Scientific Computer Engineer, Staff Cloud Infrastructure

Revolution Medicines, Inc.; San Francisco (CA); On-site; USD 164,000 - 205,000; Full time

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.

Skills

Cloud infra

SRE practices

Kubernetes ops

Observability

Databricks infra

FinOps

Cost optimization

Terraform

Linux

CI/CD

Docker

Security

Cost reporting

Education

B.S. in Computer Science or related field

Tools

Terraform

Kubernetes

Docker

OpenTelemetry

Prometheus

Grafana

Job description

Revolution Medicines is a global, commercial-state oncology company dedicated to discovering, developing and delivering innovative medicines for patients with RAS-addicted cancers. Leveraging its differentiated RAS(ON) tri-complex inhibitor platform, the company is advancing a broad, integrated portfolio of oral RAS(ON) inhibitors designed to directly target the active, cancer-driving state of RAS. Founded on rigorous scientific inquiry and a willingness to challenge long-held assumptions, Revolution Medicines is committed to changing the trajectory of disease for patients with RAS-addicted cancers worldwide.

Our people are united by a shared way of working: follow the science, challenge assumptions, act with urgency and hold ourselves to a high standard of rigor—all in service of patients.

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 emphasize site reliability engineering, Kubernetes operations, observability, Databricks infrastructure, and cloud financial management (FinOps).

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. They will balance reliability, performance, security, and cost, using operational data to guide improvements.

• 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 service-level indicators and objectives, actionable alerting, incident response, post-incident reviews, and automation that reduces operational toil.
• Operate and improve Kubernetes environments, including cluster upgrades, workload deployment, resource allocation, autoscaling, networking, access controls, and troubleshooting.
• Develop observability across cloud, Kubernetes, and data platforms using metrics, logs, and traces to identify failures, diagnose performance issues, and understand capacity needs.
• 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.
• Own the operational foundations of Databricks environments, including workspace provisioning, compute policies, networking, identity, secrets, governance integrations, monitoring, and infrastructure automation.
• Partner with data teams to improve Databricks workload reliability, compute utilization, performance, and cost through appropriate compute sizing, autoscaling, workload isolation, and lifecycle controls.
• Support data platform infrastructure for scientific, analytics, and G&A use cases, including secure access patterns, governance integrations, and scalable storage.
• Drive FinOps practices across cloud and Databricks services, including cost allocation and tagging, usage visibility, budgets, forecasting, anomaly detection, and optimization recommendations.
• Identify and implement cost improvements through rightsizing, idle-resource cleanup, storage lifecycle management, and appropriate purchasing commitments, while preserving workload reliability and performance.
• Improve operational practices such as monitoring, incident response, runbooks, capacity planning, recovery readiness, and service ownership.
• 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 cloud operations, reliability engineering, and scientific infrastructure.

Required Skills, Experience and Education

• B.S. 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, including hands‑on experience operating Kubernetes in production.
• Experience implementing observability and reliability practices, including monitoring, alerting, incident management, and systematic troubleshooting of distributed services.
• Experience operating Databricks or comparable managed data platforms, with an understanding of compute, networking, identity, governance, and operational support.
• Experience analyzing cloud usage and expenditure and implementing cost optimization measures with measurable results.
• Demonstrated ability to work independently on complex technical problems and collaborate across teams.

Preferred Skills

• Experience using AI-assisted coding tools such as Claude Code, OpenAI Codex, or similar agents for cloud infrastructure, infrastructure‑as‑code, systems configuration, operational tooling, AI‑native application development, and agentic engineering.
• Experience with managed Kubernetes services such as Amazon EKS or Azure AKS, and deployment approaches using Helm or GitOps.
• Experience with observability technologies such as OpenTelemetry, Prometheus, Grafana, or comparable cloud-native or commercial tools.
• Experience with Databricks infrastructure automation, Unity Catalog integrations, compute policies, and workload usage and cost reporting.
• Experience with FinOps processes and tooling, including cost attribution, showback, forecasting, commitment planning, and collaboration with Finance and workload owners.
• 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 with cloud security, identity, least privilege access, auditability, and regulated environments.
• Familiarity with workflow tools such as Nextflow, Snakemake, Airflow, or cloud batch services.
• Experience building reusable platform patterns, documentation, and self‑service infrastructure capabilities.

The base pay salary range for this full-time position for candidates working onsite at our headquarters in Redwood City, CA is listed below. The range displayed on each job posting is intended to be the base pay salary range for an individual working onsite in Redwood City and will be adjusted for the local market a candidate is based in. Our base pay salary ranges are determined by role, level, and location. Individual base pay salary is determined by multiple factors, including job‑related skills, experience, market dynamics, and relevant education or training.

Please note that base pay salary range is one part of the overall total rewards program at RevMed, which includes competitive cash compensation, robust equity awards, strong benefits, and significant learning and development opportunities.

Revolution Medicines is an equal opportunity employer and prohibits unlawful discrimination based on race, color, religion, gender, sexual orientation, gender identity/expression, national origin/ancestry, age, disability, marital status, medical condition, and veteran status.

Revolution Medicines takes protection and security of personal data very seriously and respects your right to privacy while using our website and when contacting us by email or phone. We will only collect, process and use any personal data that you provide to us in accordance with our CCPA Notice and Privacy Policy. For additional information, please contact [e-mail].

Base Pay Salary Range

$164,000 - $205,000 USD