Senior Machine Learning Engineer

As a Senior Machine Learning Engineer, you play a key role in building and maintaining data and ML pipelines.

• Eneco - Rotterdam
• > 5 jaar
• Data & AI
• Hybrid working: home, office or abroad
• Work 40% at the office, 40% from home, and 20% flexibly.
• With manager approval, you may work abroad (within approved countries) up to 3 weeks/year, max 2 consecutively.

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.

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.

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 such as Databricks or similar technologies.
• Experience deploying workloads into containerized or service-based execution environments.
• Experience designing or maintaining backtesting and simulation frameworks.
• Strong understanding of reproducibility, validation, and reliability within time-series or event-driven systems.
• Strong ownership mentality with a production-first engineering mindset.
• Experience setting teamwide engineering standards and driving architectural decisions.
• Experience mentoring engineers and providing constructive review of technical designs and code.

Nice to have

• Experience with demand forecasting, energy markets, and asset detection.
• Experience with cloud-native architectures and scalable distributed systems.
• Experience collaborating closely with data scientists and data engineers.
• Experience with dbt, Snowflake, and Java

You’ll be responsible for

ML Model Experimentation

• Work with and enable data scientists to run experiments with ML models.
• Design, build, and maintain backtesting and experimentation frameworks.
• Ensure reproducibility across research, experimentation, and live execution environments.

Data & Feature Engineering

• Build and maintain data & feature pipelines used by ML models.
• Collaborate closely with data engineers on optimizing the structure and performance of underlying data models.
• Write technical documentation and encode explicit dependencies to enable clear data lineage & governance.
• Handle incomplete, delayed, or partial data safely in both experimentation and production environments.

Productionization & Platform Integration

• Support deployment, versioning, rollback, and release management of ML models into production-grade pipelines.
• Optimize runtime performance and resource utilization where relevant.
• Implement validation, safeguards, and operational controls before production deployment.
• Ensure stable and deterministic execution alongside other engineers.

Monitoring, Reliability & Risk Awareness

• Implement monitoring and observability for forecasting models.
• Support drift detection, anomaly monitoring, and performance degradation analysis.
• Resolve operational data incidents and conduct post-mortems.
• Ensure deterministic reruns and explainability of historical models.

This is where you’ll work

You’ll become part of a team focused on enabling accurate and efficient demand forecasting, ML experimentation, and key insights on customer behavior and trends.

The team combines expertise across data science, data engineering, data analytics, and ML engineering.

What we have to offer

Gross annual salary between €88.000 and €131.000

Including FlexBudget, 8% holiday allowance, and depending on your role a bonus or collective profit sharing.

FlexBudget

Including FlexBudget, 8% holiday allowance, and depending on your role a bonus or collective profit sharing. Have it paid out, use it to buy extra holiday days or save it up for something nice, it's up to you.

Personal and professional growth

Eneco is fully committed to help you in your personal and professional development.

Hybrid working: home, office or abroad

Work 40% at the office, 40% from home, and 20% flexibly. With manager approval, you may work abroad (within approved countries) up to 3 weeks/year, max 2 consecutively.