Senior Machine Learning Engineer

Own the journey from ML experimentation to production

• Turn forecasting and other ML models into reliable, scalable, reproducible systems that directly support trading, pricing, and customer insights.
• Shape ML engineering standards and architecture - Drive technical decisions, establish teamwide engineering standards, and mentor engineers in a highly technical, production-first environment.
• Build ML for a complex, real-world energy environment - Develop robust pipelines, backtesting frameworks, and monitoring capabilities that handle dynamic markets and incomplete or delayed data while supporting Eneco’s energy-transition ambitions.

Why choose Eneco?

At Eneco, we are accelerating the energy transition through our One Planet Plan, with the ambition to become climate neutral by 2035. Energy markets are becoming increasingly dynamic and complex due to renewable generation, electrification, and shifting market behavior. To operate effectively in this environment, efficient and accurate demand forecasts are essential, alongside data-driven insights on customer behavior to enable customer-fit offerings and prices.

As a Senior ML Engineer, you play a key role in building and maintaining data and ML pipelines. You will work at the intersection of data science and data engineering, helping transform ML models into reliable, scalable, and production-ready systems. These systems will drive trading decisions in the energy market, provide crucial insight for improving pricing and offerings. These insights can also identify potential improvements to business processes and guide future research. You’ll join a highly technical environment where engineering quality, ownership, and operational reliability are critical to business success.

What you’ll do

You will work closely with data scientists to experiment with and operationalize ML models for various uses such as demand forecasting, asset detection, and market simulations. Together with data engineers, you will build the foundations that enable reliable deployment of these models and monitoring in production environments.

Your focus is turning research into robust production systems while ensuring reproducibility, validation, and observability across the ML lifecycle.

Is this about you?

Must have

• Strong software engineering skills in Python.
• Proven experience productionizing, maintaining, and monitoring ML models and pipelines.
• Hands-on experience with distributed data and compute platforms.