Staff Cloud Infrastructure, Scientific Computer Engineer

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 You’ll 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.

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