ML Infrastructure Engineer
Team: Automation
Location: San Francisco, CA
Commitment: Full-Time
Workplace Type: hybrid
Salary:
Responsibilities
- Design, build, and maintain the infrastructure, tooling, and workflows that enable reliable, scalable deployment of ML models to production.
- Develop monitoring and observability systems to track model performance, data drift, data quality, and overall system health.
- Create and maintain end-to-end testing frameworks and simulation environments to validate models and pipelines prior to deployment.
- Work closely with Data Engineering and Platform Engineering teams to ensure ML systems integrate cleanly with broader Gridware infrastructure and operational standards.
- Improve CI/CD pipelines for ML workloads, ensuring reproducibility, safe rollout, and automated rollback strategies.
Required Skills
- 5+ years of experience building production ML infrastructure
- Strong software engineering skills and proficiency in Python
- Experience with cloud platforms (AWS) and container orchestration (Kubernetes)
- Familiarity with feature stores, model registries, or centralized metadata systems (i.e. MLFlow)
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