Senior ML Infrastructure Engineer: Scale Production ML

Gridware; San Francisco (CA); On-site; USD 190,000 - 260,000; Full time

Benefits offered by this job

Health, Dental & Vision plans

Paid parental leave

Alternating day off (every other Monday)

Two week paid break ('Off the Grid')

Commuter allowance

Company-paid training

Job summary

Gridware in San Francisco is looking for a Senior ML Infrastructure Engineer to design, build, and maintain their ML deployment infrastructure. This role will involve creating monitoring systems and collaborating with engineering teams to improve CI/CD pipelines.

The ideal candidate should have over 5 years of experience in production ML infrastructure and proficiency in Python, AWS, and Kubernetes. Competitive benefits include health coverage, a paid two-week break, and a commuter allowance.

Qualifications

• 5+ years of experience building production ML infrastructure.
• Strong software engineering skills and proficiency in Python.
• Experience with cloud platforms (AWS) and container orchestration (Kubernetes).
• Familiarity with feature stores, model registries, or centralized metadata systems.

Responsibilities

• Design, build, and maintain the infrastructure for ML model deployment.
• Develop monitoring systems to track model performance and data quality.
• Create end-to-end testing frameworks for models and pipelines.
• Collaborate with engineering teams to integrate ML systems with infrastructure.
• Improve CI/CD pipelines for ML workloads.

Skills

5+ years of experience building production ML infrastructure

Strong software engineering skills and proficiency in Python

Experience with cloud platforms (AWS)

Container orchestration (Kubernetes)

Familiarity with feature stores, model registries, or centralized metadata systems (i.e. MLFlow)

Job description

Gridware in San Francisco is looking for a Senior ML Infrastructure Engineer to design, build, and maintain their ML deployment infrastructure. This role will involve creating monitoring systems and collaborating with engineering teams to improve CI/CD pipelines.

The ideal candidate should have over 5 years of experience in production ML infrastructure and proficiency in Python, AWS, and Kubernetes. Competitive benefits include health coverage, a paid two-week break, and a commuter allowance.