Scientific Computer Engineer, Staff Cloud Infrastructure

Revolution Medicines•Redwood City, CA

24d•Hybrid

About The Position

Revolution Medicines is seeking a Staff Cloud Infrastructure / Scientific Computer Engineer to build, operate, and improve infrastructure supporting computational science, data engineering, analytics, and enterprise business operations. The role emphasizes site reliability engineering, Kubernetes operations, observability, Databricks infrastructure, and cloud financial management (FinOps). The engineer will partner with scientific, G&A, data, security, and IT stakeholders to deliver reliable, secure, scalable compute and platform services, balancing reliability, performance, security, and cost. The role is based onsite at the company’s Redwood City, California headquarters for the stated salary range; the posting is also tagged #LI-Hybrid. The position includes designing and operating secure cloud infrastructure for R&D and scientific workloads; building reusable infrastructure patterns; applying Infrastructure as Code, primarily Terraform; and using SRE practices to improve critical services. It also entails operating Kubernetes environments, building observability across cloud, Kubernetes, and data platforms, supporting HPC and scheduler-based workloads, and enabling reproducible compute with Linux and containers. The engineer will own operational foundations for Databricks, partner with data and ML teams on reliable and cost-effective data and compute workloads, and support secure, governed data platform infrastructure. The role also drives FinOps practices, including usage visibility, budgeting, forecasting, anomaly detection, and cost optimization. The engineer will improve operational practices, collaborate across functions, mentor others, and contribute to standards, documentation, and reusable patterns.

Requirements

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.

Nice To Haves

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.

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 service-level indicators and objectives, actionable alerting, incident response, post-incident reviews, and toil-reducing automation.
Operate and improve Kubernetes environments, including 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 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 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 reliability and performance.
Improve monitoring, incident response, runbooks, capacity planning, recovery readiness, and service ownership.
Collaborate 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.