Senior / Staff Cloud Platform Engineer (ML)
About The Position
Calico seeks a Senior / Staff Cloud Engineer to lead the execution and operation of our next-generation machine learning (ML) platform. As the technical authority for our Google Cloud Platform (GCP) environment, you will build infrastructure-as-Code (IaC), design secure perimeters, automate identity management, and migrate existing workflows into a unified, compliant infrastructure. If you are passionate about DevSecOps, IaC, and building "paved paths" that empower scientists while enforcing rigorous security, this is the role for you. You will be the critical bridge that allows our ML Systems and Research engineers to train and deploy ML models for frontier BioML research. No biology or life sciences background is required for this role.
Requirements
- BS/MS with 7+ years or PhD with 4+ years of relevant software engineering, DevSecOps, or Cloud architecture experience
- Strong programming skills in Python
- Strong hands-on experience with Google Cloud Platform (GCP) core services
- Experience with Kubernetes cluster administration
- Experience with Terraform, building CI/CD pipelines, and containerization (Docker)
- Must be willing to work onsite at least four days a week
Nice To Haves
- A strong intellectual curiosity for life sciences
- Experience managing Kubernetes for HPC or ML workloads
- Experience supporting Machine Learning orchestration and queueing frameworks (e.g., Kueue, Ray)
- Experience reading and debugging ML Python code
- Experience implementing compliant cloud architectures (e.g., SOC2, HIPAA, or similar frameworks)
- Familiarity with Golang
- Strong communication skills with a proven ability to guide users through migration
Responsibilities
- Deliver our next-generation ML platform
- Lead the consolidation and modernization of our compute environments, and accelerate the whole company's R&D cycle
- Lead the centralization of GCP projects into a unified, compliant landing zone
- Design secure perimeters to protect sensitive biological data, and build automated guardrails using Terraform
- Design, deploy, and maintain robust GKE clusters tailored for distributed ML training and batch processing workloads
- Troubleshoot node-level GPU/TPU issues, manage scheduling add-ons, and optimize cluster autoscaling
- Design intuitive onboarding processes and "paved paths" for scientists to seamlessly onboard onto the new platform
- Manage org-level GPU/TPU compute capacity, implement centralized billing and cost reporting, and build comprehensive monitoring dashboards to track cluster utilization and optimize R&D expenditure
Benefits
- two annual cash bonuses
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Associate degree
