Description

You'll develop perception systems for autonomous battery-electric rail vehicles, working on deep learning models for detection, segmentation, tracking, and scene understanding using multimodal sensor data. You'll build scalable training pipelines, adapt state-of-the-art research for real-world deployment, and optimize inference performance on edge compute systems.

Requirements

• Strong foundations in machine learning and computer vision
• Built meaningful ML systems through research, internships, or early industry experience
• Experience with deep learning and model training workflows
• Ability to work with multimodal sensor data
• Familiarity with real-time inference and edge computing

Responsibilities

• Train and improve deep learning models for detection, segmentation, tracking, and scene understanding
• Develop scalable training pipelines for large perception datasets
• Work with multimodal sensor data including cameras, lidar, and radar
• Evaluate and adapt state-of-the-art research for real-world deployment
• Support deployment of perception models to edge compute systems on autonomous vehicles