Senior Director, Applied AI Solutions

Gilead Sciences•Foster City, CA

About The Position

The Senior Director, Applied AI Solutions, leads the applied AI function within the AI Research Center (ARC), part of Gilead’s Clinical Data Science (CDS) organization in Drug Development. As CDS’s center of excellence for applied AI, this leader is accountable for the technical and product path from a prioritized AI use case defined by CDS functional organizations to a trusted, reusable capability in production — spanning discovery, experimentation, technical validation, first production release, adoption support, and measured value. The ownership model is deliberate. Business ownership of priorities, requirements, funding, process change, and workflow outcomes remains with the CDS and Development functions, including Clinical Data Engineering & Analytics (CDEA) and Statistical Programming. Enterprise platform services, infrastructure, and scaled production operations and support remain with Enterprise IT and Development Systems. This role supplies the AI expertise, solution engineering, and delivery capacity that make both possible, and is measured on how effectively it accelerates outcomes owned by others. The role combines technical depth, product-minded execution, and people leadership. You will define ARC’s applied AI engineering practices — model and prompt lifecycle, evaluation, agent design patterns, solution architecture — and influence the enterprise engineering and architecture standards owned by IT, while remaining close enough to the work to make consequential design decisions and to lead multidisciplinary teams building AI systems for internal CDS and Development users. Priority domains for AI enablement may 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. In each of these domains the owning CDS or Development function leads its own workflow and process transformation agenda; this role provides the AI solutions, methods, and technical expertise that enable it. Applied AI Solutions favors reusable capabilities over isolated proofs of concept. The team builds on Gilead’s approved data, cloud, security, privacy, and responsible-AI foundations and on the enterprise platform services provided by IT, and partners closely with ARC’s Product Management & Experiences (PMx) and AI Acceleration (AIx) functional (scientific and operational) teams, as well as with Development Systems, Enterprise IT, Information Security, Privacy, Legal, Quality, CDEA, Statistical Programming, and business stakeholders. The mandate: turn high-value Clinical Data Science and Development AI problems — as prioritized by their business owners — into trusted, reusable, production-grade AI capabilities that improve how Gilead designs, conducts, analyzes, and learns from clinical development.

Requirements

Bachelor’s degree with 14+ years of relevant experience, Master’s degree with 13+ years, or PhD with 12+ years in computer science, engineering, data science, computational science, bioinformatics, or a related quantitative field.
Meaningful, current hands-on experience. Direct, recent experience personally developing and maintaining AI systems and applications — not solely theoretical knowledge or management oversight. This includes writing and committing code, reviewing pull requests, debugging, and troubleshooting production issues, with the depth and currency needed to make sound technical design decisions, experiment credibly, and stay hands-on as technology evolves.
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 delivery of AI products or reusable capabilities from problem framing, data and technical design decisions, and experimentation through validated release, adoption, and value measurement, working with central platform and IT organizations that own infrastructure and scaled operations.
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 — sufficient to design AI solutions that meet enterprise standards and to partner credibly with IT platform and architecture teams.
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, with the ability to translate between scientific or business needs, product choices, technical design 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, in support of the functions that own those 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, 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

Translate prioritized AI demand into a multi-year applied AI engineering and delivery plan tied to CDS and Development outcomes.
Own the design, build, evaluation, and technical validation of AI services, applications, agents, and shared components through first production release.
Co-design with Enterprise IT and Development Systems from the outset so that solutions are built for scale, then hand over to IT-owned platform and operations for enterprise scaling and sustained run, remaining the accountable AI technical partner after transition.
Establish reference solutions and workflow designs for model gateways, retrieval-augmented generation (RAG), semantic search, knowledge graphs, multimodal AI, tool use, workflow orchestration, human review, and integration with validated enterprise systems.
Guide the responsible use of agentic patterns for bounded, auditable CDS workflows.
Use open interoperability protocols such as MCP and A2A where appropriate to connect AI applications and agents to governed data, tools, and workflows, and ensure authorization, least-privilege access, provenance, action controls, and human escalation are designed in from the start and that agents are registered in the enterprise agent registry.
Define and uphold ARC’s applied AI engineering practices — data contracts, model and prompt lifecycle management, evaluation harnesses, LLMOps/MLOps, automated testing, and reproducible releases — while adopting and contributing to the enterprise standards owned by IT for software engineering, infrastructure as code, CI/CD, and security.
Build for reliability, latency, cost, maintainability, portability, and graceful failure, not demonstrations alone.
Make evaluation a first-class engineering discipline.
Establish offline and online evals, gold-standard datasets, task and outcome metrics, trace-based observability, model and data monitoring, adversarial testing, and incident response.
Partner with Responsible AI enterprise teams and control functions to implement risk-tiered governance, privacy, security, records, auditability, explainability, and GxP or regulatory controls when applicable.
Co-own product discovery and experience design with PMx and support adoption alongside CDS workflow owners.
Operate as an expert partner, not a gatekeeper — augmenting their workflows while they retain ownership of priorities, requirements, process change, and outcomes.
Instrument and report the scientific, operational, quality, and user outcomes AI solutions deliver: cycle-time reduction, improved signal detection, decision quality, reuse, reliability, and sustained utilization.
Build, lead, and develop a high-performing team spanning applied AI, machine learning, and software and data engineering.
Set a high technical bar through technical design reviews, design critiques, coaching, and selective hands-on prototyping.
Create clear career paths, succession plans, and an inclusive culture of scientific rigor, learning, accountability, and responsible experimentation.
Contribute the AI assessment that informs build, buy, and partner decisions taken jointly with business owners and IT; manage strategic AI vendors within that framework; and assess emerging models, frameworks, and protocols through evidence-based benchmarks.
Represent Gilead in selected external collaborations while protecting confidential data, intellectual property, and freedom to operate.