Staff Cloud Infrastructure, Scientific Computer Engineer

Revolution Medicines is a late-stage clinical oncology company developing novel targeted therapies for patients with RAS-addicted cancers. The companys R&D pipeline comprises RAS(ON) inhibitors designed to suppress diverse oncogenic variants of RAS proteins. The companys RAS(ON) inhibitors daraxonrasib (RMC-6236) a RAS(ON) multi-selective inhibitor; elironrasib (RMC-6291) a RAS(ON) G12C-selective inhibitor; zoldonrasib (RMC-9805) a RAS(ON) G12D-selective inhibitor; and RMC-5127 a RAS(ON) G12V-selective inhibitor are currently in clinical development. As a new member of the Revolution Medicines team you will join other outstanding professionals in a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway.

The Opportunity:

We are seeking a Staff Cloud Infrastructure Scientific Computer Engineer to help build operate and improve the infrastructure foundations that support computational science data engineering analytics and enterprise business operations across RevMed. This role will partner with scientific G&A data security and IT stakeholders to deliver reliable secure scalable compute and platform services. The ideal candidate brings deep infrastructure experience strong operational judgment and the ability to translate scientific and business computing needs into practical reusable platform patterns.

What Youll Do:

• Design operate and improve secure cloud infrastructure for R&D and scientific computing workloads.

• Build reusable infrastructure patterns for compute storage networking identity secrets management and observability.

• Use Infrastructure as Code primarily Terraform to standardize provisioning and change management.

• Support high-throughput and scientific compute environments including HPC and scheduler-based workloads.

• Enable reproducible compute patterns using Linux containers registries and automated deployment workflows.

• Partner with data and ML teams on infrastructure for dataset staging training workflows batch inference and platform reliability.

• Operate and improve Databricks infrastructure including workspace patterns cluster policies networking identity secrets governance integrations and cost-aware operations.

• Support data platform infrastructure for scientific analytics and G&A use cases including secure access patterns governance integrations and scalable storage.

• Improve operational practices such as monitoring incident response runbooks capacity planning and cost awareness.

• Collaborate cross-functionally with scientists data engineers security and IT to deliver practical platform solutions.

• Mentor others and contribute to standards documentation and reusable patterns for scientific infrastructure.

Required Skills Experience and Education:

• BS in Computer Science Engineering or a related technical field or equivalent practical experience.

• 8 years of relevant experience in cloud infrastructure platform engineering systems engineering DevOps/SRE scientific computing or related technical roles.

• Strong experience operating production infrastructure in AWS Azure or another major cloud platform.

• Strong experience with Terraform or comparable Infrastructure as Code practices.

• Practical experience with Linux networking fundamentals system troubleshooting and operational support.

• Experience with CI/CD version control peer review testing and change management practices.

• Experience supporting containerized workloads using Docker or similar technologies.

• Demonstrated ability to work independently on complex technical problems and collaborate across teams.

Preferred Skills:

• Experience supporting HPC or high-throughput compute environments including Slurm or similar schedulers.

• Experience supporting scientific computing computational chemistry computational biology or data-intensive R&D workflows.

• Experience operating Databricks infrastructure data platform services or governed analytics environments.

• Experience with cloud security identity least privilege access auditability and regulated environments.

• Familiarity with workflow tools such as Nextflow Snakemake Airflow cloud batch services or Kubernetes.

• Experience building reusable platform patterns documentation and self-service infrastructure capabilities.

What Success Looks Like

• Scientific data and G&A teams can run scalable workflows reliably using clear supported infrastructure patterns.

• Databricks environments are secure governed reliable cost-aware and aligned with enterprise platform standards.

• Infrastructure changes are reproducible reviewed tested documented and safely deployed.

• Cloud and scientific compute environments are reliable observable secure and cost-aware.

• Platform patterns reduce one-off support needs and improve consistency across teams.

• Cross-functional partners view this role as a trusted technical owner for scientific infrastructure.

#LI-Hybrid #LI-YG1

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