ML Engineer (Power)

Location: Paris, France; hybrid workplace

Company

hirify.global develops commodity, energy, and maritime intelligence platforms that transform complex data into actionable insights for trading firms, industrial companies, and analysts.

What you will do

• Design, build, and maintain production-grade machine learning pipelines and microservices for power market forecasting and electricity grid modeling.
• Convert statistical and machine learning prototypes into scalable, production-ready Python applications.
• Design and optimize PostgreSQL schemas for high-throughput time-series data, event streams, and normalization workflows.
• Establish automated model training, backtesting, evaluation, tuning, and feature and model versioning practices.
• Build reliable data ingestion and transformation pipelines with strong validation and low-latency analytical access.
• Contribute to modular, tested code, peer reviews, CI/CD automation, and Agile delivery alongside Data Scientists, Data Engineers, and Product teams.

Requirements

• Approximately 2–5 years of experience as a data-focused software engineer.
• Significant experience with large production Python codebases.
• Deep understanding of electricity grids, including generation, transmission, and electricity markets.
• Experience with data engineering, PostgreSQL or similar databases, database design, normalization, and time-series and event data.
• Experience with statistics, hypothesis testing, model training, evaluation, backtesting, tuning, model selection, and feature and model versioning.
• Confidence with Git, code reviews, Agile methodologies, and strong written and spoken English.

Nice to have

• Experience deploying machine learning workloads on AWS or GCP with Docker and Kubernetes.
• Familiarity with Apache Airflow, Kubeflow, or MLflow.
• Exposure to real-time streaming architectures such as Apache Kafka.

Culture & Benefits

• Full-time employment in a hybrid work environment.
• Collaboration across engineering, data science, data engineering, and product functions.
• Emphasis on making things happen, building together, and supporting colleagues.