Senior ML Ops Engineer (Machine Learning Infrastructure)

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 remote role 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 go