machine learning engineer in digital manufacturing

Protolabs is a digital manufacturing company that brings innovative products to market through digitally enabled custom manufacturing. Its intelligent pricing platform supports real-time quoting for custom-manufactured parts in a two-sided marketplace.

• Develop, improve, and maintain machine learning models for demand and supply dynamics in a digital manufacturing marketplace;
• Build and refine pricing models, including CAD geometry cost estimation, demand forecasting, and partner routing probability models;
• Apply tree-based methods, probabilistic models, and deep learning to new and existing challenges;
• Design, build, and maintain reliable training and inference pipelines on AWS;
• Run offline experiments, including A/B testing and backtesting, to validate model improvements before deployment;
• Collaborate with ML engineers, data scientists, and domain experts in a cross-functional team;
• Translate part geometry, order history, and partner capacity into meaningful model features;
• Monitor production model performance and address drift or degradation;
• Stay up to date with advances in machine learning, pricing, marketplace modelling, and manufacturing intelligence;
• Mentor and support mid-level and junior engineers.

• Proven experience building and deploying machine learning models in production environments;
• Strong coding skills in Python or a similar language;
• Experience with PyTorch, TensorFlow, or scikit-learn;
• Solid understanding of supervised and probabilistic modelling, including regression, classification, and uncertainty estimation;
• Experience with feature engineering from structured or geometric data;
• Hands-on experience with ML pipelines, model versioning, experiment tracking, and MLOps tools such as Weights & Biases, Prefect, or Karpenter;
• Comfortable working with messy real-world data and solving ambiguous problems;
• Nice to have: marketplace or pricing models, operations research, econometrics, supply chain optimisation, 3D or geometric data, scaling ML infrastructure, explaining complex models to non-technical stakeholders, ML monitoring, alerting, and retraining workflows.

• Annual company bonus;
• Access to OpenUp psychologists and Headspace;
• Dog-friendly office;
• Daily lunch and snacks provided in the office;
• Learning and development days, funding for courses, events, and training;
• Access to the in-house LEARN platform;
• In-house 3D printing.