Sr Director, Applied AI Engineering

Role Summary

The Senior Director of Applied AI Engineering leads the development and deployment of advanced AI applications within Gilead’s drug development portfolio. This leader focuses on translating AI research—particularly in generative AI, LLMs, and autonomous agents—into scalable, production-ready tools and drives collaboration across clinical operations, regulatory, medical writing, and real-world evidence teams.

Responsibilities

• Design, deploy, and maintain machine learning and generative AI systems that address real-world challenges in drug development (e.g., trial optimization, regulatory authoring, medical review automation).
• Translate cutting-edge research into practical tools that impact the business.

• Partner with AIx and DPMx to define, build, and integrate AI-powered solutions into key processes.
• Engage with IT and DevSys to ensure secure, scalable infrastructure and seamless handoffs from prototyping to production.

• Architect and optimize AI/ML pipelines across model training, inference, and monitoring.
• Define engineering best practices for LLMOps and applied AI across therapeutic areas.

• Build and lead a team of applied AI engineers.
• Foster a culture of ownership, speed, and continuous learning.
• Mentor AI engineers, and champion strong coding, testing, and documentation standards.

• Track usage, performance, and business impact of deployed AI systems.
• Partner with domain experts to calibrate model outputs and continuously improve system behavior.

Qualifications

• Required: Advanced degree (MS/PhD) in Computer Science, Machine Learning, Data Science, or related field.
• Required: 14+ years of experience in AI/ML engineering, with at least 5 years in leadership roles.
• Required: Proven track record of deploying AI systems at scale in high-stakes domains (e.g., life sciences, healthcare, finance).
• Required: Expertise in deep learning and large language models (e.g., transformers, RAG, distillation).
• Required: Proficiency in frameworks such as PyTorch or TensorFlow.
• Required: Strong engineering foundation (data structures, architecture, testing, CI/CD).
• Required: Excellent communicator with the ability to navigate ambiguity and drive alignment across science, engineering, and operations.

• Preferred: Experience working in or alongside clinical, regulatory, or medical affairs functions.
• Preferred: Familiarity with GxP, 21 CFR Part 11, and other relevant compliance frameworks.
• Preferred: Experience with AI agents, model evaluation for healthcare, and multimodal data integration.

Skills

• Deep learning and large language models (transformers, RAG, distillation)
• AI/ML pipeline architecture, training, inference, monitoring
• LLMOps practices and applied AI across therapeutic areas
• PyTorch, TensorFlow, and related ML frameworks
• Software engineering best practices (testing, CI/CD, documentation)
• Cross-functional collaboration and stakeholder management