Founding Machine Learning Engineer
Design, develop, and optimize machine learning and generative AI systems for pharmaceutical marketing content evaluation.
- New York, New York, United States
- On-site
- $140K–$250K per year
- Posted Nov 11, 2026
INDUSTRYSoftware & InternetAI & Machine Learning
Responsibilities
- 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.
- Collaborate with engineering, product, regulatory, and business teams to translate scientific, regulatory, and commercial requirements into production-ready AI solutions.
Requirements
- Master's degree, or foreign equivalent, in Computer Science or a related field
- Experience in a Life sciences
- Experience in a Pharmaceutical marketing
Skills
Machine learning and deep learning model developmentNatural language processing and large language model applicationsMachine learning model development and applied AI/ML engineeringPython and machine learning frameworks, including PyTorch or TensorFlowMachine learning data pipeline development and processingModel evaluation, experimentation, and performance benchmarking, including evaluation of factuality, groundedness, or accuracyDeployment of machine learning models and systems in production environmentsSoftware debugging, testing, monitoring, and performance optimizationPythonPyTorchTensorFlow
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