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

We are looking for a DevOps Engineer to own and evolve the cloud infrastructure that powers our computational biology platforms, internal applications, and data pipelines. You will build and maintain systems spanning workflow orchestration for genomics and structural biology workloads, internal-facing scientific applications, and the security and reliability infrastructure that supports them.

This role sits at the intersection of cloud engineering, scientific computing, and applied software development. You will work closely with computational biology, antibody discovery, and software engineering teams to translate prototype workflows into production systems, ensure uptime and observability for the tools scientists rely on daily, and apply security best practices appropriate for a biotech environment handling sensitive scientific and business data.

This role is 100% remote and can be based in the US, Canada, Mexico, Central America, or South America.

Key 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

Logistics

• Fully remote, open to candidates based in the Americas (US, Canada, Mexico, Central America, or South America).
• You will report directly to the head of computational biology.
• Compensation calibrated to local market with US benchmarking
• Direct collaboration with our US-based engineering and computational biology teams in South San Francisco.