Sr MLOps Engineer

Intuitive•Sunnyvale, CA

5d•$160,300 - $271,400•Onsite

About The Position

In this role, you will be responsible for designing, building, and maintaining the infrastructure and tools necessary to support the entire machine learning lifecycle, from development to deployment. You will work closely with ML engineers and software developers across Intuitive to ensure that machine learning models are seamlessly integrated into our systems and deliver value at scale. The ideal candidate is an independent and fast-paced engineer with excellent problem-solving skills and practical working knowledge of modern ML development techniques.

Requirements

3+ years of experience in infrastructure, DevOps, or MLOps roles, or equivalent practical experience
Demonstrated experience operating Kubernetes in production (networking, storage, RBAC, troubleshooting)
Strong scripting/automation skills in Python and/or Bash; comfort with Infrastructure-as-Code tools (Ansible, Helm, Terraform, or similar)
Hands-on experience with at least one distributed storage system (S3, MinIO, NetApp, or similar)
Experience building or maintaining CI/CD pipelines (GitLab CI, ArgoCD, or equivalent)
Solid understanding of Linux systems administration and networking fundamentals
Excellent communication and documentation skills, with the ability to write clear runbooks and migration guides
High degree of autonomy and comfort working across the full stack, iteratively building solutions
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field; or equivalent experience

Nice To Haves

Experience with ML orchestration frameworks (Metaflow, MLflow, Kubeflow, or similar)
Familiarity with GPU infrastructure (NVIDIA drivers, CUDA, NVLink/NUMA topology, MIG partitioning)
Prior experience in a regulated industry (healthcare, finance, or similar) where auditability and access control are critical
Experience leading or supporting large-scale infrastructure migrations with multiple stakeholder teams

Responsibilities

Bootstrap and maintain a production-grade Kubernetes cluster, including CNI networking and storage integration
Deploy and configure ML orchestration tooling (e.g., Metaflow) and artifact/dataset storage solutions to support reproducible ML workflows
Validate GPU node health and configuration across heterogeneous hardware (B200, L40S, A6000, V100), including driver/CUDA standardization and topology checks
Design and execute team migration playbooks, working directly with engineering teams to port workflows, migrate datasets/artifacts, and roll out tool updates
Write and maintain runbooks, architecture documentation, and disaster recovery procedures
Participate in on-call rotation and incident response for platform-level issues
Collaborate with IT/Security on identity integration, access control, and compliance requirements
Continuously evaluate and adopt infrastructure best practices for reliability, cost, and developer experience