Director, AI Systems Quality & Governance, R&D

Gilead Sciences•Foster City, CA

20h•Hybrid

About The Position

Gilead is seeking a Director, AI Systems Quality & Governance, R&D to establish and operationalize the quality framework for artificial intelligence across Research & Development. Reporting to the Senior Director of eSystems Quality for R&D, this role will define how AI-enabled solutions are assessed, developed, tested, implemented, monitored, changed, and retired in a regulated environment. The Director will build quality, data integrity, risk management, and appropriate controls into AI solutions by design, and establish the documentation, testing, traceability, and ongoing monitoring needed to demonstrate that solutions remain fit for intended use and inspection-ready throughout their lifecycle. The role develops risk-based AI lifecycle, risk-and-control, data-quality, testing and assurance, monitoring, vendor evaluation, and inspection-readiness approaches. It also builds AI quality capabilities across R&D and eSystems Quality through playbooks, templates, decision tools, examples, and training. The Director provides product direction for the R&D Quality Intelligence Platform, advancing it from reporting toward quality intelligence and decision support while ensuring its AI capabilities meet established governance requirements. Extensive experience in a Quality organization is not required. The position seeks someone with experience in AI, data, digital products, technology risk, or controls who is willing to develop deeper knowledge of GxP regulations and apply guidance from colleagues with expertise in Quality, validation, data integrity, and regulated R&D processes. This is an onsite role under Gilead’s G.Flex policy, with onsite work Tuesday through Thursday.

Requirements

Bachelor’s degree in computer science, data science, engineering, information systems, life sciences, or a related discipline and 12 or more years of relevant experience; or a master’s degree in one of these fields and 10 or more years; or a Ph.D. in one of these fields and 8 or more years.
Significant experience in AI, machine learning, advanced analytics, digital products, data governance, technology risk, controls, or a related field.
Experience implementing, evaluating, or governing AI-enabled solutions in a complex enterprise environment.
Understanding of how AI models are developed, tested, deployed, monitored, changed, and retired.
Experience evaluating model performance, data quality, drift, human oversight, vendors, and operational controls.
Ability to translate technical risks into clear, implementable, and testable requirements.
Strong documentation, communication, facilitation, and stakeholder-management skills.
Willingness to develop deeper expertise in GxP regulations, Quality systems, validation, data integrity, and inspection readiness.

Nice To Haves

Experience in pharmaceutical, biotechnology, healthcare, medical device, financial services, or another regulated industry.
Familiarity with Clinical Operations, Regulatory Affairs, Patient Safety, Clinical Data Management, Medical Affairs, or related R&D processes.
Experience developing AI governance frameworks, lifecycle standards, model-risk frameworks, or control requirements.
Experience evaluating AI vendors, platforms, and embedded AI capabilities.
Familiarity with responsible AI, data lineage, model documentation, internal controls, validation, or risk-based assurance.
Product-management or product-owner experience.
An advanced degree in a technical, scientific, data, or business discipline.

Responsibilities

Establish, implement, and maintain a risk-based AI Development Lifecycle for R&D, defining requirements from intake and intended use through risk classification, data readiness, development, testing, release, monitoring, change management, periodic review, and retirement.
Define lifecycle documentation, evidence, reviews, approvals, decision points, and inspection expectations, and incorporate quality and control requirements during solution design and development.
Align AI lifecycle requirements with applicable GxP regulations, Gilead procedures, data-integrity principles, and inspection expectations.
Develop a risk and control framework for AI-enabled solutions, considering model performance, unreliable outputs, bias, data quality, drift, explainability, automation, human oversight, privacy, security, and vendor-controlled changes.
Define implementable, testable, and monitorable controls, including requirements for human review, intervention, override, escalation, documentation, and evidence of control effectiveness.
Work with GxP Quality subject-matter experts to determine when formal change control, deviation, investigation, CAPA, or other Quality-system processes apply.
Define data requirements for AI training, configuration, testing, validation, and operation, including a framework for grading data fitness and quality.
Ensure data sources, transformations, exclusions, assumptions, and limitations are documented, and establish traceability across source data, model versions, requirements, testing, outputs, and production use.
Evaluate use of patient, clinical, proprietary, and company data by internal models, external platforms, and vendors.
Establish risk-based testing and assurance strategies, including expectations for requirements, acceptance criteria, test data, test methods, traceability, and objective evidence.
Ensure testing addresses accuracy, reliability, reproducibility, boundary conditions, failure scenarios, bias, human oversight, and real-world performance; determine when statistical testing, expert review, comparative testing, deterministic testing, or production monitoring is appropriate.
Ensure model limitations, prohibited uses, expected failure modes, and required user actions are documented.
Define post-implementation monitoring requirements for model and data drift, performance, output quality, exceptions, human overrides, unintended outcomes, and vendor changes.
Establish monitoring metrics, thresholds, alert criteria, escalation pathways, and triggers for investigation, retraining, additional testing, revalidation, suspension, or retirement.
Set risk-based periodic review requirements and clarify monitoring responsibilities and decision rights.
Assess risks and controls for open, closed, hosted, embedded, and vendor-managed AI models; evaluate vendor evidence and identify additional testing, controls, contractual provisions, or monitoring needed.
Define expectations for vendor notification of model updates, retraining, material changes, incidents, and performance issues, and evaluate whether company data or user inputs may be retained or used to train external models.
Establish documentation and traceability requirements so intended use, risks, requirements, testing, approvals, limitations, monitoring, changes, and decisions are supported by retrievable evidence.
Ensure AI assurance is proportionate to risk and defensible during internal audits and health authority inspections; help technical and business teams explain AI operation, controls, and continued performance.
Identify and address documentation, testing, or control gaps before inspection, and apply lessons from audits, inspections, incidents, and implementation experience to improve the AI Development Lifecycle.
Build AI quality capabilities across R&D and eSystems Quality by developing playbooks, templates, decision tools, examples, and role-based training.
Communicate AI risks and assurance expectations to technical, business, Quality, and executive audiences, and help Quality colleagues apply established GxP risk and control principles to AI.
Develop personal knowledge of GxP regulations, validation, data integrity, and regulated R&D processes through collaboration with experienced colleagues.
Provide product direction for the R&D Quality Intelligence Platform, defining requirements for structured data, common taxonomies, data quality, traceability, and analytics.
Advance the R&D Quality Intelligence Platform from reporting toward Quality intelligence and decision support, enabling identification of emerging risks, recurring issues, control effectiveness, CAPA effectiveness, and areas requiring earlier intervention.
Ensure AI-enabled platform capabilities follow the lifecycle, testing, monitoring, and governance requirements established for other R&D AI solutions.
As a people leader, foster inclusion, develop talent through coaching and feedback, align team goals with organizational objectives, hold the team accountable, and help remove barriers.