Senior Machine Learning Engineer
Role details
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Tech stack
Job description
The Senior Machine Learning Engineer plays a key role in advancing Protolabs' intelligent pricing platform by building, maintaining, and improving machine learning models that power real-time quoting for custom-manufactured parts. Working within a complex two-sided marketplace, this role focuses on modelling demand and supply dynamics to enhance pricing accuracy and automation, enabling scalable, data-driven decisions even before manufacturing cost inputs are available. The position partners closely with engineering, data, and product teams to deliver innovative solutions that improve pricing performance and support overall business growth. What you'll do:
- Develop, improve, and maintain machine learning models that capture demand and supply dynamics in a digital manufacturing marketplace
- Build and refine pricing-related models, including cost estimation from CAD geometry, demand forecasting, and partner routing probability models
- Apply a range of ML techniques such as tree-based methods, probabilistic models, and deep learning to solve both 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 closely with ML engineers, data scientists, and domain experts in a cross-functional team.Translate complex marketplace inputs like part geometry, order history, and partner capacity into meaningful features for models
- Monitor model performance in production and proactively identify and address drift or degradation
- Stay up to date with advancements in machine learning, especially in pricing, marketplace modelling, and manufacturing intelligence
- Mentor and support mid-level and junior engineers within the team
Requirements
- 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
- Solid understanding of supervised and probabilistic modelling, including regression, classification, and uncertainty estimation
- Experience with feature engineering from structured and/or geometric data
- Hands-on experience with ML pipelines, model versioning, experiment tracking, and MLOps tools (e.g. Weights & Biases, Prefect, Karpenter)
- Comfortable working with real-world, messy data and solving ambiguous problems
Nice to have
- 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