ML) Capability Engineering Team Lead

MSD•Rahway, NJ

6d•$173,200 - $272,600•Hybrid

About The Position

The Artificial Intelligence / Machine Learning (AI/ML) Capability Engineering Team Lead supervises a cross-functionally embedded team of AI/ML Engineers and Data Scientists developing AI Natural Language Processing (NLP) and Agentic AI capabilities for therapeutic research and development. The role works with partners across the R&D pipeline and software technology stack to lead AI initiatives, influence AI strategy, and oversee the development of models and pipelines intended to solve real-world problems and improve patient outcomes. The team builds, deploys, and publishes AI methods and agentic workflows in areas including machine translation, content generation and document drafting, workflow optimization, data enrichment, literature mining, target and biomarker prioritization, and representation learning. The lead partners with stakeholders and product teams to ideate, build, evaluate, and improve solutions; develops human-in-the-loop approaches; and collaborates across informatics and AI/ML disciplines. The role also monitors AI regulations and internal policies related to data privacy and risk mitigation, stays current with AI/ML innovations, 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 and 10% travel. Domestic relocation is listed. 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 can 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 orchestration 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, and/or data engineering.
Familiarity with enterprise AI governance, data privacy boundaries, validation, fairness, and related ethical AI topics.
Required skills listed: Apache Airflow, Apache Spark, Artificial Intelligence (AI), Data Engineering, Git, IBM watsonx.governance, Image Processing, LangGraph, Machine Learning (ML), Natural Language Processing (NLP), Orchestrate, Python, Software Development Life Cycle (SDLC), Solution Architecture, System Designs, System Integration, Systems Engineering, and Testing.
Passion for technology, science, biomedicine, and mentorship.

Responsibilities

Supervise a cross-functionally embedded team of AI/ML Engineers and Data Scientists.
Lead AI initiatives and influence AI strategy for therapeutic research and development.
Build, deploy, and publish AI methods and agentic workflows for applications including machine translation, content generation, document drafting, workflow optimization, data enrichment, literature mining, target and biomarker prioritization, and representation learning.
Partner with stakeholders and product teams to ideate, design, build, and iteratively improve solutions.
Evaluate and optimize machine learning and AI workflows using standard and novel techniques, including developing human-in-the-loop approaches.
Collaborate with experts across the R&D pipeline and areas such as 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.
Stay up to date on AI/ML and Agentic AI innovations and use AI coding assistants in innovative and responsible ways.
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.