Tech
What is a Machine Learning Engineer?
Complete job description for your hiring: role and missions, required skills, training, salary, and career paths
The Machine Learning Engineer designs, trains, and deploys models that can learn from data.
Their role: turn raw data into intelligent systems capable of predicting, classifying, or automating decisions.
It's a hybrid role between data science, software engineering, and applied research, demanding both a solid grasp of algorithms and engineering rigor.
Why do companies need this role?
With the rise of data and AI, companies are looking to industrialize their models.
The Machine Learning Engineer steps in where Data Scientists leave off: they put models in production, scale them, and ensure their performance over time.
In short, they turn a proof of concept into a real product.
The Machine Learning Engineer's missions
- Build, train, and test machine learning models.
- Design the data pipelines required for training.
- Optimize model performance and latency.
- Collaborate with data and product teams to align use cases with business strategy.
- Monitor performance and set up MLOps (monitoring, versioning, CI/CD, retraining).
Team collaboration
They work with:
- Data Scientists (for modeling and feature engineering)
- Data Engineers (for pipeline and infrastructure)
- AI Product Managers and sometimes applied AI researchers
Key skills of the Machine Learning Engineer
Technical:
- Languages: Python, SQL
- Frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost
- MLOps: MLflow, Kubeflow, Airflow, Docker, Kubernetes
- Cloud: AWS, GCP, Azure
Soft skills:
Rigor, analytical mindset, curiosity, cross-disciplinary collaboration.
Training & salary
- Master's/PhD in computer science, mathematics, or AI.
- Average salary: 45–60K€ at entry level, 70–90K€ for mid-level profiles, up to 120K€+ for seniors.
Possible career paths
Lead Machine Learning Engineer, Head of Data Science, AI Architect, or AI CTO.