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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
Job Description
This ML Platform Engineer will have the unique opportunity to apply cutting\-edge data and AI technologies to one of the most meaningful challenges in healthcare: ensuring the quality of medicines that improve and save lives. As a pivotal member of R\&D Quality, this role will help transform how quality insights are generated, scaled, and acted upon across Gilead’s drug development and clinical research programs. Through the operationalization of machine learning models, data pipelines, and advanced analytics platforms, the successful candidate will enable more proactive quality oversight, smarter decision\-making, and continuous improvement, ultimately supporting Gilead’s mission to deliver life\-changing therapies to patients worldwide.
The ML Platform Engineer will partner with the Quality Analytics \& Insights team, a small, high\-impact group responsible for advancing data science, analytics, and AI capabilities across R\&D Quality. This role will build and maintain the ML and data infrastructure that supports Quality Performance and Quality Health models focused on signal detection, risk analytics, early identification of emerging issues, mitigation strategies, and continuous improvement. Working closely with data scientists, the engineer will operationalize models through robust data pipelines, cloud infrastructure, monitoring, automation, and MLOps practices, transforming analytical prototypes into scalable, production\-ready solutions. The role will collaborate directly with Quality teams, IT, and global delivery teams to support key Quality System elements and programs, including Audit, Deviation, CAPA, Risk Management, Escalation/Serious Breach, and Quality Analytics/Data Science, while helping define the technology roadmap for next\-generation analytics, automation, and AI capabilities across the organization.
Primary Responsibilities
ML \& Data Engineering
- Technical Ownership: Operate as a self\-directed contributor who scopes, plans, and drives initiatives end\-to\-end — translating ambiguous Quality problems into technical solutions, making sound architectural trade\-offs, and delivering production outcomes with minimal oversight.
- Infrastructure \& Environment Automation: Independently provision and manage cloud infrastructure using infrastructure\-as\-code and containerization, standing up reproducible, scalable environments for training, serving, and experimentation with minimal reliance on external teams.
- Model Lifecycle Management: Develop and maintain pipelines to transition models from experimentation to production, including packaging, CI/CD, automated testing, and deployment. Support model serving for Quality use cases such as signal detection, risk analytics, and Quality Performance/Quality Health models.
- Data Pipeline Development: Design robust batch and streaming data workflows; integrate, define, and manage feature sets, lineage, and reuse across QMS data sources (e.g., Audit, Deviation, CAPA, Risk Management).
- Data Orchestration: Author and schedule reliable, observable workflows using orchestration tools and distributed processing, ensuring dependencies, retries, and SLAs are handled without manual intervention.
- Production Operations \& Monitoring: Ensure the reliability and scalability of data pipelines; implement effective logging, tracing, and alerting. Establish monitoring for model performance, data drift, bias, and service health, paying particular attention to data quality across QMS data feeds, where low\-frequency quality signals amplify the impact of anomalies.
AI \& Agent Systems Support
- Workflow Support: Collaborate with data scientists and Quality stakeholders to explore how parts of complex quality workflows (e.g., audit preparation, deviation triage, CAPA trending) can be supported by AI\-assisted or agent\-based approaches, while keeping clear boundaries between automated execution and human data science judgment.
- Prompt \& Instruction Design: Help design and maintain prompt and instruction patterns, including context and memory handling, that translate Quality analytics requirements into clear, well\-scoped directives with defined acceptance criteria.
- Efficiency \& Optimization: Where AI tooling is used, apply sensible practices to manage context usage and cost, balancing capability with available budget.
Collaboration \& Enablement
- Cross\-functional Partnership: Work closely with data scientists, Quality analysts, and stakeholders across R\&D Quality programs (e.g., Audit, Deviation, CAPA, Risk Management, Escalation/Serious Breach). Provide frameworks, templates, and guardrails that accelerate analytics delivery.
- Testing \& Validation: Demonstrate a strong focus on testing by setting up frameworks for both traditional ML models and AI\-generated code. Design validation pipelines with automated quality gates, including type checking, linting, integration tests, and contract tests.
- Documentation \& Release Management: Develop clear, detailed guides, operational playbooks, and user instructions. Coordinate releases with IT and the global team; maintain runbooks, rollback strategies, and change tickets.
- Security \& Compliance: Apply security, access\-control, and data\-governance best practices across pipelines and infrastructure, ensuring solutions meet the expectations of a validated, GxP\-regulated environment.
Innovation \& Technical Strategy
- Technology Evaluation \& Roadmap Input: Evaluate emerging ML, data, and AI tooling; prototype promising approaches and recommend adoption, contributing to the technical roadmap for next\-generation Quality analytics and automation.
- Guardrails \& Assurance: Define evaluation criteria, test sets, and guardrails for AI\-assisted and agent\-based components, ensuring outputs are accurate, traceable, and appropriate for a regulated Quality environment.
Tech Stack
Basic
- Programming \& scripting: Python and SQL; scripting with Python, Bash, or PowerShell.
- Source control: Git and source control management.
- CI/CD \& release management: Working knowledge of CI/CD tools and release management (e.g., GitHub Actions).
- Cloud platforms: Hands\-on experience with a major cloud provider (AWS or Azure).
- Containers: Container technologies (Docker; Kubernetes).
- Data \& ML platform: Databricks.
- Core ML understanding: Understanding of model evaluation and scoring, including avoidance of model bias.
Preferred
- Cloud infrastructure / infrastructure\-as\-code: Terraform; broader cloud engineering experience (AWS preferred).
- AI/ML packages: Experience with common AI/ML libraries such as scikit\-learn, PyTorch, TensorFlow, and XGBoost.
- Monitoring \& logging: Datadog, Splunk, CloudWatch, or Prometheus.
- Infrastructure concepts: Understanding of networking, security, and infrastructure fundamentals.
Basic Qualifications:
Bachelor's Degree and Eight Years' Experience
OR
Masters' Degree and Six Years' Experience
OR
PhD / PharmD
Preferred Qualifications:
- Degree in computer science, computer engineering, information systems, or a related discipline with relevant experience in ML engineering, data engineering, or ML operations
- Significant hands\-on experience operationalizing data/ML solutions end\-to\-end, including data engineering, pipeline development, deployment, and production monitoring.
- Strong programming skills in key languages such as Python, SQL, Go, and TypeScript, with proven ability to manipulate large and complex datasets using distributed computing technologies.
- Familiarity with AWS cloud services.
- Strong troubleshooting and problem\-solving skills.
- Excellent verbal and written communication skills, with the ability to present complex findings to both technical and non\-technical audiences and a strong orientation toward teamwork in a fast\-paced, regulated environment.
- Experience building, packaging, and maintaining machine learning models and libraries in production.
- Experience with CI/CD, infrastructure\-as\-code, and cloud\-based ML platforms.
- Proficiency with Databricks distributed processing (Spark), data orchestration, and similar data and BI technologies.
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 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: $157,590\.00 \- $203,940\.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 $157K-$203K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 Required
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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($180K) sits 16% below the category median. Disclosed range: $157K to $203K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Gilead Sciences AI Hiring
Gilead Sciences has 5 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Foster City, CA, US, Raleigh, NC, US. Compensation range: $189K - $272K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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