What You'll Do
Design and implement advanced 3D object detection models that integrate multi-modal sensor inputs, including camera, LiDAR, and radar, to enable reliable perception in autonomous ground systems. Own the architecture and optimization of detection pipelines from prototyping through production deployment.
Build and manage scalable data infrastructure to support model training and evaluation, including the creation of a proprietary dataset tailored to real-world operational conditions. Continuously assess model performance using meaningful metrics and iterate on state-of-the-art approaches to improve accuracy and robustness.
Collaborate with cross-functional teams to ensure perception solutions meet system-level requirements. Provide technical leadership and mentorship to junior engineers, promoting best practices in machine learning and software development.
Requirements
- Master’s or PhD in Computer Science, Robotics, Deep Learning, or a closely related field
- Minimum of five years of hands-on experience designing and deploying 2D and 3D object detection models in production environments
- Proven track record working across the full perception stack—from raw sensor data to detection and tracking outputs
- Strong programming skills in Python with experience in building and debugging machine learning pipelines
- Ability to drive technical projects independently and make sound architectural decisions under ambiguity
Preferred Qualifications
- PhD in Computer Science, Robotics, or a related field with research focus on perception or computer vision
- Peer-reviewed publications in top-tier conferences such as CVPR, ICRA, NeurIPS, IJCAI, or AAAI
Technical Environment
Work with cutting-edge tools and frameworks in 3D object detection and multi-modal sensor fusion. Develop and maintain data pipelines, train models at scale, and implement monitoring systems to track performance in real-world conditions. Focus on delivering reliable, field-tested perception solutions that meet rigorous safety and operational standards.
