Overview

We are seeking a Machine Learning Engineer with strong forecasting skills and hands-on experience maintaining cloud-based applications.

Responsibilities

• Develop machine-learning models to forecast electricity generation and market participants bidding patterns using historical electricity-market data, weather forecasts, and operating data from individual energy assets.
• Develop and evaluate machine-learning models for time-series forecasting, using tools such as CatBoost, XGBoost, and LightGBM.
• Conduct reproducible, out-of-sample backtests and report forecast performance.
• Integrate forecasting models and data pipelines into the existing SaaS application.
• Maintain databases and data workflows using Supabase.
• Build and maintain Docker-based development and deployment workflows.
• Manage deployments and troubleshoot application issues on Render.
• Monitor application and pipeline performance, investigate failures, and improve reliability.
• Maintain documented code, deployment procedures, and operating workflows.

Qualifications

• Bachelor's or Master's degree in Computer Science or a related field.
• Minimum of three years of relevant professional experience in machine-learning development and software engineering.
• Demonstrated experience developing, backtesting, and deploying time-series forecasting models.
• Strong Python skills in data processing, feature engineering, and machine learning.
• Strong SQL skills and hands-on experience with database design, queries, and maintenance.
• Practical experience with Supabase, Render, and Docker or similar tools.
• Experience maintaining production applications, troubleshooting deployments, and managing cloud environments.

Nice to have

• Experience with electricity markets, energy data, or renewable generation forecasting.
• Experience forecasting participant bidding behavior, order-book dynamics, auction bids, or price-quantity offer patterns from historical data.
• Experience maintaining SaaS applications and supporting production users.
• Takes ownership of assigned work and follows through on commitments.
• Works effectively in a fast-paced startup environment with clear daily deliverables.
• Brings an entrepreneurial mindset and a practical approach to problem-solving.
• Is committed to high-quality work and reliable application operation.
• One forecasting model you personally developed and how you backtested it.
• One production application or ML pipeline you deployed or maintained.
• Your hands-on experience with Supabase, Render, and Docker or similar tools.