Interested in this AI/ML Engineer role at Gilead Sciences?
Apply Now →About This Role
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.
We believe every employee deserves a great leader. People Leaders are the cornerstone to the employee experience at Gilead and Kite. As a people leader now or in the future, you are the key driver in evolving our culture and creating an environment where every employee feels included, developed and empowered to fulfil their aspirations. Join Gilead and help create possible, together.
Job Description
Senior Director, Applied AI, Design \& Innovation
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Global Development \| Clinical Operations
This position is located in Foster City, California (Hybrid), or Remote and may require domestic or international travel as required (valid travel documents must be obtainable).
Position Overview
Design \& Innovation is responsible for advancing the clinical trial experience through evidence\-based decision\-making, human\-centered design, advanced analytics, digital capability development, and AI\-enabled innovation. These approaches transform how programs and studies are designed, planned, and operationalized. The function supports patient\-centric clinical trial design that provide enable more efficient execution through lean designs and less burden for patients and sites. The team has developed an industry leading set of capabilities that enable the organization to reliably predict the enrollment of the portfolio with a high degree of accuracy through modern statistical approaches, and we are developing the next generation platform to further enable even greater efficiency in the design and planning process capabilities.
The Sr. Director, Applied AI will be responsible for leading the cross\-functional design delivery team and is ultimately accountable for the overall operational strategy and study/site feasibility processes, systems, and people that support the entire portfolio across our three therapeutic areas of Virology, Oncology, and Inflammation. This role is also responsible for project leadership of Development level AI initiatives that aim to continue the development of our AI capabilities within the Design \& Innovation scope and beyond to also include priority deliverables for Clinical Operations. This leader will report to and partner closely with the Executive Director to shape strategy, operating frameworks, and delivery models across a portfolio of initiatives within defined budgets and timelines. The role will lead the identification, development, integration, scaling, and value realization of innovative processes, technologies, data assets, and AI\-enabled capabilities within Gilead’s clinical development model.
This leader will serve as a strategic integrator across Clinical Operations, Clinical Development, Clinical Data Science, Regulatory, Medical Affairs, Commercial, and IT translating complex operational and clinical needs into coherent, realistic, and trackable innovation and AI strategies. The Sr. Director will help shape how innovation and AI capabilities are prioritized, governed, adopted, and scaled across Clinical Operations, with a focus on measurable business, operational, quality, and patient impact.
Core Responsibilities
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### Enterprise AI Strategy \& Capability Leadership
- Define and own the multi\-year Applied AI roadmap for Clinical Development \& Operations, aligned to Gilead's enterprise AI strategy and portfolio priorities.
- Build, scale, and lead a center\-of\-excellence team spanning data science, ML engineering, GenAI product development, and AI delivery management.
- Establish governance, prioritization frameworks, KPIs, and ROI/value\-realization models that quantify AI's impact at the trial, program, and portfolio level.
- Serve as the executive voice of Applied AI to Senior Leadership, R\&D governance forums, and external partners.
### Clinical Trial Forecasting \& Enrollment Modeling
- Lead development of predictive enrollment models that improve forecasting accuracy at the country, site, and study level.
- Build real\-time enrollment performance monitoring capabilities to enable earlier, more proactive interventions.
- Partner with Clinical Operations and Program Management to integrate forecasting outputs into operational planning, resource demand, and budget cycles.
### Trial Design Optimization \& Study Feasibility
- Drive AI/ML\-enabled approaches to protocol design optimization , including eligibility criteria simulation, endpoint selection, and protocol complexity scoring.
- Develop site selection and feasibility models leveraging internal data, external RWD/RWE, and historical performance signals.
- Partner with Biostatistics and Clinical Development teams to embed AI insights into protocol authoring, design tollgates, and study start\-up decisions.
- Establish, refine, and drive a multi\-year AI Innovation strategy aligned to Development and Clinical Operations priorities, operating model evolution, and enterprise AI direction.
- Lead functional deliverables across Design \& Innovation, design delivery, early and late\-stage innovation capability development by providing overall operational excellence.
- Lead the Clinical Intelligence team to support a variety of strategic queries and operational benchmarking for the executive Clinical Operations and Development leadership teams.
- Shape and manage a portfolio of innovation and AI initiatives aligned to Clinical Operations strategy, business priorities, and value realization goals.
- Identify, prioritize, and advance high\-value innovation and AI use cases that improve clinical program and study design, operational planning, decision support, workflow efficiency, and stakeholder insight generation.
### Real\-World Data \& Evidence Integration
- Champion integration of RWD/RWE into trial design, feasibility, and patient identification workflows.
- Build partnerships with external data platforms and evaluate AI start\-ups/vendors to access cutting\-edge methodologies.
- Recommend strategic partnerships, in\-licensing, or build\-vs\-buy decisions to accelerate capability development.
### Cross\-Functional Delivery \& Change Management
- Partner with Clinical Operations, Clinical Data Sciences, Biostatistics, Safety, Regulatory, and Digital to ensure AI solutions are designed for adoption, not just deployment.
- Lead change management, training, and enablement to drive scaled adoption across study teams.
- Represent Gilead externally with industry consortia, regulators (e.g., FDA), and strategic partners on Applied AI in clinical research.
Additional Responsibilities
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- Translate clinical and operational needs into actionable innovation and AI strategies, delivery plans, business cases, and measurable outcomes.
- Partner with Clinical Development, Clinical Operations, Clinical Data Science (AI Research Center, Clinical \& Development Systems, Biostatistics), Regulatory, Portfolio Strategy \& Analytics, Medical Affairs, IT, Digital, and other stakeholders to translate innovation priorities into scalable capabilities and operating improvements.
- Support governance and decision\-making processes by preparing strategic recommendations, facilitating prioritization discussions, and communicating proposals, trade\-offs, and decision options to senior stakeholder audiences.
- Lead the development and governance of frameworks for identifying, testing, validating, integrating, scaling, and monitoring new capabilities across process, technology, data, analytics, and AI.
- Define, develop, and track KPIs, value measures, and ROI frameworks to quantify the impact of innovation and AI capabilities at the study, program, and functional levels.
- Establish governance standards for business cases, value tracking, adoption measures, scale\-up criteria, and stop, pivot, or expand decisions across the innovation portfolio.
- Help define fit\-for\-purpose operating, data, and platform requirements needed to support priority innovation and AI use cases across clinical development and operations.
- Partner with Digital, IT, data science, and enterprise technology teams to support scalable architecture, integration, and deployment approaches for new capabilities.
- Drive responsible implementation of AI\-enabled capabilities with appropriate rigor around business value, adoption, usability, privacy, transparency, bias and risk management, compliance, and human\-in\-the\-loop oversight.
- Shape and operationalize an innovation portfolio that balances near\-term business impact with longer\-term capability building.
- Strategically identify emerging external capabilities across start\-ups, mid\-sized organizations, and enterprise partners to accelerate innovation and create meaningful gains in speed, quality, and value.
- Serve as a functional leader and liaison to key Development areas, aligning enterprise needs with practical capability development and deployment.
- Act as an integrator across business, technology, and data teams to ensure alignment, visibility, accountability, and successful scale\-up of priority capabilities.
- Inform and influence corporate and regulatory policies and external guidance related to clinical development innovation, including patient\-centricity, diversity, accessibility, decentralization, digital health, real\-world evidence, and responsible use of AI.
- Lead and support the creation of governance documents, procedural documentation, white papers, abstracts, presentations, manuscripts, and other thought leadership materials.
- Promote awareness, adoption, and change management for innovation\- and AI\-enabled processes across the organization through playbooks, communications, training, and ongoing engagement.
- Build organizational capability in innovation, digital, and AI\-enabled ways of working by helping teams adopt new tools, practices, and decision models at scale.
- Provide functional financial oversight, including latest estimates, resource planning, and communication across clinical and finance stakeholders.
- Lead recruitment, development, and performance management for a team of full\-time employees, contractors, and external partners.
Knowledge, Experience \& Skills
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- Experience leading strategic and complex scientific, operational, and analytical initiatives that support program and study design including, but not limited to the execution of Bayesian inference models including Poisson\-gamma, predictive enrollment scenario modeling, real\-time enrollment performance monitoring
- Demonstrated experience shaping and advancing AI, analytics, digital, or business transformation initiatives in a regulated environment
- Strong understanding of how to evaluate AI and advanced analytics use cases for business impact, feasibility, scalability, adoption, and operational fit
- Experience translating business, clinical, and operational needs into scalable technology, analytics, or AI\-enabled solutions
- Experience defining KPI, value, and ROI frameworks for innovation, digital, or AI initiatives
- Experience leading or supporting portfolio governance, prioritization, and value realization for complex cross\-functional initiatives
- Experience supporting adoption of digital or AI\-enabled capabilities through change management, stakeholder engagement, and scalable operating models
- Understanding of data, platform, and integration considerations required to deploy digital and AI\-enabled capabilities in a complex enterprise environment
- High degree of customer focus and collaboration in a cross\-functional team environment
- Demonstrated planning, organizational, and large\-scale budget management skills
- Inspirational leadership style with the ability to attract talent, build high\-performing teams, and influence across a complex matrix
- Strong interpersonal skills, including the ability to lead, coach, and mentor staff and align multifunctional teams around informed risk\-taking and data\-driven decision\-making
- Advanced capabilities in change management and business transformation
- Experience leading high\-complexity cross\-functional initiatives in life sciences, including strategic planning, governance, team management, and organizational alignment
- Demonstrated ability to solve complex problems requiring strong judgment related to domestic and global regulations, guidelines, investigator interactions, and timelines
- Excellent verbal and written communication skills, including experience presenting scientific, operational, and strategic concepts to executive audiences
- Experience applying GDPR, HIPAA, information security, privacy, and vendor assessment requirements in the evaluation and implementation of new capabilities and technologies
Education and Experience Requirements:
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- Bachelor's degree with 14\+ years of relevant scientific discipline such as Life Sciences, Information Technology, Computer Science, Pharmacy, Data Science, Business or related field OR 12\+ years of relevant experience with an advanced degree or higher in Life Sciences, Information Technology, Computer Science, Medicine, Pharmacy, Data Science, Sociology, Public Health, Industrial Design, Marketing, Business, or related field.
- Extensive working knowledge of drug development and global clinical trial conduct, including recent advances in data science, digital solutions, regulatory science, and AI\-enabled technologies.
- Prior experience leading organizational transformation and designing or executing large global programs and studies in program or clinical project management/clinical operations.
- Extensive line management experience, which may include managing other people leaders, and a strong track record of hiring, managing, and developing diverse top talent.
- Experience leading AI initiatives across multiple therapeutic areas (Oncology, Virology, Inflammation a plus)Familiarity with MLOps, CI/CD, and AI Platform Architecture in Regulated (GxP) environmentsPrior engagement with FDA, EMA, or industry consortia (e.g., TransCelerate, PhRMA, CDISC) on AI/digital innovation/clinical innovation.
- Experience with agentic AI architectures, retrieval\-augmented generation, and human\-in\-the\-loop design patterns.
- Track record of published thought leadership or external speaking on AI in clinical research.
What You'll Bring
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Strategic clarity — you can translate ambiguous scientific and operational needs into a prioritized, measurable AI roadmap and support a team of strategic thinkers for enabling critical portfolio decision making and planning
Builder's mindset — you've stood up teams, capabilities, and platforms from scratch and scaled them
Bias for outcomes — you measure success in trial acceleration, cost reduction, and adoption, not models shipped
Collaborative leadership — you build trust with clinical, scientific, technical, and regulatory partners alike
Patient focus — you understand that every day saved on a trial timeline is a day earlier patients access therapies
Why This Role Matters
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A 12\-month reduction in clinical development timelines can deliver hundreds of millions of dollars in net present value across a sponsor's portfolio — and immeasurable benefit to patients waiting for therapies.
This role is Gilead's opportunity to set the pace for the industry: combining classical AI/ML and Generative AI with new ways of working to make our trials patient\-centered, intelligent, and lean – making them reliably fast for our stakeholders.
People Leader Accountabilities :
•Create Inclusion \- knowing the business value of diverse teams, modeling inclusion, and embedding the value of diversity in the way they manage their teams.
•Develop Talent \- understand the skills, experience, aspirations and potential of their employees and coach them on current performance and future potential. They ensure employees are receiving the feedback and insight needed to grow, develop and realize their purpose.
•Empower Teams \- connect the team to the organization by aligning goals, purpose, and organizational objectives, and holding them to account. They provide the support needed to remove barriers and connect their team to the broader ecosystem.
The salary range for this position is:
Other US Locations: $221,000\.00 \- $286,000\.00\.
Bay Area: $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\*.
For additional benefits information, visit:
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.
For jobs in the United States:
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Gilead Sciences Inc. is committed to providing equal employment opportunities to all employees and applicants for employment, and is dedicated to fostering an inclusive work environment comprised of diverse perspectives, backgrounds, and experiences. Employment decisions regarding recruitment and selection will be made without discrimination based on race, color, religion, national origin, sex , age, sexual orientation, physical or mental disability, genetic information or characteristic, gender identity and expression, veteran status, or other non\-job related characteristics or other prohibited grounds specified in applicable federal, state and local laws. In order to ensure reasonable accommodation for individuals protected by Section 503 of the Rehabilitation Act of 1973, the Vietnam Era Veterans' Readjustment Act of 1974, and Title I of the Americans with Disabilities Act of 1990, applicants who require accommodation in the job application process may contact [email protected] for assistance.
For more information about equal employment opportunity protections, please view the 'Know Your Rights' poster.
NOTICE: EMPLOYEE POLYGRAPH PROTECTION ACT
YOUR RIGHTS UNDER THE FAMILY AND MEDICAL LEAVE ACT
Gilead Sciences will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, (c) consistent with the legal duty to furnish information; or (d) otherwise protected by law.
Our environment respects individual differences and recognizes each employee as an integral member of our company. Our workforce reflects these values and celebrates the individuals who make up our growing team.
Gilead provides a work environment free of harassment and prohibited conduct. We promote and support individual differences and diversity of thoughts and opinion.
For Current Gilead Employees and Contractors:
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Please apply via the Internal Career Opportunities portal in Workday.
Salary Context
This $221K-$314K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Gilead Sciences, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($267K) sits 22% above the category median. Disclosed range: $221K to $314K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Gilead Sciences AI Hiring
Gilead Sciences has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Foster City, CA, US, Remote, US. Compensation range: $292K - $314K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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