DevOps Engineer, Cloud Infrastructure & Scientific Computing (Remote, Americas)

Cartography Biosciences is hiring a DevOps Engineer to own and evolve cloud infrastructure supporting its computational biology platforms, internal applications, and data pipelines. The role focuses on productionizing scientific workflows, maintaining reliable and observable services, and applying security practices for sensitive scientific and business data.

Responsibilities:

• Architect, deploy, and maintain cloud infrastructure across Google Cloud Platform (primary) and AWS (secondary), with a focus on cost efficiency, reproducibility, and security across compute, storage, networking, and identity
• Manage infrastructure-as-code using Terraform, with full version control via GitHub and CI/CD pipelines that support both scientific compute and internal application deployment
• Support and extend our scientific compute infrastructure, including Cromwell, Google Batch, Cloud Run, Cloud Storage, and GPU-accelerated environments for protein design, structural prediction, and machine learning workloads
• Maintain uptime, observability, and incident response for internal applications including Slack integrations, web dashboards, and data ingestion services that connect lab and computational workflows
• Implement and uphold data security practices appropriate for a biotech environment, including IAM, secrets management, audit logging, network segmentation, and compliance-adjacent controls
• Collaborate with computational biologists and software engineers to operationalize new tools and pipelines, translating prototype workflows into reliable production systems
• Triage and resolve infrastructure issues across the stack, with clear documentation and post-mortems that improve the system over time
• Articulate technical decisions and trade-offs to diverse audiences, including engineering and scientific teams

Requirements:

• 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
• Excellent written and verbal communication in English, with the ability to document systems clearly for both engineers and scientists
• Fluency with AI-augmented development workflows, including practical use of tools such as Claude Code, Cursor, or GitHub Copilot as part of day-to-day infrastructure and scripting work
• Experience supporting scientific or research computing environments (genomics, single-cell, structural biology, ML, or HPC)
• Familiarity with workflow orchestration systems such as Cromwell, Nextflow, WDL, or Airflow
• Experience with GPU infrastructure and scheduling of ML or protein design workloads
• Background in life sciences infrastructure, including familiarity with Benchling, ELN integrations, or scientific data management systems
• Prior experience at an early-stage biotech, scientific software company, or research-driven startup
• Familiarity with SOC 2, HIPAA-adjacent, or other regulated-environment security frameworks