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
At Gilead our pursuit of a healthier world for all people has yielded a cure for hepatitis C, revolutionary improvements in HIV
treatment and prevention as well as advancements in therapies for viral and inflammatory diseases and certain cancers.
We set and achieve bold ambitions in our fight against the world’s most devastating diseases, united in our commitment to
confronting the largest public health challenges of our day and improving the lives of patients for generations to come.
As a Director, Product Management, AI Content Creation, at Gilead you will
act as the Product Manager for AI\-powered content creation platforms and reporting to the Senior Director – Head of Product Management – Content Tech \& AI, this role will lead the ideation, definition, execution, and scaling of AI\-driven content generation capabilities across marketing.
This is a delivery\-focused leadership role, responsible for building and scaling AI\-enabled content creation ecosystems that materially transform how marketing content is produced, localized, and personalized at scale.
The role will focus on:
- Reimagining content creation using AI, across both net new content generation and derivative content production
- Reducing content production timelines and accelerating speed\-to\-market
- Optimizing cost structures, reducing reliance on external agencies.
- Enabling personalization at scale, progressing towards N\=1 content delivery
The Director will work in close partnership with IT, Global Commercial Excellence, Global Strategic Marketing and affiliate marketing teams to ensure AI content creation solutions are fully operationalized and delivering measurable business value.
Key Responsibilities
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### Transform content creation through AI:
- Own the design and delivery of AI\-enabled content creation platforms, spanning:
+ Net new content generation (e.g. campaigns, messaging frameworks, modular assets)
+ Derivative content creation (e.g. localization, adaptation, channel\-specific variations)
- Collaborate with Marketing teams at local and global level along with Marketing Excellence to define how AI reshapes the end\-to\-end content lifecycle, from briefing through generation to campaign execution.
- Drive standardization of AI\-enabled workflows across content creation and reuse, aligned to enterprise approaches to content operations.
- Enable rapid iteration and testing of content, improving responsiveness to marketing needs.
### Drive AI Use Cases and Platform Delivery in Partnership with IT
- Partner with IT to translate business priorities into effective proof of concepts, pilots and ensure solutions are iterated and refined to be scalable AI content creation platforms.
- Define and own a roadmap of AI content creation capabilities, including:
+ Generative content engines
+ Content authoring and assembly
+ Content personalization systems
+ Tagging
- Ensure alignment with data, security, and compliance requirements in regulated environments
- Establish clear quarterly milestones, priorities, and deliverables to drive execution
- Act as the single accountable business owner for AI\-powered content creation use cases.
### Drive Adoption and Scaling with Commercial Excellence
- Partner with Global Commercial Excellence teams to embed AI content creation into real\-world marketing workflows
- Develop and deploy:
+ Adoption and rollout strategies across affiliates
+ Training, playbooks, and enablement frameworks
+ Governance models and best practices for AI\-generated content
- Ensure solutions meet local affiliate needs, including localization and regulatory considerations
- Establish feedback loops to continuously improve tools, models, and workflows
### Drive Content Economics and Efficiency Gains
- Working with colleagues across Commercial Excellence, redesign the content creation operating model to:
+ Reduce dependency on external agencies and production vendors
+ Shift investment towards AI\-enabled internal capabilities
- Shorten the content creation lifecycle, reducing production time and increasing throughput
- Enable scalable personalization, supporting progression towards audience\-level and individual\-level targeting (N\=1\)
- Ensure alignment between cost efficiency, quality, and brand consistency
### Define, Track, and Deliver KPIs
- Own definition and tracking of clear, outcome\-based KPIs, including:
- Reduction in content production cycle time
- Increase in content throughput and reuse
- Reduction in cost per asset
- Adoption and utilization of AI content creation platforms
- Increase in personalized content deployment at scale
- Ensure all initiatives are tied to measurable business outcomes and value realization
Basic Qualifications:
-------------------------
Bachelor's Degree and Eight Years’ Experience
OR
Masters' Degree and Six Years’ Experience
OR
Ph.D.
Qualifications
------------------
- Bachelor’s Degree with at least 8 years’ experience in Marketing, commercial strategy, and/or technology\-enabled transformation; or 10 years with a Master’s Degree (8 years with a Doctoral Degree)
- Strong expertise in Brand or Marketing and some experience in utilizing or building AI generated content would be preferred
- Proven experience in product management and delivery of scalable platforms, ideally in regulated environments
- Experience in pharma/biotech marketing and content workflows preferred
- Deep understanding of content lifecycle, modular content, and localization strategies
- Strong capability in business case development and value articulation
- Expertise in agile delivery, product management, and design thinking
- Excellent stakeholder management and ability to operate in global, cross\-functional environments
- Strong communication skills with ability to drive adoption and behavioral change at scale
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: $226,185\.00 \- $292,710\.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:
-------------------------------------------------
Please apply via the Internal Career Opportunities portal in Workday.
Salary Context
This $226K-$292K 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 ($259K) sits 19% above the category median. Disclosed range: $226K to $292K.
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.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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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