Staff Software Engineer, Machine Learning (Health)
Tasks
- Deploy validate monitor and operate ML powered services at scale
- Design build and maintain production services for health insights
- Develop and optimize APIs and backend services for reliability latency cost
- Drive monitoring observability incident response and reliability improvements
- Improve ML data pipelines tooling and validation frameworks
- Lead architecture and development of scalable ML inference systems
- Participate in on call rotations for ML and data services
- Provide technical leadership through architecture reviews standards and mentorship
- Support model evaluation experimentation and performance validation
- Translate research prototypes into production systems
Perks/Benefits
- N/A
Skills/Tech-stack
APIs | AWS | Batch Processing | CI/CD | Cloud platform | Data Pipelines | Data Processing | Debugging | Distributed Systems | Event Driven | Event-driven architecture | Experimentation | Google Cloud | Google Cloud Platform | High Throughput | High-throughput data | High-throughput data processing | Infrastructure automation | Kubernetes | ML inference | Machine Learning | Model Deployment | Model Evaluation | Model Validation | Monitoring | Observability | Performance optimization | Python | Real Time | Real-time Streaming | Reliability Engineering | Series data | Time Series | Time Series Data
Education
Regions
Countries
States
Cities
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