Machine Learning Engineer

Design, develop, and optimize machine learning and generative AI systems used to evaluate pharmaceutical marketing and promotional content for scientific accuracy, evidentiary support, and regulatory compliance. Develop large language model applications using prompt engineering, retrieval-augmented generation (RAG), multi-agent orchestration, model experimentation, fine-tuning, and performance benchmarking to analyze clinical literature, prescribing information, regulatory guidance, and other life sciences source materials.

Build machine learning pipelines for content classification, claim extraction, source attribution, evidence verification, and contextual compliance analysis. Design and deploy scalable production infrastructure supporting AI applications, including model serving, retrieval systems, data processing, evaluation frameworks, testing, monitoring, and system integrations.

Develop and maintain evaluation systems to measure model factuality, groundedness, accuracy, reliability, and compliance-related performance. Monitor, debug, and optimize production machine learning systems for accuracy, reliability, scalability, latency, and performance.