# DevOps Engineer, Cloud Infrastructure & Scientific Computing (Remote, Americas) at Cartography Biosciences

- Company: Cartography Biosciences
- What the company does: About Cartography Biosciences Cartography Biosciences is a therapeutics organization creating the first atlas to identify targets that are specific enough to only engage cancerous cells, broad enough to work across cancer cells and patients, and safe enough... Backed by a16z.
- Company website: https://cartography.bio
- Type: Startups
- Level: Mid level
- Location: Remote (United States)
- Work setup: Remote
- Pay: $130K to $145K base salary per year (USD)
- Posted: 2026-05-13
- Apply by: 2026-10-08
- Apply: https://ats.rippling.com/cartography-biosciences/jobs/c290244a-6b5b-451f-b4e1-29497edd1bd1
- Page: https://www.1752.vc/careers/jobs/cartography-biosciences-devops-engineer-cloud-infrastructure-and-scientific-comp/

## About the role

We are looking for a DevOps Engineer to own and evolve the cloud infrastructure that powers our computational biology platforms, internal applications, and data pipelines. You will build and maintain systems spanning workflow orchestration for genomics and structural biology workloads, internal-facing scientific applications, and the security and reliability infrastructure that supports them.

## What they're looking for

- DevOps Engineer: 4+ years of hands-on DevOps, SRE, or cloud infrastructure experience
- Strong production experience with Google Cloud Platform (GCE, GKE, GCS, Cloud Run, IAM, Cloud Logging, Batch) and working knowledge of AWS (EC2, S3, IAM, Lambda)
- Deep proficiency with Terraform, GitHub Actions or equivalent CI/CD systems, and Docker
- Proficiency in Python for scripting, automation, and data handling, comfortable in Unix or Linux terminal environments
- Practical experience implementing data security controls, including encryption, access policies, secrets management, and audit trails
- Demonstrated ability to maintain internal-facing applications and services with high uptime expectations

Tags: Computational Biology