Machine Learning Software Engineer

🕒 May 15

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Camus Energy

WebsiteLinkedInAll Job Openings

11 - 50 employees

⚡ Energy

🤖 Artificial Intelligence

☁️ SaaS

Energy • Artificial Intelligence • SaaS

Camus Energy is a technology company specializing in AI-powered tools for managing real-time distribution grid constraints and integrating distributed energy resources (DER) flexibility into system planning. The company offers utilities enhanced visibility into grid operations, incorporating machine learning to anticipate local grid constraints and optimize grid capacity. Camus Energy's platform supports electric vehicle (EV) detection, short-term forecasting, and DER control, helping utilities improve reliability, affordability, and operational efficiency while facilitating the transition to a zero-carbon grid. Their solutions are designed to serve investor-owned utilities, co-ops, municipal utilities, and generation and transmission providers, ensuring seamless grid management and planning with a focus on the energy transition.

📋 Description

• Design, train, and evaluate predictive ML models with a focus on forecasting and time-series applications • Conduct exploratory data analysis, feature engineering, and statistical modeling across large structured and unstructured datasets • Collaborate with Engineering to define ML infrastructure requirements, and deploy and integrate ML models into operational workflows and decision-support tools • Work cross-functionally with Camus teams to define problem statements and translate business objectives into ML solutions • Communicate model performance, uncertainty, and limitations clearly to both technical and non-technical audiences • Champion ML best practices around reproducibility, versioning, and testing

🎯 Requirements

• PhD with 3+ years of industry experience, Masters with 5+ years, or Bachelors with 8+ years in Machine Learning, Statistics, Computer Science, Applied Mathematics, or a related quantitative field • Demonstrated track record of delivering ML models into production environments • Experience with time-series forecasting methods — including classical approaches (e.g. ARIMA) and modern ML-based methods (e.g. gradient boosting or temporal neural networks) • Strong proficiency in Python and core ML/data science libraries (PyTorch, scikit-learn, statsmodels, pandas, etc.) • Experience with probabilistic forecasting, uncertainty quantification and backtesting • Ability to translate ambiguous business problems into well-scoped ML projects • Comfortable operating with autonomy in a small team, balancing speed of delivery with the engineering discipline that production-grade software demands.

🏖️ Benefits

• Competitive base salary • Comprehensive benefits, including FSA and 401k for full time employees • Fully remote workplace with options for in office work in the Bay Area • Flexible PTO, which we encourage you to use! • A real impact on climate change - we’re building the world we want to live in and we want you to join us!