We are looking for a highly capable Perception Engineer to build robust perception systems for outdoor robotics. This role focuses on developing vision models, data pipelines, and inference systems for challenging real-world environments, with strong emphasis on accuracy, edge-case handling, maintainability, and deployment readiness. The ideal candidate should combine strong fundamentals with applied vision engineering expertise across image processing, deep learning, data-centric model development, failure analysis, and deployment optimization. We are looking for someone who can understand complex problems deeply, define clear evaluation standards, and build reliable perception pipelines for real-world operation.
Key Responsibilities
- Design and develop perception pipelines for real-world environments using computer vision, deep learning, and geometric reasoning.
- Build and improve models for scene understanding, recognition, segmentation, tracking, and spatial perception using modern deep learning architectures.
- Develop perception systems for monocular scene understanding and depth-related tasks where explicit depth sensing may not be available.
- Develop and evaluate online CNN models for human detection, tracking, and path prediction under occlusion for safe robotic operation.
- Translate use cases into measurable ML objectives, success metrics, and acceptance criteria such as mIoU, mAP, precision/recall, latency, FPS, and failure-case coverage.
- Own the data pipeline end to end: data collection planning, annotation workflow definition, dataset curation, preprocessing, augmentation, label quality checks, and post-processing.
- Work with annotation teams and tools such as CVAT to create high-quality datasets, including pixel-wise annotations, taxonomy definition, and edge-case labeling.
- Fine-tune and adapt modern foundation models such as SAM, DINOv2, and evaluate when they are useful versus custom task-specific architectures.
- Apply both classical and modern vision techniques where appropriate.
- Optimize models for embedded and edge platforms, with focus on compute efficiency, inference speed, memory constraints, and deployment practicality.
- Build clean, maintainable, modular code and scalable training/inference pipelines with good engineering practices and reproducibility.
- Collaborate with robotics and embedded teams to integrate perception outputs into navigation, planning, and control systems.
- Maintain documentation, experiment records, deployment specifications, and clear records of model limitations and known failure modes.
About Company: We are a early stage start-up incubated inside ARTPARK, IISC. We are a fast-growing robotics startup building autonomous ground robots for agriculture and industrial applications. At our core, we believe in practical innovation, rapid prototyping, and solving real-world problems with smart, rugged, and reliable robots. Interns here get hands-on exposure, work directly with the founding team, and contribute to meaningful projects that go from concept to field deployment.
