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

Summary

Cartography Biosciences is a therapeutics organization focused on creating an atlas to identify cancer-specific targets. They are seeking a DevOps Engineer to manage and enhance their cloud infrastructure for computational biology platforms and data pipelines, ensuring reliability and security in a biotech environment.

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

Skills

• 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
• Excellent written and verbal communication in English, with the ability to document systems clearly for both engineers and scientists
• Working hours that include meaningful synchronous overlap with US Pacific Time
• 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

Qualifications

Must Haves

• 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
• Excellent written and verbal communication in English, with the ability to document systems clearly for both engineers and scientists
• Working hours that include meaningful synchronous overlap with US Pacific Time

Nice to Haves

• 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