Sr. Manager, AI/ML Lead - Foster City, CA

Gilead Sciences is seeking an AI/ML Lead to design, build, and operationalize an AI-powered platform that accelerates Legal and Compliance review of materials. The solution leverages AI models trained on regulatory requirements and tunable on Gilead products, internal materials, and best-practice templates.

You will integrate FDA and other regulatory knowledge bases alongside Gilead-specific labels and clinical data to enable faster, more consistent, higher-quality material review while reducing regulatory risk.

This is a high-impact role at the intersection of generative AI, regulatory science, and enterprise data engineering, partnering closely with Legal, Regulatory, and Compliance stakeholders

Key Responsibilities:

Model development & tuning: Build, fine-tune, and evaluate AI/LLM models trained on regulatory requirements and adaptable to Gilead products, internal content, and approved templates.

Knowledge base engineering: Design and maintain retrieval-augmented (RAG) knowledge bases incorporating FDA and other regulatory requirements, Gilead-specific product labels, and clinical data.

Review automation: Develop capabilities that check materials for label consistency, claim substantiation against clinical data, citation accuracy, balanced efficacy/safety information, and IP protection (patents, trademarks).

Data pipelines: Engineer secure, scalable ingestion and processing pipelines for diverse material types (strategy documents, ad board slides, scientific narratives, congress symposium slides, press releases, external publications, grants, market research, RFAs, and more).

Evaluation & quality: Establish evaluation frameworks, accuracy benchmarks, and human-in-the-loop validation to ensure trustworthy, auditable outputs.

Stakeholder collaboration: Collaborate with Legal, Compliance, and Regulatory teams to turn review workflows into AI-powered solutions that speed up material review while lowering regulatory risk. Serve as the bridge between business stakeholders and AI delivery converting Legal and Compliance review needs into trusted, auditable solutions that cut review time and regulatory exposure.

Governance & compliance: Ensure solutions meet data privacy, security, validation (GxP where applicable), and responsible-AI standards.

MLOps: Implement model deployment, monitoring, versioning, and continuous improvement practices.

Basic Qualifications:

• 5+ years of experience in AI/ML engineering, with hands-on work building LLM/generative-AI applications (fine-tuning, RAG, prompt engineering).

• Strong programming skills in Python and modern ML frameworks.

• Experience designing knowledge bases and retrieval systems over large, unstructured document corpora.

• Experience with AWS cloud AI platforms and MLOps tooling.

• Solid data engineering fundamentals: pipelines, data modeling, and large-scale processing.

• Experience in pharmaceutical, biotech, life sciences, or other highly regulated industries.

• Familiarity with FDA and global regulatory requirements, promotional review (MLR/MRC/PRC) processes, or compliance workflows.

• Experience with contract lifecycle management (CLM) systems — Apttus/Conga preferred — and contract data structures.

• Knowledge of clinical data structures, product labeling, and medical/scientific content.

• Experience with responsible AI, model governance, and validation in regulated environments.

• Understanding of IP concepts (patents, trademarks) as applied to content review.