Senior Machine Learning Engineer

Join Protolabs as a Senior Machine Learning Engineer based in India, where you will be instrumental in enhancing our intelligent pricing capabilities. You will develop, maintain, and optimize machine learning models that enable real-time quoting for custom-manufactured parts within a complex two-sided marketplace. Your work will focus on modeling demand and supply dynamics to improve pricing accuracy and automation, facilitating scalable, data-driven decisions even before manufacturing cost inputs are available. Collaborating closely with engineering, data, product, and domain teams, you will create innovative solutions that drive pricing performance and support business growth.

Key Responsibilities

- Develop, improve, and maintain machine learning models that capture demand and supply dynamics in a digital manufacturing marketplace.
- Build and refine pricing models to support intelligent and automated quoting processes.
- Create models for cost estimation from CAD geometry, demand forecasting, and partner routing probability.
- Apply diverse machine learning techniques, including tree-based methods, probabilistic models, and deep learning, to address both new and existing business challenges.
- Translate complex marketplace inputs such as part geometry, order history, and partner capacity into meaningful model features.
- Design, build, and maintain reliable ML training and inference pipelines on AWS, ensuring scalable workflows for experimentation, training, validation, and deployment.
- Implement model versioning, experiment tracking, and production-ready ML systems, contributing to the scaling of ML infrastructure as business needs evolve.
- Conduct offline experiments, A/B testing, and backtesting to validate model improvements before deployment.
- Monitor production models to detect drift, degradation, or unexpected behavior, supporting alerting and retraining workflows to sustain model performance.
- Collaborate effectively with ML engineers, data scientists, product teams, and domain experts to translate marketplace dynamics into machine learning solutions.
- Communicate model behavior and results clearly to both technical and non-technical stakeholders.
- Mentor mid-level and junior engineers, sharing best practices and fostering a collaborative engineering environment focused on continuous learning.
- Stay current with advancements in machine learning, particularly in pricing, marketplace modeling, and manufacturing intelligence, and pragmatically apply relevant innovations to production challenges.

Required Qualifications

- Proven experience building and deploying machine learning models in production environments.
- Strong programming skills in Python or similar languages.
- Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Solid understanding of supervised and probabilistic modeling, including regression, classification, and uncertainty estimation.
- Experience in feature engineering from structured and geometric data.
- Familiarity with ML pipelines, model versioning, experiment tracking, and MLOps tools like Weights & Biases, Prefect, or equivalent.
- Experience designing and scaling ML infrastructure for production systems.
- Knowledge of ML monitoring, alerting, and retraining workflows.
- Strong communication skills with the ability to explain complex models to non-technical stakeholders.
- Experience working in cross-functional teams and mentoring junior engineers.
- Ability to work effectively with ambiguous problems and real-world, messy data.
- A problem-solving mindset with a practical, data-driven approach and strong ownership of model performance and reliability.

Preferred Qualifications and Additional Information

- Experience with marketplace or pricing models, including demand modeling, price elasticity, or cost estimation.
- Background in operations research, econometrics, or supply chain optimization.
- Experience working with 3D or geometric data such as CAD, point clouds, or mesh processing.
- Strong communication skills and experience explaining complex models to non-technical stakeholders.

Protolabs may utilize artificial intelligence tools to assist in parts of the hiring process, such as reviewing applications and analyzing resumes. These tools support but do not replace human judgment, with final hiring decisions made by humans.

This role offers an exciting opportunity to contribute to cutting-edge machine learning applications in manufacturing intelligence, working in a collaborative environment that values innovation and continuous improvement.