Note: The job is a remote job and is open to candidates in USA. 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
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