DevOps Engineer, Cloud Infrastructure, Scientific Computing

This is a fully remote position, open to applicants in North America.

📋 Description

• Design, implement, and manage cloud infrastructure primarily on Google Cloud Platform and secondarily on AWS, emphasizing cost-effectiveness, reproducibility, and security across compute, storage, networking, and identity.

• Oversee infrastructure-as-code utilizing Terraform, incorporating full version control via GitHub and CI/CD pipelines that facilitate both scientific computing and internal application deployment.

• Enhance and support our scientific computing infrastructure, which includes Cromwell, Google Batch, Cloud Run, Cloud Storage, and GPU-accelerated environments tailored for protein design, structural prediction, and machine learning tasks.

• Ensure uptime, observability, and incident response for internal applications such as Slack integrations, web dashboards, and data ingestion services that bridge lab and computational workflows.

• Establish and maintain data security measures suitable for a biotech setting, including IAM, secrets management, audit logging, network segmentation, and controls related to compliance.

• Work collaboratively with computational biologists and software engineers to operationalize new tools and pipelines, converting prototype workflows into dependable production systems.

• Diagnose and resolve infrastructure challenges across the stack, providing thorough documentation and conducting post-mortems that enhance the system progressively.

• Communicate technical decisions and trade-offs to a variety of audiences, including engineering and scientific teams.

⛳️ Requirements

• A minimum of 4 years of practical experience in DevOps, SRE, or cloud infrastructure roles.

• Extensive production experience with Google Cloud Platform (GCE, GKE, GCS, Cloud Run, IAM, Cloud Logging, Batch) and a foundational understanding of AWS (EC2, S3, IAM, Lambda).

• Strong expertise in Terraform, GitHub Actions or similar CI/CD systems, and Docker.

• Proficient in Python for tasks involving scripting, automation, and data manipulation; comfortable working in Unix or Linux terminal environments.

• Relevant experience in implementing data security measures, including encryption, access policies, secrets management, and audit trails.

• Proven ability to maintain internal applications and services with high uptime standards.

• Exceptional written and verbal communication skills in English, capable of documenting systems clearly for both engineering and scientific stakeholders.

• Availability for working hours that ensure significant synchronous overlap with US Pacific Time.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health, dental, and vision insurance.

• Flexible work hours and remote work options.

• Opportunities for professional development and continuous learning.

• Collaborative and innovative work environment.