Sr MLOps Engineer

Intuitive; Sunnyvale (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Intuitive is seeking an engineer to design, build, and maintain the infrastructure for the full ML lifecycle, from development to deployment. You will ensure seamless integration of models into production systems and deliver scalable value.

The role demands hands-on experience with Kubernetes, ML orchestration (Metaflow), and GPU hardware validation across diverse devices, plus strong problem-solving in a fast-paced environment.

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

Tools

Metaflow

Job description

Job Description

Primary Function of 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.

Essential Job Duties

• 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