Member of Technical Staff - ML Infrastructure Engineer
Freiburg (Germany), San Francisco (USA)
R
USD 180K-300K Senior-level Full Time
Tasks
- Design and deploy ML inference clusters
- Design and deploy ML training clusters
- Ensure cloud security best practices
- Implement custom autoscaling for ML workloads
- Implement infrastructure as code for resource provisioning
- Maintain Kubernetes clusters
- Maintain Slurm clusters
- Manage network cloud file systems
- Manage object storage for ML workloads
- Monitor and optimize observability for ML infrastructure
- Optimize CI/CD pipelines for ML workflows
- Provide developer friendly ML operations tooling
Perks/Benefits
Skills/Tech-stack
AWS | Amazon S3 | Ansible | Argo CD | Autoscaling | Azure | Best practices | CI/CD | CircleCI | Cloud platform | Container Security | Data Versioning | Distributed Training | Experiment tracking | File System | GPU infrastructure | GPU infrastructure management | GitHub Actions | Google Cloud | Google Cloud Platform | Grafana | High Performance | High-Performance Computing | Infrastructure Management | Infrastructure as Code | Kubernetes | Loki | MLOps | Monitoring | Network File System | Object storage | Observability | Performance Computing | Prometheus | Security best practices | Slurm | Storage Optimization | Terraform | Vulnerability scanning | “as-code”
Education
N/A
Regions
Countries
States
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