Perception Engineer

Key Responsibilities:

1. Design and develop perception pipelines for real-world environments using computer vision, deep learning, and geometric reasoning.
2. Build and improve models for scene understanding, recognition, segmentation, tracking, and spatial perception using modern deep learning architectures.
3. Develop perception systems for monocular scene understanding and depth-related tasks where explicit depth sensing may not be available.
4. Develop and evaluate online CNN models for human detection, tracking, and path prediction under occlusion for safe robotic operation.
5. Translate use cases into measurable ML objectives, success metrics, and acceptance criteria such as mIoU, mAP, precision/recall, latency, FPS, and failure-case coverage.
6. Own the data pipeline end to end: data collection planning, annotation workflow definition, dataset curation, preprocessing, augmentation, label quality checks, and post-processing.
7. Work with annotation teams and tools such as CVAT to create high-quality datasets, including pixel-wise annotations, taxonomy definition, and edge-case labeling.
8. Fine-tune and adapt modern foundation models such as SAM, DINOv2, and evaluate when they are useful versus custom task-specific architectures.
9. Apply both classical and modern vision techniques where appropriate.
10. Optimize models for embedded and edge platforms, with focus on compute efficiency, inference speed, memory constraints, and deployment practicality.
11. Build clean, maintainable, modular code and scalable training/inference pipelines with good engineering practices and reproducibility.
12. Collaborate with robotics and embedded teams to integrate perception outputs into navigation, planning, and control systems.
13. Maintain documentation, experiment records, deployment specifications, and clear records of model limitations and known failure modes.