Perception & Tracking Engineer

Job description

Job responsibilities

• Design, implement, test, debug, and maintain core 3D Computer Vision based multi-object tracking algorithms and infrastructure, including measurement conditioning, data association, track correlation, propagation,…
• Develop and optimize computer vision algorithms for real-time detection and tracking of small objects in robotic and autonomous systems.
• Implement and improve object detection and multi-object tracking pipelines using modern deep learning techniques.
• Train, evaluate, and deploy machine learning models for perception tasks in dynamic environments.
• Design image preprocessing and feature extraction pipelines to improve model robustness and performance.
• Optimize tracking pipelines for low-latency, real-time performance on embedded hardware and/or GPUs. Profile bottlenecks and apply algorithmic or implementation improvements.
• Collaborate with robotics and software teams to integrate perception systems into autonomous platforms and real-world deployments.
• Participate in the full software development lifecycle: requirements, design, implementation, unit/integration/system testing, deployment, and maintenance.

Job requirements

• Associate Perception & Tracking Engineer
• Perception & Tracking Engineer
• Senior Perception & Tracking Engineer Your title and level will be determined based on your experience, skills, and the scope of responsibility appropriate for the role.
• 2-3 years of experience working with perception algorithms , robotics, or machine learning and comfortable in a role that specializes in 3D Computer Vision based tracking
• Bachelor's or Master's degree in Computer Science, Aerospace Engineering, Applied Math or related field
• Strong experience developing vision-based tracking algorithms for real-time applications
• Experience implementing object detection (YOLO, SSD, R-CNN, or similar) and classic or deep learning-based tracking algorithms (Kalman and particle filters, ByteTrack, or similar)
• Solid applied math foundation in linear algebra, probability, statistics, and optimal estimation (Kalman filtering and variants)