Machine Learning Engineer

The Role

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.

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

• Education & Experience: 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

Nice to Have

• 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

• Competitive base salary: $180,000 - $230,000 annually, depending on experience, skills, and qualifications
• 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
• Real impact on climate change — we're building the world we want to live in and we want you to join us