Remote ML Engineer: Time-Series Forecasting for Clean Energy

Latitude; Campbell (CA); Hybrid; USD 180,000 - 230,000; Full time

Benefits offered by this job

Fully remote

Bay Area in-office option

Flexible PTO

Competitive salary and benefits

Job summary

Camus Energy is seeking a Machine Learning Engineer to own forecasting and predictive modeling for the Camus platform. This is an individual contributor role with deep technical depth and product influence; you'll manage the full lifecycle of ML model development from exploration to deployment.

You'll work with Camus’ teams to understand data, define the right questions, and translate signals into reliable analytics.

Qualifications

• PhD with 3+ years of industry experience in ML, Statistics, CS or related field.
• Masters with 5+ years, or Bachelor with 8+ years in quantitative area.
• Strong proficiency in Python and ML libraries; production experience required.

Responsibilities

• Design, train, and evaluate predictive ML models for forecasting and time-series.
• Perform exploratory data analysis and feature engineering on large datasets.
• Collaborate with Engineering to define ML infrastructure and deploy models into workflows.
• Translate business objectives into ML solutions and communicate results to diverse audiences.
• Ensure reproducibility, versioning, and testing of ML models.

Skills

ML forecasting

Python

Production deployment

Data analysis

Communication

Education

PhD

Masters

Bachelor

Tools

PyTorch

scikit-learn

pandas

statsmodels

Job description

Camus Energy is seeking a Machine Learning Engineer to own forecasting and predictive modeling for the Camus platform. This is an individual contributor role with deep technical depth and product influence; you'll manage the full lifecycle of ML model development from exploration to deployment.

You'll work with Camus’ teams to understand data, define the right questions, and translate signals into reliable analytics.