Senior Director, Applied AI Solutions- Clinical Drug Development

Gilead Sciences•Foster City, CA

4d•$243,100 - $314,600

About The Position

The Senior Director, Applied AI Solutions leads the applied AI function within Gilead’s AI Research Center (ARC), part of the Clinical Data Science organization in Drug Development. The role is accountable for the technical and product path that turns AI use cases prioritized by Clinical Data Science and Development business owners into trusted, reusable production capabilities. This path includes discovery, experimentation, technical validation, first production release, adoption support, and measurement of value. Business ownership of priorities, requirements, funding, process change, and workflow outcomes remains with Clinical Data Science and Development functions. Enterprise IT and Development Systems own enterprise platform services, infrastructure, and scaled production operations and support. This role provides AI expertise, solution engineering, and delivery capacity, and is accountable for accelerating outcomes owned by others. The position combines technical depth, product-minded execution, and people leadership. It defines ARC’s applied AI engineering practices and influences enterprise engineering and architecture standards while guiding multidisciplinary teams and remaining involved in consequential design decisions. The role works closely with ARC’s Product Management & Experiences and AI Acceleration teams and partners across Development Systems, Enterprise IT, Information Security, Privacy, Legal, Quality, Clinical Data Engineering & Analytics, Statistical Programming, and business stakeholders. It favors reusable capabilities over isolated proofs of concept and builds on Gilead’s approved data, cloud, security, privacy, responsible-AI foundations, and enterprise platform services. The role leads the design, development, evaluation, and technical validation of AI services, applications, agents, and shared components through first production release, then partners with IT for transition to enterprise scaling and sustained operations. Potential application domains include clinical data review and insights, study design and operations, biomarker and real-world data workflows, safety and risk detection, regulatory evidence generation, and scientific knowledge access. The role also establishes practices for model and prompt lifecycle management, evaluation, agent design, solution architecture, interoperability, observability, responsible AI, adoption, and value measurement.

Requirements

Bachelor’s degree with 14 or more years of relevant experience, master’s degree with 13 or more years, or PhD with 12 or more years in computer science, engineering, data science, computational science, bioinformatics, or a related quantitative field.
Meaningful, current hands-on experience personally developing and maintaining AI systems and applications, including writing and committing code, reviewing pull requests, debugging, and troubleshooting production issues.
Substantial experience leading applied AI, ML, or data engineering organizations while remaining technically hands-on, and personally delivering business-critical systems in complex enterprises.
Demonstrated experience delivering AI products or reusable capabilities from problem framing, data and technical design, and experimentation through validated release, adoption, and value measurement, in collaboration with central platform and IT organizations.
Deep working knowledge of modern AI system design, including foundation models and LLMs, RAG and semantic retrieval, agent and tool orchestration, model selection and routing, structured outputs, evaluation, guardrails, and human-in-the-loop patterns.
Strong software and platform engineering judgment across APIs, distributed systems, cloud services, data design, security, CI/CD, MLOps/LLMOps, observability, and production reliability.
Experience implementing AI in environments with rigorous privacy, security, quality, compliance, validation, or regulatory expectations.
Track record of hiring, developing, and retaining multidisciplinary technical teams and coaching senior engineers and technical leaders.
Executive-level communication and influence, including the ability to translate among scientific or business needs, product choices, technical tradeoffs, risk, and investment.

Nice To Haves

Experience in pharmaceutical, biotechnology, healthcare, or another regulated scientific domain, with familiarity across clinical development data and workflows.
Experience applying AI to clinical data review, study design or operations, safety, biomarkers, real-world evidence, regulatory evidence, or scientific knowledge workflows.
Practical experience designing or governing MCP servers and clients, A2A-enabled systems, or comparable open, vendor-neutral interoperability patterns.
Experience with multimodal biomedical data, ontologies, knowledge graphs, privacy-preserving methods, causal inference, or statistical and ML methods used in clinical research.
Evidence of external technical leadership through open-source contributions, patents, peer-reviewed work, conference presentations, standards participation, or substantive industry collaborations.

Responsibilities

Develop a multi-year applied AI engineering and delivery plan with CDS and Development business owners, Enterprise IT, and ARC leadership, translating prioritized demand into plans tied to business outcomes.
Assess feasibility, solution options, effort, data readiness, reuse potential, and technical risk; recommend sequencing and provide evidence for decisions to scale, redesign, or stop initiatives.
Design, build, evaluate, and technically validate AI services, applications, agents, and shared components through their first production release.
Co-design solutions with Enterprise IT and Development Systems so they are built for scale, and remain the accountable AI technical partner after transition to IT-owned platforms and operations.
Establish reference solutions and workflow designs for model gateways, RAG, semantic search, knowledge graphs, multimodal AI, tool use, workflow orchestration, human review, and integration with validated enterprise systems.
Guide responsible use of agentic patterns for bounded, auditable CDS workflows, including appropriate use of MCP, A2A, or other open interoperability protocols.
Ensure authorization, least-privilege access, provenance, action controls, human escalation, and enterprise agent registry registration are incorporated into agent solutions.
Define and uphold applied AI engineering practices, including data contracts, model and prompt lifecycle management, evaluation harnesses, LLMOps/MLOps, automated testing, and reproducible releases.
Adopt and contribute to Enterprise IT standards for software engineering, infrastructure as code, CI/CD, and security; build for reliability, latency, cost, maintainability, portability, and graceful failure.
Establish offline and online evaluations, gold-standard datasets, task and outcome metrics, trace-based observability, model and data monitoring, adversarial testing, and incident response.
Partner with Responsible AI teams and control functions to implement risk-tiered governance, privacy, security, records, auditability, explainability, and applicable GxP or regulatory controls.
Co-own product discovery and experience design with PMx, and support adoption alongside CDS workflow owners while respecting their ownership of priorities, requirements, process change, and outcomes.
Instrument and report scientific, operational, quality, and user outcomes against business-defined baselines, including cycle-time reduction, signal detection, decision quality, reuse, reliability, and sustained utilization.
Build, lead, and develop a high-performing multidisciplinary team spanning applied AI, machine learning, software engineering, and data engineering.
Set technical standards through design reviews, design critiques, coaching, and selective hands-on prototyping; create career paths and succession plans and foster an inclusive culture of rigor, learning, accountability, and responsible experimentation.
Provide AI assessments to inform build, buy, and partner decisions made jointly with business owners and IT; manage strategic AI vendors within that framework and assess emerging models, frameworks, and protocols using evidence-based benchmarks.
Represent Gilead in selected external collaborations while protecting confidential data, intellectual property, and freedom to operate.