Senior ML Infrastructure & MLOps Engineer (Core Platform)

Location: La Honda, California, United States

Salary: $126,900–$185,100 per year

Company

hirify.global provides staffing and technical workforce solutions.

What you will do

• Architect and maintain high-performance ML training and model-serving infrastructure on Google Kubernetes Engine.
• Build optimization pipelines, including knowledge distillation and foundational training tooling.
• Develop automated pipelines for model training, validation, and continuous deployment.
• Create scalable data-sampling and feature-generation platforms for ML research and experimentation.
• Build standardized deployment tools that improve platform adoption and onboarding for research and engineering teams.
• Collaborate with ML researchers and software engineers to turn theoretical models into scalable production systems.

Requirements

• 5–10+ years of experience designing and operating large-scale distributed ML platforms.
• Experience supporting production-grade ML workflows in cloud environments.
• Deep expertise in container orchestration, especially GKE or equivalent enterprise Kubernetes environments.
• Hands-on experience with scalable ML pipelines such as Kubeflow, Airflow, or TFX.
• Strong proficiency in distributed training, feature stores, and model-serving infrastructure.
• Ownership-driven, pragmatic approach with strong communication and collaboration skills.

Nice to have

• Experience on tier-one enterprise ML/AI platform teams.
• Knowledge of distributed-systems backend optimization and infrastructure as code.

Culture & Benefits

• Focus on reliability, infrastructure uptime, cost efficiency, and developer velocity.
• Cross-functional collaboration with researchers and platform engineers.
• For temporary assignments lasting 13 weeks or longer: medical, dental, vision, and 401(k) benefits.
• Statutory sick pay where required.
• Reasonable accommodations are available during the employment process.