Senior Manager, Machine Learning Platform Engineer

Gilead Sciences•Foster City, CA

1d•$157,590 - $203,940

About The Position

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.

Requirements

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.
Bachelor's Degree and Eight Years' Experience OR Masters' Degree and Six Years' Experience OR PhD / PharmD
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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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).
Author and schedule reliable, observable workflows using orchestration tools and distributed processing, ensuring dependencies, retries, and SLAs are handled without manual intervention.
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.
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.
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.
Where AI tooling is used, apply sensible practices to manage context usage and cost, balancing capability with available budget.
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.
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.
Develop clear, detailed guides, operational playbooks, and user instructions. Coordinate releases with IT and the global team; maintain runbooks, rollback strategies, and change tickets.
Apply security, access-control, and data-governance best practices across pipelines and infrastructure, ensuring solutions meet the expectations of a validated, GxP-regulated environment.
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.
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.