Remote ML Engineer — Grid Forecasting & Planning

Camus Energy Inc.; Campbell (CA); On-site; USD 180,000 - 230,000; Full time

Benefits offered by this job

Fully remote

Bay Area office option

Flexible PTO

Climate impact through work

Job summary

Camus Energy Inc. is seeking a Machine Learning Engineer to own forecasting and predictive modeling for the platform. You will handle the full ML lifecycle, from data exploration and model design to production deployment and monitoring.

You'll work with Camus teams and stakeholders to understand data, define questions, and translate signals into reliable analytics that inform product decisions and engineering integration.

Qualifications

• PhD with 3+ years (or Masters 5+, Bachelors 8+) in ML or related field.
• Proven track record of delivering ML models to production.
• Experience with time-series forecasting methods (ARIMA, ML-based).
• Strong Python and ML libraries (PyTorch, scikit-learn, pandas).
• Experience with probabilistic forecasting and backtesting.
• Ability to translate business problems into scoped ML projects.
• Autonomous contributor in a small team balancing speed and discipline.

Responsibilities

• Design, train, and evaluate predictive ML models with focus on forecasting and time-series.
• Conduct exploratory data analysis, feature engineering, and statistical modeling.
• Collaborate with Engineering to define ML infra requirements and deploy models.
• Work cross-functionally to translate business objectives into ML solutions.
• Communicate model performance, uncertainty, and limitations clearly.
• Champion ML best practices around reproducibility, versioning, and testing.

Education

PhD or higher

Masters

Bachelors

Tools

Airflow

Spark

Databricks

Cloud platforms

Containerization

Model serving patterns

Job description

Camus Energy Inc. is seeking a Machine Learning Engineer to own forecasting and predictive modeling for the platform. You will handle the full ML lifecycle, from data exploration and model design to production deployment and monitoring.

You'll work with Camus teams and stakeholders to understand data, define questions, and translate signals into reliable analytics that inform product decisions and engineering integration.