AI Engineering Intern

Master Control Inc. is a leading provider of cloud-based quality and compliance software for life sciences and other regulated industries. Our mission is the same as that of our customers to bring life‑changing products to more people sooner. The Master Control Platform helps organizations digitize, automate and connect quality and compliance processes across the regulated product development life cycle. Over 1,000 companies worldwide rely on Master Control solutions to achieve new levels of operational excellence across product development, clinical trials, regulatory affairs, quality management, supply chain, manufacturing and postmarket surveillance.

For more information, visit

Master Control Inc. is a leading provider of cloud-based quality and compliance software for life sciences and other regulated industries. Our mission is the same as that of our customers to bring life‑changing products to more people sooner. The Master Control Platform helps organizations digitize, automate and connect quality and compliance processes across the regulated product development life cycle. Over 1,000 companies worldwide rely on Master Control solutions to achieve new levels of operational excellence across product development, clinical trials, regulatory affairs, quality management, supply chain, manufacturing and postmarket surveillance.

For more information, visit

Master Control is looking for an Intern to help build AI capabilities for life-sciences quality and manufacturing. Our AI/ML platform combines predictive machine learning, self-hosted language models, and governed analytical workflows to help customers identify quality risks, investigate deviations, and understand manufacturing performance.

You will work alongside data scientists, ML engineers, and platform engineers on real manufacturing execution records and quality‑event data. This internship offers hands‑on experience across data preparation, model development, evaluation, and production integration, with projects scoped to your experience and supported by technical mentorship.

• Prepare data and develop features. Help transform manufacturing records, quality events, and related operational data into reliable training and evaluation datasets. Investigate data quality, missing values, and relationships between process execution and quality outcomes.
• Build and evaluate predictive models. Contribute to models for operational risk scoring, early batch‑outcome prediction, recurring deviations, and nonconformance risk. Compare simple statistical and machine learning baselines with more advanced approaches.
• Support semantic search and quality‑event analysis. Experiment with text embeddings, clustering, and retrieval methods to identify similar deviations, organize unstructured information, and improve access to relevant evidence.
• Contribute to governed language‑model applications. Help develop and test capabilities that interpret user questions, connect them to approved analytical procedures, and produce responses grounded in computed results and supporting evidence.
• Run rigorous experiments. Create reproducible evaluations, investigate failure cases, and document findings. Apply appropriate validation methods, prevent data leakage, and assess performance beyond overall accuracy, including false positives, calibration, and prediction lead time where relevant.
• Help integrate models into production. Write and test Python services and APIs, contribute to training and inference pipelines, and measure reliability, latency, and computational efficiency under the guidance of ML and platform engineers.
• Maintain reproducibility and traceability. Document datasets, features, model…