We are an engineering and IT staffing agency that specializes in recruiting clean energy personnel for our clients. Our current client is a fast-growing clean energy technology company revolutionizing how solar and EV infrastructure projects are designed, permitted, and engineered. Through a powerful online platform, they deliver scalable, code-compliant solutions for residential, commercial, and municipal renewable energy systems.
By combining automation, geospatial analysis, and cloud-based collaboration, the platform streamlines complex engineering tasks for solar panel layouts, battery storage planning, and EV charging station deployments. The team is now hiring an experienced Machine Learning Engineer to drive innovation in intelligent system design, permitting intelligence, and real-time design optimization.
Typical Duties and Responsibilities
- Design, build, and deploy machine learning models that automate and optimize the solar and EV infrastructure planning process, including solar array layout, shading analysis, site selection, permit review, and equipment specification.
- Work with large datasets including GIS data, building footprints, satellite imagery, utility interconnect info, and permit histories to develop models for predictive analytics and design recommendations.
- Develop intelligent rule-based systems and NLP pipelines that parse, interpret, and classify jurisdiction-specific permitting requirements across thousands of localities.
- Implement computer vision algorithms for roof segmentation, surface detection, and panel placement using tools like OpenCV, YOLO, or Detectron2.
- Collaborate with front-end, backend, and DevOps teams to deploy models in a scalable, cloud-native infrastructure using Docker, Kubernetes, and CI/CD pipelines.
- Continuously improve model accuracy and inference speed using techniques like transfer learning, hyperparameter tuning, and model quantization.
- Monitor data quality and maintain pipelines for data ingestion, preprocessing, model training, and versioning with tools like MLflow, Airflow, or Kubeflow.
- Partner with product, UX, and engineering teams to convert ML insights into platform features that assist solar designers, engineers, and project managers.
- Stay informed on regulatory and industry standards in clean energy such as NEC, UL 1741, and IEEE 1547.
Education
- Bachelor’s Degree in Computer Science, Machine Learning, Electrical Engineering, Renewable Energy Systems, or a related technical field is required.
- Master’s Degree or Ph.D. in Machine Learning, Artificial Intelligence, or Data Science is strongly preferred, especially with a focus on geospatial or energy applications.
Required Skills and Experience
- 7+ years of experience in building production-ready ML models in a SaaS or engineering-heavy environment.
- Proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, and LightGBM.
- Strong experience with geospatial data (GIS), image segmentation, and satellite imagery analysis.
- Solid understanding of supervised and unsupervised learning, time-series forecasting, and reinforcement learning principles.
- Experience with cloud platforms (AWS, Azure, or GCP) and deploying models in serverless or containerized environments.
- Working knowledge of SQL, NoSQL, and data lake/data warehouse architectures.
- Familiarity with design optimization, energy yield prediction, or load estimation in renewable energy systems is a strong plus.
- Knowledge of NLP, document classification, or text extraction for parsing utility data or permits.
- Experience in Agile development environments and version control systems like Git.
Preferred Qualifications
- Experience in the solar energy, EV infrastructure, or clean tech sector.
- Familiarity with PV design tools (e.g., HelioScope, Aurora Solar, AutoCAD, SketchUp) and solar interconnection rules.
- Publications or research in clean energy modeling, AI-based energy planning, or computer vision for infrastructure.
- Exposure to building code compliance, NEC 2023, or state-level energy incentives and policies (e.g., NEM, SGIP).
- Certifications in AWS Machine Learning Specialty, Google Cloud Professional ML Engineer, or similar.
