volunteer time.
- Development Dollars for career growth and access to e-learnings.
- 6 weeks paid vacation plus a day off for your birthday.
- Free lunch 2 days per week and monthly social events.