Senior ML Ops Engineer (Machine Learning Infrastructure)

📋 Description

• Design and implement MLOps pipelines for data, training, deployment, monitoring.
• Architect and deploy scalable ML infra for distributed training and inference.
• Own ML infra stack across R&D and production; enable teams to deploy models.
• LA office presence required ~1 week/month.
• Build cloud-based ML systems (AWS/GCP) optimized for ML workloads.
• Ensure CI/CD and governance for models and datasets.

🎯 Requirements

• Bachelor's or higher in CS/ML or related field.
• 5+ years building large-scale reliable systems; 2+ in ML infra/MLOps.
• Deploy prod-grade ML pipelines and platforms; end-to-end lifecycle.
• Proficient in Python, Git, and software engineering fundamentals.
• Hands-on with MLflow, Kubeflow, SageMaker, Airflow, Metaflow.
• Experience with AWS, GCP, Azure ML architectures.

🎁 Benefits

• Salary Range: $150,000 - $240,000 USD.
• Hybrid work with LA office ~1 week per month.