Director, AI Platform & Agentic Engineering, AI & Genome Sciences

Merck•Cambridge, MA

2d•$194,100 - $305,600•Hybrid

About The Position

AI/ML is central to the company's mission to accelerate target and biomarker discovery through a deeper understanding of disease biology. The Director, AI Platform & Agentic Engineering will build and lead the technical capabilities that enable scientists to rapidly develop, evaluate, and apply cutting-edge AI/ML methods. The role works across genome-scale perturbation data, proprietary foundation models, large-scale biological datasets, and experimental partnerships that allow model predictions to be tested in the lab. This hands-on technical leadership role spans software and data engineering, scientific computing, foundation models, and agentic AI. The director will build an agile research platform that connects multimodal data, knowledge graphs, scalable compute, and reusable AI tools to accelerate experimentation, reproducible research, and scientific discovery. The role owns the software development lifecycle for shared AI/ML tools and applications, while remaining engaged in architecture, prototyping, and implementation and mentoring engineers and supporting scientists.

Requirements

PhD in a relevant field such as Computer Science, Electrical Engineering, Data Science, Physics, Informatics, AI/ML, Mathematics, or a related STEM discipline, with 6 or more years of industry experience; or a Master's degree with 10 or more years of industry experience.
Experience building AI/ML infrastructure, scientific software, or high-performance computing environments.
Experience with cloud and computing technologies such as AWS, Databricks, Spark, Docker, Kubernetes, GitHub, and CI/CD.
Strong software engineering expertise, particularly in Python, AI/ML frameworks, and modern software architecture.
Experience developing GenAI and agentic AI systems, including frameworks such as LangGraph, Semantic Kernel, AutoGen, or equivalent.
Experience with RAG, vector databases, knowledge graphs, and agent orchestration.
Strong communication skills and ability to work effectively across scientific, engineering, and technology teams.

Nice To Haves

Advanced degree in Computer Science, Engineering, Machine Learning, Physics, or a related quantitative field, with significant industry experience.
Hands-on experience with foundation model training, fine-tuning, and evaluation.
Experience developing computational capabilities for biological or biomedical research.
Experience creating internal scientific platforms, developer tools, or open-source software.
Life science experience.
Omics experience.

Responsibilities

Build and evolve the AI/ML research environment, developer tooling, and reusable capabilities that connect scientists with data, knowledge, compute, and models.
Enable foundation model training, fine-tuning, and evaluation.
Develop frameworks for AI agents, tool integration, retrieval, orchestration, and human-in-the-loop scientific workflows.
Partner with IT and technology teams to provide flexible access to cloud, Databricks, GPU clusters, and other high-performance computing environments for AI research.
Own the SDLC for shared AI/ML tools and applications, establishing pragmatic standards for software architecture, testing, CI/CD, reproducibility, security, and maintainability.
Remain actively engaged in architecture, prototyping, and implementation.
Mentor engineers and support scientists in translating new AI approaches into scientific applications.