Camus Energy builds software solutions that help new load and generation connect to the grid faster—without sacrificing reliability.
Key Responsibilities
We're looking for a Machine Learning Engineer to own and advance the forecasting and predictive modeling capabilities at the heart of the Camus platform. This is an individual contributor role with real technical depth and product influence; you'll be responsible for the full lifecycle of ML model development, from exploratory analysis and model design through to production deployment and monitoring.
This is not a role where the problem statements are handed to you. You'll work directly with Camus’ teams and external stakeholders to understand their data, define the right questions, and translate messy real-world signals into reliable, production-grade data driven analytics. You'll bring that ground-truth perspective back into product decisions, and work closely within the Engineering team to integrate ML models into our planning and operational workflows.
The forecasting and predictive modeling problems we're solving often don't have off-the-shelf answers. We work as a tight, technical team that moves with urgency but builds with the discipline that production-grade software demands. If you want to do the most technically interesting ML work in the clean energy space while directly shaping how it becomes a product, this is the role.
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
Benefits & Perks
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!
The expected base salary for this role is $180,000 - $230,000 annually, depending on experience, skills, and qualifications.
Ready to Apply?
Join Camuse Energy and make an impact in renewable energy