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.

About the Role

As a Senior Machine Learning Engineer on the Data Opportunities team at Gridware, you’ll take ownership of critical end-to-end analytics workflows from reliably ingesting large, time-series and spatial datasets to crafting features that drive insight, to building and refining predictive models. You’ll work closely with engineering and product teams to define success criteria, establish robust evaluation frameworks, and develop scalable solutions that can transition from prototype to production. This position offers an opportunity to shape future product development at Gridware by leveraging data science to strengthen grid resilience and mitigate wildfire threats. 

• Collaborate cross-functionally to translate business questions into analytical designs and technical requirements 
• Architect reusable data pipelines and model frameworks that can evolve as new sources and use cases emerge 
• Guide junior colleagues through code reviews, design discussions, and hands-on mentoring to build a high-performing team 
• Implement automated testing, monitoring, and documentation practices to ensure quality and reproducibility 
• Balance exploratory research with delivery of tangible outcomes, iterating quickly on proof-of-concepts and then scaling the best approaches 
• Present results, trade-offs, and recommendations to stakeholders at all levels, helping drive data-informed decisions and roadmaps 

• Master’s or PhD in Data Science, Statistics, Computer Science, Engineering, or related 
• 5+ years in data science with at least 2 years building production ML pipelines 
• Strong Python (pandas, numpy), Spark, SQL, Airflow (or equivalent) 
• Geospatial experience: rasterio,xarray, GDAL, geopandas, Google Earth Engine  
• Familiar with weather/climate data (HRRR, gridMET, RTMA, GFS, etc.) 
• Experience containerizing, CI/CD pipelines, and cloud infrastructure (AWS/GCP/Azure/Databricks) 
• Proven track record mentoring junior engineers or scientists 

• Background in environmental forecasting, time-series modeling, or hazard prediction 
• Experience with dashboarding/monitoring/alerting tools (Grafana) 

This describes the ideal candidate; many of us have picked up this expertise along the way. Even if you meet only part of this list, we encourage you to apply!

Benefits

Health, Dental & Vision (Gold and Platinum with some providers plans fully covered) 

Paid parental leave 

Alternating day off (every other Monday)

“Off the Grid”, a two week per year paid break for all employees. 

Commuter allowance 

Company-paid training