ML) Capability Engineering Team Lead

MSD•Rahway, NJ

4d•Hybrid

About The Position

The AI/ML Capability Engineering Team Lead supervises a cross-functionally embedded team of AI/ML Engineers and Data Scientists who develop AI Natural Language Processing and Agentic AI capabilities for therapeutic research and development. The role leads AI initiatives, contributes to AI strategy, and partners with stakeholders across the R&D pipeline and software technology stack. The team’s models and pipelines are intended to address real-world problems and improve patient outcomes. The position involves building, deploying, and publishing robust AI methods and agentic workflows in areas such as machine translation, content generation and document drafting, workflow optimization, data enrichment, literature mining, target and biomarker prioritization, and representation learning. The lead works with stakeholders and product teams to design and iteratively improve solutions, rigorously evaluates and optimizes AI workflows, and develops human-in-the-loop approaches. The role also tracks evolving AI regulations and internal policies, presents plans and results to scientific and IT leadership, contributes to strategic planning, and supervises, mentors, and recruits team members. The position is based in Rahway, New Jersey, with a hybrid work arrangement. It includes 10% travel. The role is a regular employee position; domestic relocation is available, and visa sponsorship is not available.

Requirements

PhD in data science, AI/ML/LLM/Agentic system engineering, computer science, semantic engineering, a related discipline, or a scientific field, plus 5 years of relevant industry experience; or an M.S. in a similar field plus 6 years; or a B.S. in a similar field plus 7 years.
5 years of technical leadership experience with AI/ML, NLP, or Agentic AI projects or products.
2 years of experience supervising team members.
Excellent technical and non-technical communication, planning, teamwork, and leadership skills.
Ability to critically evaluate new problems and AI/ML solution architecture and implementation.
Strategic mindset and ability to envision how AI/ML and software solutions may generalize to new application areas.
Deep understanding of modern LLM and Agentic architectures, including RAG, tool calling/MCP, structured extraction, and agent orchestration.
Fluency in Python, Git, environment management such as uv or Docker, and at least one ML framework such as PyTorch or vLLM.
Excellent statistical analysis skills, including understanding the implications of class imbalance.
Experience with scalable engineering frameworks such as Apache Spark and orchestrator frameworks such as Airflow.
Experience with Agentic AI protocols and standards such as MCP, A2A, and A2UI, and with building production-ready software and AI solutions.
Experience with harness optimization approaches such as auto-research.
Experience with AI/ML model deployment and operations and modern software deployment standards such as DevOps and CI/CD.
Experience with agentic system development such as LangGraph, text processing, semantic engineering such as RDF, and/or data engineering.
Familiarity with enterprise AI governance, data privacy boundaries, validation, fairness, and ethical AI topics.
Required skills include Apache Airflow, Apache Spark, Artificial Intelligence, Data Engineering, Git, IBM watsonx.governance, Image Processing, LangGraph, Machine Learning, Natural Language Processing, Orchestration, Python, SDLC, Solution Architecture, System Designs, System Integration, Systems Engineering, and Testing.

Nice To Haves

Experience with agentic system development, such as LangGraph.
Experience with text processing, including regular expressions, information retrieval, and search systems.
Experience with semantic engineering, such as RDF.
Experience with data engineering.
Experience with harness optimization approaches, such as auto-research.
Familiarity with enterprise AI governance, data privacy boundaries, validation, fairness, and ethical AI topics.

Responsibilities

Supervise a cross-functionally embedded team of AI/ML Engineers and Data Scientists.
Lead AI initiatives and contribute to AI strategy for therapeutic research and development.
Build, deploy, and publish robust AI methods and agentic workflows for research and development use cases.
Partner with stakeholders and product teams to ideate, design, build, and iteratively improve AI solutions.
Evaluate and optimize machine learning and AI workflows using standard and novel techniques.
Develop human-in-the-loop AI approaches.
Collaborate with experts across the R&D pipeline and across informatics and AI/ML areas, including image processing, bioinformatics, representation learning, and decision sciences.
Track and help adhere to evolving AI regulations and internal policies, including data privacy and risk mitigation requirements.
Stay current with innovations in AI/ML and Agentic AI and use AI coding assistants innovatively and responsibly.
Present plans and results to scientific and IT leadership and provide input to strategic planning.
Supervise, mentor, and recruit AI/ML engineers and other team members.