Senior Machine Learning Engineer

Hybrid
- Eindhoven, Noord-Brabant, Netherlands
- Amsterdam, Noord-Holland, Netherlands
+1 more
Engineering

Why you’ll love this job

Help us bridge the gap between cutting-edge machine learning research and production-ready AI systems. As part of our Machine Learning Engineering team, you’ll leverage graph-enhanced ML systems and deep learning models to unlock new capabilities in our knowledge graph infrastructure. You’ll work closely with data scientists and engineers to build robust ML pipelines, optimize model performance, and deploy scalable, production-ready AI systems. Your work will directly contribute to groundbreaking solutions that push the boundaries of AI applications.

The Role: As a Machine Learning Engineer, you will take ownership of the entire ML lifecycle, from experimentation and training to deployment and monitoring. You will design and implement end-to-end ML pipelines, optimize models for production environments, and develop graph-based ML systems using our knowledge graph. This is a hands-on role where you’ll collaborate with data scientists to productionize models, while ensuring scalability and reliability.

Your mission at:

Assess and select the best machine learning algorithms for diverse business challenges.
Design and build end-to-end ML pipelines for training, evaluation, and deployment.
Develop and optimize deep learning models for real-world performance.
Leverage graph structures to build graph-enhanced ML systems.
Implement efficient data pipelines for ML training and inference.
Build and maintain infrastructure for continuous model training, monitoring, and versioning.
Collaborate with cross-functional teams to productionize research models.
Design and maintain scalable model-serving architectures.
Monitor and optimize model performance in live environments.

What makes you a great candidate:

Experience: 5+ years in machine learning engineering or related roles.
Technical Expertise:
- Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Scikit-learn).
- Experience with MLOps tools (e.g., MLflow, Kubeflow, DVC).
- Strong understanding of graph-based ML and graph neural networks.
- Experience with containerization and orchestration (Docker, Kubernetes).
- Familiarity with CI/CD practices and software engineering best practices.
- Experience with graph databases and query languages (e.g., Neo4j, Cypher).
Domain Knowledge:
- Deep understanding of the ML lifecycle and model serving architectures.
- Knowledge of ML monitoring and observability.
- Familiarity with graph-based feature engineering and modern deep learning architectures.
Professional Skills:
- Proven track record of deploying ML systems to production.
- Strong problem-solving and debugging skills.
- Excellent communication and collaboration abilities.
Nice to Have:
- Experience with knowledge graph embeddings and multi-modal ML systems.
- Background in NLP or information extraction.
- Contributions to ML open-source projects or publications in ML conferences/journals.