Machine Learning Engineer

Responsibilities

• Develop ML models using in-process sensor data to identify anomalies and quality issues during printing. 

• Build and iterate on training and evaluation workflows; document experiments and results for reproducibility. 

• Own ML experimentation end to end: Design datasets, preprocessing pipelines, and training workflows; iterate on model architectures and metrics; document experiments and results for reproducibility. 

• Help define data collection and management: Partner with process and software teams to improve how build data is ingested, cataloged, versioned, and made available for training and evaluation. 

• Deploy models into production: Work with print software and embedded teams to integrate validated models into production code running on printer hardware, including performance and reliability considerations. 

• Collaborate with supporting software engineers: Hand off validated Python prototypes for production hardening, provide clear specifications and acceptance criteria, and support integration and regression testing. 

Requirements

• Bachelor's degree in Computer Science, Electrical Engineering, Applied Mathematics, or a related field; advanced degree preferred. 

• 3+ years of experience building and evaluating machine learning models in a professional setting. 

•  Hands-on experience with computer vision or image-based ML (e.g., segmentation, classification, or anomaly detection). 

• Strong Python skills and experience with modern ML frameworks (e.g., PyTorch). 

• Experience designing ML pipelines: data loading, preprocessing, training, evaluation, and experiment tracking. 

• Comfort working in a production software environment: version control, code review, testing, and cross-functional collaboration. 

• Ability to communicate technical tradeoffs clearly to engineers and non-engineers. 

• Strong programming skills in Python or C++. 

• Experience organizing and working with structured and unstructured datasets. 

• Background in a STEM or scientific discipline, with demonstrated use of ML to address substantive technical or engineering problems. 

Bonus  

• Experience with powder bed fusion or other additive manufacturing processes. 

• Knowledge of manufacturing data workflows, IoT sensor data, or industrial automation systems. 

• Experience with image-based or time-series machine learning. 

• Familiarity with model deployment in production or embedded environments. 

• Familiarity with cloud storage and data pipelines (e.g., AWS S3, batch retrieval workflows). 

• Experience in domains such as robotics, aerospace, materials, instrumentation, scientific computing, or other fields where ML is applied to physical or experimental data.