Senior Machine Learning Engineer

Join the team as our new Senior Machine Learning Engineer – India

You will play a key role in advancing Protolabs’ intelligent pricing capabilities by building, maintaining, and improving machine learning models that support real-time quoting for custom-manufactured parts. Working within a complex two-sided marketplace, you will model demand and supply dynamics to improve pricing accuracy and automation, enabling scalable, data-driven decisions even before manufacturing cost inputs are available. You will work closely with engineering, data, product, and domain teams to develop innovative solutions that improve pricing performance and support business growth.

What You'll Do:

Build & Evolve Machine Learning Models

• Develop, improve, and maintain machine learning models that capture demand and supply dynamics within a digital manufacturing marketplace.
• Build and refine pricing-related models supporting intelligent and automated quoting.
• Develop models for cost estimation from CAD geometry, demand forecasting, and partner routing probability.
• Apply a range of machine learning techniques, including tree-based methods, probabilistic models, and deep learning, to solve both new and existing business challenges.
• Translate complex marketplace inputs such as part geometry, order history, and partner capacity into meaningful model features.

Design & Maintain ML Training and Inference Pipelines

• Design, build, and maintain reliable machine learning training and inference pipelines on AWS.
• Develop scalable workflows for experimentation, model training, validation, and deployment.
• Apply appropriate model versioning and experiment tracking practices.
• Build solutions that support reliable production machine learning systems.
• Contribute to scaling ML infrastructure as model and business requirements evolve.

Validate & Improve Model Performance

• Conduct offline experiments to evaluate and validate model improvements before deployment.
• Apply A/B testing and backtesting approaches to assess model performance.
• Monitor models in production and proactively identify model drift, degradation, or unexpected behaviour.
• Support monitoring, alerting, and retraining workflows to maintain model performance over time.
• Continuously improve models based on production performance and evolving business requirements.

Collaborate Across Engineering, Data & Product

• Work closely with ML Engineers, Data Scientists, Product teams, and domain experts in cross-functional teams.
• Collaborate with stakeholders to understand marketplace dynamics and translate business challenges into machine learning solutions.
• Work with real-world, complex, and imperfect datasets to develop practical solutions to ambiguous problems.
• Communicate model behaviour, results, and technical considerations effectively across technical and non-technical stakeholders.
• Contribute to the development of machine learning capabilities that support manufacturing intelligence and business growth.

Mentor & Support Engineering Talent

• Mentor and support mid-level and junior engineers within the team.
• Share machine learning engineering practices, technical knowledge, and lessons learned.
• Contribute to a collaborative engineering environment focused on continuous learning and improvement.

Stay Current with Machine Learning Innovation

• Stay up to date with advancements in machine learning, particularly in pricing, marketplace modelling, and manufacturing intelligence.
• Evaluate emerging approaches and techniques that could improve model performance and business outcomes.
• Apply relevant advances pragmatically to production machine learning challenges.

What It Takes:

Technical

• Proven experience building and deploying machine learning models in production environments.
• Strong coding skills in Python or a similar programming language.
• Hands-on experience with 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 such as Weights & Biases, Prefect, Karpenter, or equivalent technologies.
• Experience designing and scaling ML infrastructure for production systems.
• Experience with ML monitoring, alerting, and retraining workflows.

Leadership & Collaboration

• Experience working effectively in cross-functional teams with ML Engineers, Data Scientists, Product teams, and domain experts.
• Strong communication skills, with the ability to explain complex machine learning models and concepts to non-technical stakeholders.
• Ability to work effectively with ambiguous problems and real-world, messy data.
• Experience mentoring and supporting junior and mid-level engineers.
• Ability to translate complex business and marketplace requirements into practical machine learning solutions.

Mindset

• Strong problem-solving mindset with the ability to work through ambiguous and complex challenges.
• Practical and data-driven approach to developing and improving machine learning solutions.
• Curiosity and willingness to explore emerging machine learning techniques and approaches.
• Strong ownership of model performance, reliability, and production outcomes.
• Collaborative mindset with a willingness to share knowledge and support the development of other engineers.
• Ability to balance experimentation and innovation with the reliability requirements of production systems.
Preferred Qualifications
• Experience with marketplace or pricing models, including demand modelling, price elasticity, or cost estimation.
• Background in operations research, econometrics, or supply chain optimisation.
• Experience working with 3D or geometric data, including CAD, point clouds, or mesh processing.
• Experience designing and scaling ML infrastructure for production systems.
• Experience with ML monitoring, alerting, and automated retraining workflows.
• Strong communication skills and experience explaining complex models to non-technical stakeholders.