ML Ops Engineer

Parallel Systems is pioneering autonomous battery-electric rail vehicles designed to transform freight transportation by shifting portions of the $900 billion U.S. trucking industry onto rail. Our innovative technology offers cleaner, safer, and more efficient logistics solutions. Join our dynamic team and help shape a smarter, greener future for global freight.

Senior ML Ops Engineer (Machine Learning Infrastructure)

Parallel Systems is seeking an experienced MLOps/ML Infrastructure Engineer to lead the design and development of the scalable systems that power our autonomy and perception pipelines. As we build the first fully autonomous, battery-electric rail vehicles, you will play a critical role in enabling the ML teams to develop, train, and deploy models efficiently and reliably in both R&D and real-world environments.

This is an opportunity to take full ownership of the ML infrastructure stack, from distributed training environments and experiment tracking to deployment and monitoring at scale. You’ll collaborate closely with world-class engineers in autonomy, robotics, and software, helping shape the core systems that make real-time, safety-critical ML possible. If you're driven by building robust platforms that unlock innovation in AI and robotics, we’d love to work with you. 

This can be a hybrid role (minimum of 1 week per month onsite in Los Angeles) for a senior engineer with experience in 0 to 1 builds of perception systems. 

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. 

What Success Looks Like: 

• After 30 Days: You have developed a deep understanding of the product goals, existing infrastructure, and stakeholder requirements. You've conducted technical discovery and proposed a preliminary MLOps architecture—evaluating various ML tools, cloud services, and workflow strategies—clearly outlining pros and cons for each option. 
• You’ve delivered a detailed design document that outlines the end-to-end ML pipeline, including data ingestion, model training, deployment, and monitoring. Based on feedback from ML engineers and stakeholders, you’ve iterated on the design and built PoC for the core ML workflow aligned with the approved architecture.