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Senior Manager, Machine Learning Platform Engineer

United States - California - Foster City💼 Full-time🗓 2026-08-10 → 2026-09-26

Core

Build and maintain ML/data infrastructure to operationalize quality models for signal detection, risk analytics, and continuous improvement in drug development.

Role type

Senior Manager, Machine Learning Platform Engineer

Builds

Scalable data pipelines, cloud infrastructure, and MLOps platforms for R&D Quality models

Domain

Healthcare / Pharmaceutical R&D Quality

Deliverable

production ML models | infrastructure

Required skills

Python, SQL, Cloud infrastructure (AWS/Azure), Containerization (Docker/Kubernetes), CI/CD, Data orchestration, Model lifecycle management, Infrastructure-as-code, Distributed processing

Preferred skills

Terraform, PyTorch, TensorFlow, XGBoost, scikit-learn, Datadog, Splunk, CloudWatch, Prometheus

Technologies

Python, SQL, Git, GitHub Actions, AWS, Azure, Docker, Kubernetes, Databricks, Terraform, scikit-learn, PyTorch, TensorFlow, XGBoost, Datadog, Splunk, CloudWatch, Prometheus

Responsibilities

Operate as a self-directed contributor scoping and driving end-to-end technical initiatives; Independently provision and manage cloud infrastructure using IaC and containerization; Develop and maintain pipelines for model transition from experimentation to production; Design robust batch and streaming data workflows integrating QMS data sources; Author and schedule reliable, observable workflows using orchestration tools; Ensure reliability and scalability of data pipelines with effective logging, tracing, and alerting; Collaborate with data scientists to explore AI-assisted workflow support; Design and maintain prompt and instruction patterns for AI tooling; Work with stakeholders to provide frameworks and guardrails for analytics delivery; Set up testing frameworks for traditional ML models and AI-generated code; Develop operational playbooks and coordinate releases; Apply security and data-governance best practices for GxP-regulated environments; Evaluate emerging ML/AI tooling and contribute to the technical roadmap; Define evaluation criteria and guardrails for AI-assisted components.

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