Senior MLOps Engineer, Deep Genomics

• You are someone who enjoys keeping the infrastructure running smoothly so that scientists can focus on their research
• You are comfortable working across cloud platforms, CI/CD systems, containers, and GPUs – and you take pride in making these systems reliable and easy for others to use
• You have 4+ years of experience in production infrastructure or MLOps, you write solid Python, and you are curious about the ML and scientific workflows your work supports
• Above all, you are a collaborative, kind team member who communicates clearly, adapts to evolving needs, and is happy to help colleagues grow their own infrastructure skills along the way
• If this sounds like you, we would love to hear from you
• 4+ years of experience operating production infrastructure
• Proficiency with cloud platforms (GCP preferred; AWS/Azure acceptable) and Infrastructure-as-Code (Terraform)
• Extensive Hands-on experience with Kubernetes and containerization (Docker)
• Solid background in CI/CD systems (CircleCI, GitHub Actions, or similar)
• Experience managing GPU compute (provisioning, debugging, driver management)
• Familiarity with Python package and environment management (e.g., pip, conda, pixi)
• Strong Python programming skills
• Self-motivated problem solver with excellent communication skills

Desirable

• You will own and evolve the infrastructure that powers our ML pipelines – from cloud environments and CI/CD systems to workflow orchestration and model deployment
• You will work closely with ML scientists, bioinformaticians, and software engineers to keep our platform reliable, reproducible, and scalable
• You'll maintain and improve cloud infrastructure (GCP) using Infrastructure-as-Code tools (Terraform)
• Manage IAM, RBAC, and permission policies across cloud environments
• Own and evolve CI/CD pipelines (CircleCI, GitHub Actions) and ensure best practices are followed across the engineering and ML teams
• Administer and support workflow orchestration platforms (e.g., Seqera/Nextflow, Argo, Kubeflow)
• Operate and configure ML experiment tracking and registry tooling (e.g., W&B, MLflow)
• Build and maintain containerized environments (Docker) and manage Kubernetes clusters
• Manage GPU resources – provisioning, scheduling, and debugging hardware and driver issues
• Write and maintain Python tooling, scripts, and integrations that support ML infrastructure
• Help deploy ML models to production environments and monitor their performance