Staff / Senior Staff Architect - Data & AI Platforms

Revolution Medicines•Redwood City, CA

2h•$164,000 - $205,000•Hybrid

About The Position

Revolution Medicines is a clinical-stage precision oncology company focused on developing novel targeted therapies to inhibit frontier targets in RAS-addicted cancers. The company’s 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 Revolutionaries in 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 architect–builder: 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.

Requirements

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.

Responsibilities

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.
Build early reference implementations (V0–V0.9) 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.
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).
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.
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.
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.