Machine Learning Engineer (Energy Management)

Location: Rotterdam, Netherlands. Hybrid working: 40% at the office, 40% from home, and 20% flexibly; with manager approval, work abroad within approved countries for up to 3 weeks per year, with a maximum of 2 consecutive weeks.

Gross annual salary: €84,000–€117,000, including FlexBudget and an 8% holiday allowance; a bonus or collective profit sharing may apply depending on the role.

Company

hirify.global develops digital products and services that support the energy transition and its goal of becoming climate neutral by 2035.

What you will do

• Design, build, and maintain end-to-end machine learning pipelines for training, validation, and batch or real-time production inference.
• Develop scalable MLOps capabilities, including CI/CD, automated testing, model versioning, deployment strategies, drift detection, and rollback mechanisms.
• Build and operate Databricks-based data processing and machine learning workflows at scale, including MLflow experiment tracking and model registry.
• Deploy, operate, and optimize machine learning workloads on AWS while balancing performance, scalability, and cost.
• Develop feature engineering pipelines, data contracts, and feature stores, including Scala-based ETL where applicable.
• Build monitoring, alerting, and observability for model performance, latency, throughput, and data or model drift; collaborate with Data Scientists and mentor engineers.

Requirements

• 4+ years of professional experience in ML engineering, including 2+ years owning MLOps or production ML systems.
• Hands-on Databricks experience with notebooks, jobs, clusters, large-scale data processing, and model training.
• Production experience with MLflow for experiment tracking and model registry.
• Strong AWS experience deploying and operating machine learning workloads.
• Proficiency in Scala for data processing and ETL on Spark, with production-quality code and familiarity with the JVM ecosystem.
• Experience building CI/CD for ML and containerized deployments using Docker and Kubernetes.
• Practical knowledge of model monitoring, A/B testing, canary deployments, and data or model drift detection.
• Strong software engineering fundamentals, including testing, design patterns, code reviews, and documentation.
• Excellent communication and collaboration skills.

Culture & Benefits

• Multidisciplinary collaboration with Machine Learning Engineers, Data Scientists, Data Engineers, and Data Analysts.
• Culture focused on collaboration, ownership, experimentation, knowledge sharing, continuous learning, and continuous improvement.
• FlexBudget can be paid out, used for additional holiday days, or saved.
• hirify.global supports personal and professional development.
• Hybrid work across home, office, and approved international locations under the stated limits.

Hiring process

• Contact recruiter Venetia de Wit at Venetia.dewit@hirify.global.com.