Machine Learning Engineer

About the role

Camus Energy develops software to accelerate grid connections for new power generation and load sources. You will lead the development of predictive models and forecasting tools that help grid operators manage capacity and reliability.

Key facts

What you'll do

• Build, train, and assess predictive models specifically for time-series forecasting.
• Perform statistical modeling, feature engineering, and data analysis on diverse datasets.
• Partner with the engineering team to deploy models into production and integrate them into operational tools.
• Translate business goals into actionable machine learning projects.
• Explain model performance, limitations, and uncertainty to technical and non-technical stakeholders.
• Maintain high standards for testing, versioning, and reproducibility.

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.
• Proven history of moving machine learning models into production environments.
• Proficiency in time-series forecasting, including classical methods like ARIMA and modern techniques such as temporal neural networks or gradient boosting.
• Strong coding skills in Python and standard data science libraries including PyTorch, scikit-learn, statsmodels, and pandas.
• Experience with backtesting, uncertainty quantification, and probabilistic forecasting.
• Ability to define project scopes from ambiguous business requirements.
• Capability to work autonomously in a small team environment.

Nice to have

• Background in the energy industry, specifically regarding grid operations, renewable generation prediction, price modeling, or load forecasting.
• Familiarity with MLOps infrastructure, including containerization, cloud platforms, and model serving.
• Experience with data pipeline tools like Spark, Airflow, or Databricks.
• Proficiency with AI-assisted coding tools.

Skills & tools

• Python
• PyTorch
• scikit-learn
• statsmodels
• pandas
• Time-series forecasting
• Probabilistic modeling

Practical notes

• Benefits include 401k and FSA.
• The company offers flexible PTO.
• This role is fully remote with the option to work from the Bay Area office.