DevOps Engineer evaluating Kubernetes - based distributed systems for Logos’s decentralized blockchain, messaging, and storage technology stack. Automating experiments, benchmarking, and observability.
CB
DevOps Engineer, Cloud Infrastructure, Scientific Computing
Posted 2 months ago
About the role
- DevOps Engineer managing cloud infrastructure, orchestrating scientific computing workflows at Cartography Biosciences. Working remotely across the Americas with a focus on cloud security and scalability.
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.
- Working hours that include meaningful synchronous overlap with US Pacific Time.
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