Overview
About Gridware
Full job description
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 www.Gridware.io.
Role Description As a Senior ML Manager, you will serve as a player coach role for Gridware’s cloud-based modeling team. You will help grow the existing team, improve algorithmic development frameworks, focus on strategic initiatives and direction for how we approach cloud sensor modeling, and establish best practices for a high throughput ML team.
70% team management and strategy, 30% IC time
Grow a high-performing team through hiring, coaching, performance management, and career development
Partner with software engineering and ML infrastructure teams to ship robust, production-grade ML systems
Own technical roadmap and execution for the cloud ML organization, balancing near-term product delivery with long-term technical investments
Lead technical design of algorithms that improve the speed, accuracy, and reliability of Gridware’s automated hazard detection systems
Define data strategy and labeling requirements, including real-world data collection and synthetic data generation approaches
Explore and evaluate novel modeling approaches and research ideas to address existing and emerging automation challenges
3+ years of direct management experience
8+ years of experience building and deploying machine learning models in production environments
Experience with both deep learning and classical machine/statistical learning techniques
Strong programming skills with demonstrated proficiency in Python
Experience working within modern software stacks, including cloud platforms, containerization, and CI/CD workflows
Experience working with signal processing or sensor-derived data
Experience at a growth-stage start-up
Familiarity with human-in-the-loop or human-on-the-loop ML systems
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