Manager, Machine Learning Engineering
Tasks
- Build and mature on call processes SLOs SLAs and postmortems
- Collaborate with product engineering design and legal to translate goals into operational outcomes
- Develop deploy and maintain scalable robust machine learning systems with tools and platforms
- Drive operational strategy for generative AI systems balancing safety compliance cost and reliability
- Drive technical direction via design reviews architecture decisions and best practices
- Establish ML operational excellence via model observability standards automated retraining and incident response
- Implement CI CD for ML including model packaging versioning rollback
- Lead hire and grow ML AI operations team
- Manage vendor and platform relationships and make build vs buy decisions
- Own reliability scalability operational health of ML AI production systems
- Report team health system reliability metrics and operational risk to leadership
- Streamline model development to production deployment
Perks/Benefits
- 401k matching
- Dental benefits
- Employee recognition programs
- Flexible time off
- Healthcare benefits
- Hybrid work financial assistance
- Learning and development programs
- Life insurance
- Mental health and wellness resources
- Parental leave
- Vision benefits
- Volunteering program
Skills/Tech-stack
APIs | AWS | Apache Spark | CI/CD | Code review | Data Drift | Data Drift Detection | Databricks | Docker | Drift Detection | Error Budgeting | Feature Store | GitHub | Incident Response | Kubernetes | Latency monitoring | Model Monitoring | Model Serving | Model calibration | On-Call | PyTorch | Python | SLA | SLO | SQL | Scikit-learn | Snowflake | TensorFlow | Terraform | Testing
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Regions
Countries
States
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