Senior Machine Learning Engineer

Details

The Senior Machine Learning Engineer will work at the intersection of data science and data engineering to transform machine learning models into reliable, scalable, reproducible, and production-ready systems. These systems support energy-market trading decisions, pricing, customer insights, demand forecasting, asset detection, and market simulations.

Responsibilities

ML Model Experimentation

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

Data and Feature Engineering

• Build and maintain data and feature pipelines used by machine learning models.
• Collaborate with data engineers to optimize the structure and performance of underlying data models.
• Write technical documentation and encode explicit dependencies to enable clear data lineage and governance.
• Safely handle incomplete, delayed, or partial data in experimentation and production environments.

Productionization and Platform Integration

• Support the deployment, versioning, rollback, and release management of machine learning models in 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, and 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.
• Establish teamwide engineering standards, drive architectural decisions, and mentor engineers.

Requirements

• Strong software engineering skills in Python.
• Proven experience productionizing, maintaining, and monitoring machine learning 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 in time-series or event-driven systems.
• A strong ownership mentality and production-first engineering mindset.
• Experience setting teamwide engineering standards and driving architectural decisions.
• Experience mentoring engineers and providing constructive reviews 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.

Work Environment

The role is part of a team focused on accurate and efficient demand forecasting, machine learning experimentation, and insights into customer behavior and trends. The team combines expertise in data science, data engineering, data analytics, and machine learning engineering.

Benefits

• Gross annual salary between €88,000 and €131,000, including FlexBudget, 8% holiday allowance, and, depending on the role, a bonus or collective profit sharing.
• FlexBudget that can be paid out, used to purchase additional holiday days, or saved.
• Personal and professional development opportunities.
• Hybrid working: 40% in the office, 40% from home, and 20% flexibly.
• With manager approval, the role may be performed abroad within approved countries for up to 3 weeks per year, with a maximum of 2 consecutive weeks.
• Flexible working hours and the option to work from home when the role allows it.