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.
Reporting to the Senior Director, Field AI Strategy & Enablement, the Director, Field AI & GPro Enablement is responsible for driving the delivery, adoption, and continuous evolution of AI-powered field capabilities and business planning solutions for the HIV Prevention field organization. This highly visible individual contributor serves as the business lead for Field AI pre and post call solutions and the Product Manager for GPro, a strategic iOS platform that supports field force planning, execution, and performance optimization.
Working at the intersection of business strategy, technology, and field execution, the role translates business priorities into scalable solutions that improve field effectiveness, enhance customer engagement, and generate measurable commercial impact. Success requires strong business acumen, product management expertise, disciplined execution, and the ability to influence and collaborate effectively with stakeholders across Sales, Sales Analytics, Marketing, IT, Legal, Compliance, Data Science, and external partners.
This individual will define the Therapeutic Area (TA) product roadmaps, establish success metrics, drive adoption, and oversee the successful launch and optimization of innovative capabilities that enable better decision-making, streamline workflows, and support the organization's vision for AI-enabled field performance.
Through leadership of both GPro and AI-powered field capabilities, this role will be instrumental in advancing the organization’s AI transformation and enabling a more intelligent, insight-driven approach to field engagement.
This role is located in Foster City CA on a Hybrid Schedule (T,W,TH in office).
Key Responsibilities:
Transform Pre-Post Call Through AI
- Lead the TA strategy, roadmap, and delivery of AI-powered pre-call and post-call capabilities that enhance field planning, execution, and follow-up.
- Translate business priorities, field needs, and customer insights into scalable AI solutions that improve field effectiveness and customer engagement.
- Continuously optimize AI recommendations and workflows using user feedback, performance analytics, and business outcomes.
Drive Pre-Post Call AI in Partnership with Stakeholders
- Serve as the primary liaison between business stakeholders and technical teams, ensuring solutions meet user, business, and commercial objectives.
- Facilitate prioritization decisions by balancing business value, user impact, technical feasibility, and resource constraints.
- Partner with IT and vendor teams to deliver high-quality solutions against agreed milestones, priorities, and success criteria.
Lead GPro TA Strategy, Adoption, and Optimization
- Serve as the TA business lead for GPro, ensuring alignment with TA and enterprise priorities.
- Partner with Sales, Marketing, Sales Analytics, IT, Commercial Learning & Development, and vendor teams to define requirements and deliver capabilities that improve field effectiveness.
- Lead stakeholder engagement, adoption, training, and change management efforts to maximize business value and user adoption.
- Drive continuous improvement by identifying opportunities to enhance workflows, solve business challenges, and evolve GPro to meet changing business needs.
Drive Field Productivity and Workflow Efficiency
- Embed AI-powered insights and recommendations into field workflows to enhance planning, execution, and decision-making.
- Identify and eliminate process inefficiencies through automation and workflow optimization, increasing productivity and reducing administrative burden.
- Design scalable user experiences that simplify decision-making and enable more effective customer engagement.
Measure Success and Drive Performance
- Establish and manage outcome-based KPIs to evaluate adoption, engagement, performance, and business impact.
- Monitor results, identify performance gaps, and implement corrective actions to improve user adoption and commercial outcomes.
- Drive accountability through regular performance reviews and stakeholder alignment.
Champion Data Quality and Responsible AI
- Ensure AI-generated outputs are trusted by enforcing strong data quality standards, governance, and regulatory requirements.
- Champion responsible AI principles, including fairness, transparency, accountability, and explainability across the product lifecycle.
Basic Qualifications:
- 12+ Years with BS/BA
OR 10+ Years with MS/MA or MBA *
Preferred Qualifications:
- 10+ years of experience and a Bachelor’s degree or 8 years of experience and a Master's degree in IT, business administration, or related fields.
- Agile Product Manager experience preferred
- Extensive experience in Business Analysis, Data & Analytics, Artificial Intelligence, Digital Transformation, or related disciplines.
- Strong business acumen and excellent communication and interpersonal skills.
- Demonstrated communication and teamwork skills; ability to gain consensus and act within a matrix team environment .
- Excellent organizational, planning and project management skills and ability to effectively manage cross-functional projects.
- Ability to drive clear requirements, translate them to functional specifications, and facilitate cross-functional communication with a high standard of excellence.
- End-to-end experience managing the digital product development lifecycle with a proven track record of successfully launching new capabilities and growing existing products.
- Pharmaceutical or healthcare data related experience preferred but not required.
Intellectual curiosity, willingness to learn new skills, and are excited by analytical problems and new challenges. *
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: $210,375.00 - $272,250.00. 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:
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 $210K-$272K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($241K) sits 14% above the category median. Disclosed range: $210K to $272K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Gilead Sciences AI Hiring
Gilead Sciences has 9 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Positions span Parsippany, NJ, Foster City, CA, US, Raleigh, NC, US. Compensation range: $122K - $272K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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