Machine Learning Software Engineer

Camus Energy builds software solutions that help new load and generation connect to the grid faster—without sacrificing reliability. They are seeking a Machine Learning Engineer to advance the forecasting and predictive modeling capabilities at the heart of their platform, responsible for the full lifecycle of ML model development.

Responsibilities:

• 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
• Experience in the energy sector — e.g. load forecasting, renewable generation prediction, price modeling or grid operations
• Experience with MLOps tooling and infrastructure: cloud platforms, containerization, and model serving patterns
• Experience with data pipeline tooling, e.g. Airflow, Spark, or Databricks
• Able to leverage AI code development tools to accelerate development