ML Infrastructure Engineer

**About Gridware** Gridware is a San Francisco-based technology company dedicated to protecting and enhancing the electrical grid. We pioneered a groundbreaking new class of grid management called active grid response (AGR), focused on monitoring the electrical, physical, and environmental aspects of the grid that affect reliability and safety. Gridware’s advanced Active Grid Response platform uses high-precision sensors to detect potential issues early, enabling proactive maintenance and fault mitigation. This comprehensive approach helps improve safety, reduce outages, and ensure the grid operates efficiently. The company is backed by climate-tech and Silicon Valley investors. For more information, please visit [](http://www.gridware.io/). **Role Description** As a Senior ML Infrastructure Engineer, you will work directly in the Automation org with the core ML, Ops, and Analytics teams to help improve and build out the infrastructure around model deployment and monitoring. This role is essential to helping scale out the amount of time saving’s Gridware brings to customers. ### 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)