Director II, Discovery Biotherapeutics AI/ML

Company Description

AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas – immunology, oncology, neuroscience, and eye care – and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on X, Facebook, Instagram, YouTube, LinkedIn and Tik Tok.

Job Description

Role Summary

AbbVie is building a best-in-class, AI-enabled antibody discovery engine. We need an industry proven leader to architect, deploy, and continuously improve this platform, applying state-of-the-art machine learning and protein language models to advance large molecule assets from Hit ID through IND.

• Reporting structure: VP of Discovery Research Biotherapeutics. 
• Department: Biotherapeutics & Genetic Medicine (BGM) – Discovery Research

Core Responsibilities

• Provide scientific direction, resource prioritization, and career development for a cross functional computational sciences team.
• Embed AI/ML tools—including protein LLMs, structural prediction, and generative design—into antibody discovery workflows (target assessment, hit generation, affinity maturation, developability).
• Partner with discovery biology, structural biology, preclinical safety, developability, and enterprise AI groups to co-deliver portfolio impact.
• Serve as matrix leader for digital transformation initiatives; track KPIs and communicate progress to executive leadership.
• Scout emerging technologies, benchmark against academic/industry best practice, and position AbbVie as an innovation partner of choice.

Key Outcomes

• Define, socialize, and execute a multiyear AI/ML strategy for antibodies and other biotherapeutic discoveries.
• Hire, mentor, and inspire an interdisciplinary team of computational and experimental scientists.
• Deliver digital transformation “use cases” that shorten design–make–test cycles or improve candidate quality (e.g., ML-driven CDR diversification, developability