ML Forecasting Engineer – Remote Climate Impact

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

Benefits offered by this job

Fully remote work

Optional Bay Area in-office

FSA and 401k benefits

Job summary

Camus Energy is seeking a Machine Learning Engineer to own forecasting and predictive modeling within the Camus platform. You will handle the full lifecycle from exploratory analysis to production deployment and monitoring.

You will collaborate with engineering and stakeholders to turn data into reliable analytics and integrate ML models into planning and operations workflows, with an emphasis on production-grade software and reproducibility.

Qualifications

• PhD with 3+ years, Masters with 5+, or Bachelors with 8+ years in ML, statistics, computer science, applied mathematics or related field.
• Experience delivering ML models into production environments.
• Proficiency in Python and core ML libraries (PyTorch, scikit-learn, pandas).
• Experience with time-series forecasting methods including ARIMA and modern ML approaches.

Responsibilities

• Design, train, and evaluate predictive ML models focusing on forecasting and time-series applications.
• Conduct exploratory data analysis, feature engineering, and statistical modeling on large datasets.
• Collaborate with Engineering to define ML infrastructure needs and deploy models into workflows.

Skills

Python

PyTorch

scikit-learn

pandas

time-series forecasting

Education

PhD (ML/CS/Math)

Masters (ML/CS/Math)

Bachelors (ML/CS/Math)

Tools

Airflow

Spark

Databricks

Job description

Camus Energy is seeking a Machine Learning Engineer to own forecasting and predictive modeling within the Camus platform. You will handle the full lifecycle from exploratory analysis to production deployment and monitoring.

You will collaborate with engineering and stakeholders to turn data into reliable analytics and integrate ML models into planning and operations workflows, with an emphasis on production-grade software and reproducibility.