Robotics Perception and Edge AI Engineer

About the Role

CDSPACE is looking for a Robotics Perception and Edge AI Engineer to develop real-time perception and intelligent navigation capabilities for UAV and robotic platforms.

You will build computer-vision and sensor-processing pipelines, integrate perception systems with ROS/ROS2, and deploy optimized AI models on edge-computing platforms. The role spans algorithm development, sensor integration, simulation, onboard deployment, bench testing, and flight validation.

What You’ll Do

Robotics Perception

• Develop perception algorithms for object detection, tracking, classification, localization, and scene understanding.
• Build real-time image-processing pipelines using OpenCV and GStreamer.
• Develop perception capabilities using monocular, stereo, thermal, depth, and other camera systems.
• Integrate data from cameras, IMUs, LiDAR, GPS/GNSS, optical-flow sensors, and other UAV payloads.
• Support sensor calibration, synchronization, coordinate transformations, and timestamp alignment.
• Improve perception performance across changing lighting, motion, altitude, weather, and operating conditions.

Computer Vision and Machine Learning

• Develop, train, evaluate, and improve computer-vision and deep-learning models.
• Prepare datasets and define data-collection, annotation, cleaning, and augmentation workflows.
• Select model architectures based on accuracy, latency, memory, power, and hardware constraints.
• Define evaluation metrics and conduct structured model-performance analysis.
• Investigate model failures, dataset limitations, false detections, and edge cases.
• Maintain reproducible training, validation, and experiment-tracking workflows.

Edge AI Deployment

• Deploy AI models on NVIDIA Jetson, Raspberry Pi, and other edge-computing platforms.
• Optimize models for real-time inference using quantization, pruning, hardware acceleration, and other optimization techniques.
• Convert and deploy models using ONNX, TensorRT, or similar inference frameworks.
• Profile CPU, GPU, memory, power consumption, throughput, and end-to-end latency.
• Integrate inference pipelines with cameras, sensors, communication systems, and flight software.
• Develop reliable startup, monitoring, logging, recovery, and health-check mechanisms for onboard AI applications.

ROS and Robotics Integration

• Integrate sensors and perception pipelines with onboard computing platforms using ROS/ROS2.
• Develop ROS/ROS2 nodes, services, topics, actions, and launch configurations.
• Build and test robotic applications using Gazebo or similar simulation environments.
• Integrate perception outputs with guidance, navigation, control, and mission-planning systems.
• Develop interfaces between onboard computers, flight controllers, payloads, and ground-control systems.
• Diagnose issues across sensors, middleware, networks, embedded software, and onboard computers.

UAV and Autonomous Systems

• Develop perception and navigation capabilities for multirotor and fixed-wing UAV platforms.
• Support obstacle detection, target tracking, visual navigation, inspection, and autonomous mission workflows.
• Integrate onboard perception applications with PX4, ArduPilot, or similar flight stacks.
• Support communication with flight controllers using MAVLink.
• Work with QGroundControl, Mission Planner, and related UAV tools.
• Collaborate with avionics, embedded software, controls, hardware, and flight-test teams.

Testing and Validation

• Conduct software-in-the-loop and hardware-in-the-loop simulations.
• Develop test datasets, test scenarios, and acceptance criteria for perception systems.
• Perform bench testing, field testing, and flight trials.
• Evaluate system performance under representative operating and environmental conditions.
• Diagnose sensor, communication, model, software, and system-level issues.
• Perform root-cause analysis and implement corrective actions.
• Maintain testing procedures, integration records, model documentation, and validation reports.

Application and Interface Development

• Develop supporting applications, visualization tools, and operator interfaces.
• Build tools for model configuration, telemetry monitoring, data recording, playback, and analysis.
• Support Qt-based software development when required.
• Implement clear logs, diagnostics, and system-health reporting.
• Maintain modular, testable, and documented software.

Required Qualifications and Skills

• Strong proficiency in Python.
• Working proficiency in C/C++ for performance-critical and hardware-integrated applications.
• Experience developing and deploying computer-vision or deep-learning models.
• Experience with OpenCV and real-time image-processing pipelines.
• Familiarity with GStreamer or similar multimedia frameworks.
• Experience with a deep-learning framework such as PyTorch or TensorFlow.
• Understanding of model training, validation, dataset preparation, augmentation, and performance evaluation.
• Experience deploying AI models on edge-computing platforms.
• Familiarity with ROS/ROS2 and robotic software architectures.
• Experience integrating cameras, sensors, and onboard computing systems.
• Understanding of Linux-based development environments, networking, and system debugging.
• Ability to profile and optimize inference latency, throughput, memory usage, and power consumption.
• Strong analytical, debugging, and problem-solving skills.
• Ability to communicate technical findings clearly and work effectively across engineering teams.

Good to Have

• Experience with NVIDIA Jetson, CUDA, TensorRT, ONNX, or GPU-accelerated computing.
• Experience with Raspberry Pi or other embedded Linux platforms.
• Knowledge of object detection, tracking, segmentation, visual odometry, SLAM, or sensor fusion.
• Experience with ROS/ROS2, Gazebo, and robotics simulation.
• Knowledge of MAVLink, QGroundControl, and Mission Planner.
• Hands-on experience with PX4, ArduPilot, or a similar flight stack.
• Understanding of UAV flight dynamics and multirotor or fixed-wing architectures.
• Experience with Qt software development.
• Experience with camera calibration, synchronization, streaming, and recording.
• Familiarity with MLOps, experiment tracking, dataset versioning, and automated model evaluation.
• Experience taking perception or AI systems from research prototypes through field deployment.

Experience: 3–5 years in computer vision, robotics perception, AI/ML, or edge computing.

Education: Degree in Electronics, Electrical Engineering, Computer Science, Artificial Intelligence, Robotics, or a related engineering discipline.