Staff Architect – Data & AI Platforms

Revolution Medicines is a clinical-stage precision oncology company focused on developing novel targeted therapies to inhibit frontier targets in RAS-addicted cancers. The companys R&D pipeline comprises RAS(ON) Inhibitors designed to suppress diverse oncogenic variants of RAS proteins and RAS Companion Inhibitors for use in combination treatment strategies. As a new member of the Revolution Medicines team you will join other outstanding Revolutionariesin a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway.

The Opportunity:

As RevMed scales its science pipeline and clinical footprint we are building a connected trusted and AI-ready digital foundation that transforms how data software and intelligence are created shared and applied across the enterprise.

The Staff / Senior Staff Architect Data & AI Platforms is a senior individual contributor role responsible for defining validating and advancing the technical architecture of Revolution Medicines enterprise data and AI ecosystem. This role spans the entire modern technical stack cloud infrastructure data platforms AI/GenAI systems and software engineering.

This role is designed for a hands-on architectbuilder: someone who defines architectural direction and personally builds critical early implementations while also technically leading internal engineers contractors and consulting partners as solutions scale. You will work in close partnership with Data Product Managers engineering teams and scientific and clinical computational groups.

Key Responsibilities

Architect and Advance the Enterprise Data & AI Platform

• Define evolve and own the enterprise Data & AI reference architecture across AWS Azure and Databricks supporting discovery translational clinical commercial and enterprise workloads including research-grade and GxP use cases.

• Design and validate scalable end-to-end data architecture patterns across ingestion storage transformation analytics and AI/ML.

• Establish and validate lakehouse standards (Bronze/Silver/Gold) metadata lineage and AI-ready Gold datasets through working reference implementations.

• Make principled architectural decisions across data models storage formats and table designs (e.g. Delta Lake Apache Iceberg) balancing performance cost schema evolution governance and compliance.

Translate Architecture into Working Systems

• Build early reference implementations (V0V0.9) that prove architectural paths before they are scaled by delivery teams.

• Define clear architectural guardrails and reusable patterns that enable teams to scale consistently without sacrificing scientific flexibility.

Enable Domain Data Products

• Partner closely with Data Product Managers operating in a Hub-and-Spoke model to translate domain needs into concrete technical designs.

• Provide architectural blueprints and reference pipelines enabling reusable high-quality domain data products (e.g. Compound & Library Assay Sample Omics Study Patient/Subject CRO Data Exchange).

Design and Build GenAI and Agentic Platform Foundations

• Define and implement shared enterprise GenAI and agentic AI capabilities including embeddings vector stores retrieval-augmented generation (RAG) agent orchestration frameworks and ML lifecycle primitives.

• Ensure GenAI systems are grounded in governed RevMed data products are secure and auditable and can be reused across scientific clinical commercial and operational workflows.

Define Engineering Cloud and Operational Standards

• Establish architectural standards and best practices across cloud infrastructure data platforms AI/ML platforms and software engineering.

• Define and validate DevOps DataOps MLOps and CloudOps patterns including CI/CD observability data quality lineage model lifecycle guardrails cost management and security practices.

Collaborate Across Science Engineering and Partners

• Partner closely with computational and analytical teams across R&D and clinical functions to ensure platforms support real-world scientific workflows from exploratory analysis to production-grade pipelines.

• Provide hands-on technical leadership to internal engineers contractors and consulting partners ensuring alignment with RevMed standards and effective knowledge transfer.

Required Skills Experience and Education:

• 12 years of experience in architecture and hands-on engineering roles spanning data platforms cloud infrastructure AI/ML systems and software engineering.

• Deep hands-on experience with Databricks (Delta Lake Unity Catalog DLT MLflow).

• Hands-on experience with GenAI RAG vector databases and agentic systems.

• Strong experience designing and building platforms on AWS and Microsoft Azure.

• Proven ability to translate architectural designs into working implementations.

• Strong collaboration skills across product science engineering and external partners.

• Advanced degree in Computer Science Bioinformatics Engineering or related discipline.

• Experience in life sciences biotech or pharma R&D data environments. #LI-Hybrid #LI-YG1