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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
The AI \& MLOps Lead for Kite IT Sales \& Digital is an incredible opportunity within the IT Data \& AI CoE team to support the transformation of AI/ML for Kite’s Commercial line of business. This role focuses on designing, developing, and deploying data science solutions that drive impact for patients. It operates at the intersection of traditional ML engineering and autonomous AI, involving the development, deployment, and management of both classical machine learning systems and AI workflows that improve commercial efficiency across the global CGT portfolio.
The primary focus will be collaborating with data scientists and GDDI teams to build production\-grade analytics infrastructure on AWS and Databricks—from predictive patient identification models and field force alert engines to agentic workflows that autonomously surface insights and recommend actions. This position ensures that ML models and AI agent systems are reproducible, compliant, performant, and scalable throughout their lifecycle. A strong emphasis is placed on data quality, monitoring, governance, and agent\-executable system design . This role will sit in Raleigh, NC.
Responsibilities:
ML \& Data Engineering
- 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 patient identification, alignment prediction, next\-best\-action engines, and competitive intelligence models.
- Data Pipeline Development: Design robust batch and streaming data workflows; integrate, define, and manage feature sets, lineage, and reuse to support AI/ML initiatives.
- Production Operations \& Monitoring: Ensure reliability and scalability of ML systems; implement effective logging, tracing, and alerting. Establish monitoring for model performance, data drift, bias, and service health. Monitor data quality across rare disease data feeds, where small population sizes amplify the impact of anomalies.
Agentic AI \& Agent Systems Engineering
- Agent Workflow Development: Collaborate with data scientists and commercial stakeholders to decompose complex business workflows into agent\-executable workstreams. Define boundaries between agent execution and human data science judgment.
- Instruction Architecture \& Prompt Engineering: Design and maintain prompt architectures, agent skills, memories, and context injection patterns. Author structured coding instructions that translate commercial analytics requirements into precise agent directives with clear acceptance criteria.
- Build agentic AI systems that autonomously detect anomalies in commercial data, such as competitive switching, patient discontinuation signals, and payer access changes. These systems generate hypotheses and push recommended actions to stakeholders and CRM systems.
- Token Economics \& Cost Optimization: Optimize agent execution for cost efficiency—manage context window utilization, minimize token consumption, and design instruction patterns that reduce iteration cycles. Monitor token economics per workstream to balance capability with budget.
Governance, Security \& Compliance
- Model, Agent \& Data Governance: Implement version control, approvals, documentation, and audit trails for datasets, code, models, and agent instructions. Ensure all AI/ML outputs are explainable, auditable, and compliant with HIPAA/PHI, GDPR, FDA promotional regulations, and REMS requirements. Enforce secrets management, role\-based access control, network policies, and data protection for agents operating on sensitive healthcare and commercial data within the enterprise perimeter.
Collaboration \& Enablement
- Cross\-functional Partnership: Work closely with data scientists, commercial analysts, and stakeholders across Brand, Market Access, Patient Services, and Field teams. 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 agent\-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 commercial operations and IT; maintain runbooks, rollback strategies, and change tickets .
Basic Qualifications:
Bachelor's Degree and Ten Years’ Experience
OR
Masters' Degree and Eight Years’ Experience
OR
PhD and Two Years’ Experience
Preferred Qualifications:
- Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field, or equivalent experience.
- Experience: 3–6\+ years in MLOps, Data Engineering, or ML platform roles, with a proven track record of deploying ML solutions at scale. At least 2\+ years building complex data science or large\-scale analytics solutions.
- Programming: Proficiency in Python and SQL; familiarity with TypeScript/JavaScript or a systems language (Go, Rust). Experience with TDD, CI/CD pipelines, and code quality standards.
- CI/CD \& Infrastructure: Experience with CI/CD tools (e.g., GitHub Actions), containerization (Docker), and cloud infrastructure concepts.
- ML Tools: Hands\-on experience with model packaging and serving frameworks (e.g., SageMaker, Databricks MLflow), experiment tracking, and model registry tools.
- Data Technologies: Proficiency with Databricks distributed processing (Spark), data orchestration (Airflow), MLflow, etc.
- AI/Agent Tools: Hands\-on experience with AI coding tools (Claude Code, GitHub Copilot, Cursor, or equivalent) and Cortex AI or comparable LLM serving platforms. Working understanding of how LLMs reason about code and familiarity with prompt engineering as an engineering discipline.
- Security \& Compliance: Understanding of data privacy and security in healthcare; experience with secrets management, audit controls, and compliance frameworks (HIPAA, SOC 2, 21 CFR Part 11\).
- Systems Thinking: Ability to design systems that scale across both
- Domain Experience: Knowledge of pharmaceutical commercial analytics in CGT or specialty pharma—HCP/HCO profiling and targeting, patient identification, call planning, demand forecasting, specialty pharmacy data, and omnichannel measurement.
- CGT Data Expertise: Experience with IQVIA (LAAD, Symphony, NPA), Veeva CRM, MMIT, Model N, specialty pharmacy dispense data, claims/RWD, and high\-value\-per\-patient environments.
- Agent System Design: Experience designing multi\-agent workflows, orchestration patterns, and autonomous systems for enterprise applications. Familiarity with MCP (Model Context Protocol) and agent interoperability frameworks.
- Performance \& Scalability: Experience with high\-throughput inference, batch scoring at scale, low\-latency APIs, and horizontally scalable agent workloads.
- Enterprise Integration: Experience integrating with Veeva, Salesforce, Microsoft 365, and ServiceNow APIs for end\-to\-end automation.
- Communication \& Collaboration: Excellent verbal and written communication skills; ability to present complex findings to both technical and non\-technical audiences, with a strong orientation toward teamwork in a fast\-paced, regulated environment.
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: $168,980\.00 \- $218,680\.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 $168K-$218K range is above the median for MLOps Engineer roles in our dataset (median: $190K across 22 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 3,823 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Gilead Sciences, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $217,200 based on 87 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($193K) sits 11% below the category median. Disclosed range: $168K to $218K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Gilead Sciences AI Hiring
Gilead Sciences has 4 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Positions span Foster City, CA, US, Raleigh, NC, US. Compensation range: $218K - $292K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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