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# Manager, Machine Learning Platform Engineer

- **Company:** Gilead Sciences Inc.
- **Location:** Foster City, CA, United States
- **Experience:** Expert
- **Salary:** $157,590.0 - $203,940.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Business Analytics Applications, Data Analysis, Application Lifecycle Management, Automation of Tests, Microsoft Azure, Bash Shell, Big Data, Health Informatics, Cloud Computing, Cloud Engineering, Information Systems, Computer Programming, Computer Engineering, Continuous Integration, Information Engineering, Data Files, Data Infrastructure, Distributed Computing Environment, Distributed Systems, Github, Python (Programming Language), Machine Learning, Windows PowerShell, Release Management, Prometheus, DataOps, Software Deployment, SQL Databases, Data Streaming, TypeScript, Datadog, Data Logging, Data Processing, Scripting, Cloud Platform System, Apache Spark, Git, Containerization, Integration Tests, Kubernetes, Information Technology, Production Code, Code Inspection, Cloudwatch, Terraform, Splunk, Software Version Control, Data Pipelines, GXP, Docker, Databricks, Golang
- **Published:** October 11, 2026
- **Apply:** https://www.juju.com/job/1_gileadinc-R0054104

## About the Role

Preferred + **Cloud infrastructure / infrastructure-as-code:** Terraform; broader cloud engineering experience (AWS preferred). + **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 data engineering, platform engineering, or data operations + Significant hands-on experience operationalizing data and analytics 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 data processing libraries and analytics applications in production. + Experience with CI/CD, infrastructure-as-code, and cloud-based data 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.

## Description

Join Gilead and help create possible, together. **Job Description** This Data Platform Engineer will have the unique opportunity to apply cutting-edge data and analytics 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 development of robust 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 Data Platform Engineer will partner with the Quality Analytics & Insights team, a small, high-impact group responsible for advancing data and analytics capabilities across R&D Quality.
This role will build and maintain the data infrastructure that supports Quality Performance and Quality Health analytics focused on signal detection, risk analytics, early identification of emerging issues, mitigation strategies, and continuous improvement.
Working closely with analytics professionals, the engineer will transform analytical prototypes into scalable, production-ready solutions through robust data pipelines, cloud infrastructure, monitoring, and automation.
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, while helping define the technology roadmap for next-generation analytics and automation across the organization. **Primary Responsibilities** 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 data processing, application deployment, and analytics with minimal reliance on external teams. +
**Application Lifecycle Management:** Develop and maintain pipelines to transition analytics applications and data products from development to production, including packaging, CI/CD, automated testing, and deployment.
Support reliable delivery of Quality use cases such as signal detection, risk analytics, and Quality Performance/Quality Health analytics. + **Data Pipeline Development:** Design robust batch and streaming data workflows; integrate, define, and manage data 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 and analytics applications; implement effective logging, tracing, and alerting.
Establish monitoring for data quality, pipeline performance, and service health, paying particular attention to QMS data feeds, where low-frequency quality signals amplify the impact of anomalies.
Collaboration & Enablement + **Cross-functional Partnership:** Work closely with analytics professionals, 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 data pipelines, analytics applications, and production 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 data, cloud, and analytics tooling; prototype promising approaches and recommend adoption, contributing to the technical roadmap for next-generation Quality analytics and automation. **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 platform:** Databricks.