Staff Cloud Infrastructure Engineer — Scientific Computing

Revolution Medicines, Inc.

San Francisco (CA)

On-site

USD 164,000 - 205,000

Full time

14 days+

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Job summary

Revolution Medicines, Inc. is seeking a Staff Cloud Infrastructure / Scientific Computer Engineer to build, operate, and improve platform foundations for computational science and enterprise operations.

The role emphasizes site reliability engineering, Kubernetes operations, observability, and FinOps for Databricks and cloud workloads. You will partner with scientific, data, IT, and security teams to balance reliability, performance, and cost, delivering reusable platform patterns and scalable

Qualifications

B.S. in CS or related field or equivalent practical experience.
8+ years in cloud infrastructure, platform engineering, or related roles.
Strong AWS/Azure experience.
Terraform or IaC experience.
Linux, networking, troubleshooting experience.
CI/CD, version control, change management practices.
Experience with containers and Kubernetes in production.
Observability and reliability practices.
Databricks experience and compute patterns.
Cloud cost analysis and optimization experience.
Ability to work independently and collaborate across teams.

Responsibilities

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.
Apply SRE practices to critical platform services, including SLIs/SLAs, alerting, incident response, and automation.
Operate and improve Kubernetes environments, including upgrades, deployments, autoscaling, networking, access controls.
Develop observability across cloud, Kubernetes, and data platforms using metrics, logs, and traces.
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 infra for training workflows and platform reliability.
Own the operational foundations of Databricks environments, including governance and monitoring.
Collaborate with data teams to improve Databricks reliability and cost efficiency.
Drive FinOps practices across cloud and Databricks services.
Identify and implement cost improvements while preserving workload performance.
Improve operational practices like monitoring, incident response, and runbooks.
Mentor others and contribute to standards and reusable platform patterns.