Senior Manager, Machine Learning Platform Engineer

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.

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.

• 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.

• Workflow Support: Collaborate with data scientists and Quality stakeholders to explore how parts of complex quality workflows…