Machine Learning Engineer

Machine Learning Engineer/ Data Architect Location: Oakland, CA (Hybrid) Contract: 12 months About the Company A large, California-based energy and utilities organization focused on leveraging advanced analytics and machine learning to support critical infrastructure and wildfire risk mitigation initiatives. Role Overview Seeking a Data Architect / Machine Learning Engineer to bridge research and production by designing and implementing scalable ML and data solutions. This role combines hands-on engineering with architecture and technical leadership, supporting wildfire consequence modeling efforts. Key Responsibilities • Design and implement scalable ML and data architectures in cloud environments (AWS) • Build and deploy production-grade ML pipelines and data systems • Translate research models into maintainable, real-world applications • Develop APIs and system integrations for ML solutions • Partner with researchers, product teams, and leadership to deliver solutions • Communicate technical concepts effectively to cross-functional stakeholders Must-Haves • Experience developing and deploying machine learning systems in production • Strong Python and PySpark skills • Background in data architecture and system design • Experience with AWS (S3, SageMaker, Lambda, Glue) • Strong analytical, problem-solving, and communication skills Nice-to-Have • Experience with Snowflake and/or Palantir Foundry • Geospatial analytics tools (e.g., geopandas, rasterio, GDAL, rioxarray, dask) • Background in wildfire, environmental, or risk modeling • Experience supporting research-to-production ML workflows Ideal Candidate Profile • Hybrid architect + hands-on engineer • Experience building scalable, cloud-native ML platforms • Strong ability to translate research into production systems • Comfortable advising leadership and collaborating across technical and non-technical teams