Tech Stack

Description

You will lead the design and development of scalable systems powering autonomy and perception pipelines for autonomous battery-electric rail vehicles. You'll own the ML infrastructure stack, enabling ML teams to develop, train, and deploy models efficiently in both R&D and real-world environments.

Requirements

• Experience with 0 to 1 builds of perception systems
• Experience building scalable ML infrastructure for continuous integration/deployment, experiment management, and governance of models and datasets

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
• Support automation of model evaluation, selection, and deployment workflows