Senior Director, Applied AI Solutions- Clinical Drug Development

Gilead Sciences Inc.; Foster City (CA); On-site; USD 350,000 - 700,000; Full time

Job summary

Gilead Sciences Inc. in Foster City, CA, seeks a Senior Director of Applied AI Solutions to lead the AI Research Center's CDS-aligned efforts.

You will own the end-to-end AI engineering roadmap, define scalable AI services, and partner with Enterprise IT for reliable, auditable deployments. The role requires deep hands-on AI experience, executive leadership, and the ability to translate business needs into reusable AI capabilities across CDS and Development.

Qualifications

• Bachelor’s degree or higher in a quantitative field.
• Proven hands-on delivery of AI/ML systems in a production setting.
• Experience leading multidisciplinary AI/data teams in large enterprises.

Responsibilities

• Lead design, build, and delivery of AI services and components from 0→1.
• Co-design with IT to ensure scalable production and operations handover.
• Establish governance, risk, and compliance for AI workflows.
• Drive evaluation, observability, and responsible AI practices.
• Co-own product discovery and adoption with business leaders.
• Build and mentor a high-performing applied AI/ML team.

Skills

Hands-on AI systems

Executive leadership

ML/AI engineering

LLMs knowledge

Cloud & CI/CD

Communication

Regulatory awareness

Education

Bachelor’s degree in CS/Engineering/Data Science

Master’s degree in related field

PhD in related field

Tools

Python

TensorFlow/PyTorch

SQL

ML platforms

Job description

At Gilead, we’re creating a healthier world for all people. For more than 35 years, we’ve tackled diseases such as HIV, viral hepatitis, COVID-19 and cancer – working relentlessly to develop therapies that help improve lives and to ensure access to these therapies across the globe. We continue to fight against the world’s biggest health challenges, and our mission requires collaboration, determination and a relentless drive to make a difference.

Every member of Gilead’s team plays a critical role in the discovery and development of life-changing scientific innovations. Our employees are our greatest asset as we work to achieve our bold ambitions, and we’re looking for the next wave of passionate and ambitious people ready to make a direct impact.

Job Description

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.

Key Responsibilities

As the Senior Director of Applied AI Solutions, you will:

• Applied AI engineering roadmap. In partnership with CDS and Development business owners, Enterprise IT, and ARC leadership, translate prioritized AI demand into a multi-year applied AI engineering and delivery plan tied to CDS and Development outcomes. Business owners decide what to pursue and why; you bring the AI expertise that informs those decisions — feasibility, solution options, effort, data readiness, reuse potential, and technical risk — recommend sequencing, and supply the evidence that allows owners and governance forums to decide whether to scale, redesign, or stop an initiative. Anchor on the core, high-value Development AI priorities while also enabling CDS priorities beyond DevAI and IT‑funded use cases; for those outside the enterprise‑funded portfolio, CDS functional leaders hold business ownership of the roadmap and requirements, and this role builds agents and solutions directly or guides partners to evaluate, select, and integrate the right third‑party capabilities.
• AI solution design and delivery (0→1). 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.
• Agentic systems and interoperability. Guide the responsible use of agentic patterns for bounded, auditable CDS workflows. In partnership with Enterprise IT, Information Security, and Privacy, 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.
• AI engineering excellence. 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.
• Evaluation, observability, and responsible AI. 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.
• Adoption and value realization. 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. Working from baselines the business defines, 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.
• Team and technical leadership. 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.
• Ecosystem stewardship. 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.

WHAT SUCCESS LOOKS LIKE

A focused CDS and Development AI portfolio with transparent priorities set by business owners, accountable delivery owners, measurable outcomes, and fewer disconnected pilots.

Reusable AI capabilities built on the enterprise platform, and integration patterns co‑designed with IT that shorten the path from a validated use case to a supported production service.

Several high‑value CDS and Development workflows operating in production — delivered with ARC and transitioned cleanly to IT‑supported operations — with appropriate human oversight, documented controls, reliable performance, and sustained user adoption.

An evaluation and observability system that makes quality, safety, cost, latency, data provenance, and business impact visible throughout the AI lifecycle.

A recognized center of excellence for applied AI that raises AI capability across CDS and the broader Development organization and is trusted by scientific, clinical, technology, and governance partners as the AI expert partner of choice.

Business owners in CDS and Development report that ARC accelerated their prioritized outcomes without displacing their accountability for them.

REQUIRED QUALIFICATIONS

• 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, risk, and investment.

PREFERRED QUALIFICATIONS

• 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.