Machine Learning Engineer
Camus Energy · Remote · $180k–$230k
The role
Camus Energy seeks a Machine Learning Engineer to lead forecasting and predictive modeling for its grid connection platform. The role covers the full ML lifecycle, from exploratory analysis to production deployment, and involves working with internal teams and external stakeholders. The position is fully remote with options for Bay Area office work.
- Pay
- $180k–$230k
- Location
- Remote
- Work mode
- Remote
- Employment
- Full time
- Level
- Senior
- Experience
- 3+ yrs
- Education
- Bachelors
What we know that the posting doesn’t say
- Seen todaystill listed on the employer’s careers page
About Camus Energy
Camus Energy builds software to help new load and generation connect to the grid faster while maintaining reliability, bridging grid operators and large load developers.
What you would do
- Design, train, and evaluate predictive ML models for forecasting and time-series
- Conduct exploratory data analysis, feature engineering, and statistical modeling
- Collaborate with Engineering to define ML infrastructure and deploy models
- Work cross-functionally to define problem statements and translate business objectives
- Communicate model performance, uncertainty, and limitations to diverse audiences
- Champion ML best practices for reproducibility, versioning, and testing
Must have
- PhD with 3+ years, Masters with 5+, or Bachelors with 8+ years in relevant field
- Proven track record of delivering ML models to production
- Experience with time-series forecasting methods including classical and ML-based
- Strong proficiency in Python and core ML libraries like PyTorch, scikit-learn, pandas
- Experience with probabilistic forecasting, uncertainty quantification, and backtesting
- Ability to translate ambiguous business problems into well-scoped ML projects
- Comfortable working autonomously in a small team with production-grade discipline
Nice to have
- Experience in energy sector: load forecasting, renewable generation, price modeling, grid ops
- Experience with MLOps tooling: cloud platforms, containerization, model serving
- Experience with data pipeline tooling like Airflow, Spark, or Databricks
- Ability to leverage AI code development tools to accelerate development
What you get
- Competitive base salary
- Comprehensive benefits including FSA and 401k for full-time employees
- Fully remote workplace with optional Bay Area office
- Flexible PTO
- Real impact on climate change
Experience and education
- 3+ years of relevant experience
- Bachelors degree or equivalent experience
Key skills
- python
- pytorch
- scikit-learn
- statsmodels
- pandas
- time-series forecasting
- arima
- gradient boosting
- temporal neural networks
- probabilistic forecasting
- uncertainty quantification
- backtesting
- mlops
- cloud platforms
- containerization
- model serving
- airflow
- spark
- databricks
- ai code development tools
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…
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