Lead MLOps Engineer for Autonomous Rail AI

Parallel; Los Angeles (CA); Hybrid; USD 150,000 - 250,000; Full time

Job summary

Parallel Systems is seeking an experienced MLOps/ML Infrastructure Engineer to lead the design and development of scalable systems powering autonomy and perception pipelines for autonomous, battery-electric rail vehicles.

You will own ML infrastructure from training to deployment, enabling rapid, reliable model iteration in both R&D and production environments, with a hybrid onsite requirement in Los Angeles.

Qualifications

• Bachelor’s or higher degree in Computer Science, Machine Learning, or a relevant engineering discipline.
• 5+ years of experience building large-scale, reliable systems; 2+ years focused on ML infrastructure or MLOps.
• Proven experience architecting and deploying production-grade ML pipelines and platforms.
• Strong knowledge of ML lifecycle: data ingestion, model training, evaluation, packaging, and deployment.
• Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Airflow, Metaflow, or similar).

Responsibilities

• Design and implement robust MLOps solutions, including automated pipelines for data management, model training, deployment and monitoring.
• Architect, deploy, and manage scalable ML infrastructure for distributed training and inference.
• Collaborate with ML engineers to gather requirements and develop strategies for data management, model development and deployment.
• Build and operate cloud-based systems (e.g., AWS, GCP) optimized for ML workloads in R&D, and production environments.
• Build scalable ML infrastructure to support continuous integration/deployment, experiment management, and governance of models and datasets.
• Support the automation of model evaluation, selection, and deployment workflows.

Skills

MLOps

Python

CI/CD

Cloud platforms

ML pipelines

Git

System design

Education

Bachelor's degree in CS/ML

Tools

MLflow

Kubeflow

SageMaker

Airflow

Metaflow

Job description

Parallel Systems is seeking an experienced MLOps/ML Infrastructure Engineer to lead the design and development of scalable systems powering autonomy and perception pipelines for autonomous, battery-electric rail vehicles.

You will own ML infrastructure from training to deployment, enabling rapid, reliable model iteration in both R&D and production environments, with a hybrid onsite requirement in Los Angeles.