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, technical design tradeoffs, 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.
• 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.

KEY SKILLS FOR SUCCESS

• Applied AI judgment. Selects fit-for-purpose methods and knows when deterministic software, analytics, classical ML, generative AI, or an agentic approach is the right solution — and advises business and IT partners so their investment decisions are well informed.
• Technical credibility. Engages deeply in AI solution design and engineering decisions while enabling senior technical leaders to own execution, and aligns with IT-owned architecture and platform decisions for scaled systems.
• Scientific partnership. Builds trust with clinical and data science experts; respects evidence, uncertainty, provenance, and reproducibility.
• Product and execution focus. Starts from the workflows and measurable outcomes defined by business owners, delivers iteratively, and prepares solutions for scaling with IT only when the evidence supports it.
• Responsible innovation. Moves with urgency while treating privacy, security, patient impact, compliance, and reliability as design requirements.
• Enterprise partnership. Works across business functions, IT, Development Systems, and control functions to convert ambiguity into clear interfaces, shared decisions, and explicit accountability, respecting where each decision rightly sits.

The salary range for this position is: $243,100.00 - $314,600.00. Gilead considers a variety of factors when determining base compensation, including experience, qualifications, and geographic location. These considerations mean actual compensation will vary. This position may also be eligible for a discretionary annual bonus, discretionary stock-based long-term incentives (eligibility may vary based on role), paid time off, and a benefits package. Benefits include company-sponsored medical, dental, vision, and life insurance plans*.

https://www.gilead.com/careers/compensation-benefits-and-wellbeing

• Eligible employees may participate in benefit plans, subject to the terms and conditions of the applicable plans.

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