Machine Learning Software Engineer at Camus Energy

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Full Time

Start Date

Immediate

Expiry Date

07 Aug, 26

Salary

230000.0

Posted On

09 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Time-Series Forecasting, Python, PyTorch, Scikit-learn, Statsmodels, Pandas, Probabilistic Forecasting, Uncertainty Quantification, Feature Engineering, Statistical Modeling, MLOps, Data Pipeline Tooling, Model Deployment, Exploratory Data Analysis, Backtesting

Industry

Software Development

Description

About Us Camus Energy builds software solutions that help new load and generation connect to the grid faster—without sacrificing reliability. As electricity demand accelerates and clean energy scales, traditional interconnection processes are becoming the bottleneck. Utilities and developers face growing queues, long timelines, and costly upgrades that slow progress across the grid. Camus enables flexible grid connections that allow new load and generation to connect sooner by planning for and operating within real system constraints. Our platform bridges the gap between grid operators and large load developers, providing a view of time-varying grid capacity for any given new interconnection point. We combine high-reliability software experience from companies like Google and Meta with deep power systems expertise across the utility sector. If you’re excited to work at the intersection of infrastructure, software, and climate, we’d love to hear from you. 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. What You'll Do 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 What You'll Bring 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 What We Offer Competitive base salary 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! A real impact on climate change - we’re building the world we want to live in and we want you to join us! The expected base salary for this role is $180,000 - $230,000 annually, depending on experience, skills, and qualifications.

Responsibilities

Own the full lifecycle of ML model development, focusing on forecasting and predictive modeling for grid capacity. Collaborate cross-functionally to translate business objectives into production-grade data analytics and operational workflows.