Senior Machine Learning Engineer

Join Protolabs as a Senior Machine Learning Engineer, where you will play a crucial role in advancing our intelligent pricing platform. You will build, maintain, and improve machine learning models that power real-time quoting for custom-manufactured parts. This position requires a solid understanding of supervised and probabilistic modeling, experience with feature engineering, and a strong coding background in Python. You will collaborate closely with cross-functional teams and mentor junior engineers.

• Développer, améliorer et maintenir des modèles d'apprentissage automatique qui capturent les dynamiques de demande et d'offre dans un marché de fabrication numérique.
• Construire et affiner des modèles liés à la tarification, y compris l'estimation des coûts à partir de la géométrie CAD, la prévision de la demande et les modèles de probabilité de routage des partenaires.
• Collaborer étroitement avec les ingénieurs en apprentissage automatique, les scientifiques des données et les experts en la matière dans une équipe interfonctionnelle.
• Solid understanding of supervised and probabilistic modelling, including regression, classification, and uncertainty estimation
• Experience with feature engineering from structured and/or geometric data
• Comfortable working with real-world, messy data and solving ambiguous problems
• Hands-on experience with ML pipelines, model versioning, experiment tracking, and MLOps tools (e.g. Weights & Biases, Prefect, Karpenter)
• Proven experience building and deploying machine learning models in production environments
• Strong coding skills in Python (or similar), with experience using ML frameworks such as PyTorch, TensorFlow, or scikit-learn
• Experience with marketplace or pricing models (e.g. demand modelling, price elasticity, cost estimation)
• Background in operations research, econometrics, or supply chain optimisationExperience working with 3D or geometric data (e.g. CAD, point clouds, mesh processing)
• Experience designing and scaling ML infrastructure for production systems
• Strong communication skills with the ability to explain complex models to non-technical stakeholders
• Experience with ML monitoring, alerting, and retraining workflows