Robotics Vision Engineer

As a Robotics Vision Engineer, you will be responsible for developing and deploying vision algorithms that enable robotic systems to perceive, interpret, and interact with their surroundings. You will work on SLAM (Simultaneous Localization and Mapping), object detection, depth perception, sensor fusion, and real-time image processing. This role requires expertise in computer vision, robotics, real-time image processing, and integrating vision systems into autonomous robotic platforms.

Key Responsibilities

1. Development of Vision Algorithms

• Design & Implement Computer Vision Solutions: Develop vision algorithms for object detection, recognition, tracking, and segmentation using OpenCV, PyTorch, TensorFlow, or similar frameworks.
• Enhance Robotic Navigation & SLAM: Develop SLAM algorithms to enable localization and mapping using monocular, stereo, or RGB-D cameras.
• Optimize Depth & Stereo Vision Systems: Implement depth estimation techniques using stereo cameras, LiDAR, Time-of-Flight (ToF), and structured light sensors.

2. Sensor Integration & Calibration

• Integrate Vision Sensors: Configure and integrate cameras, LiDAR, and other vision-based sensors into robotic platforms.
• Multi-Sensor Fusion: Develop sensor fusion algorithms to combine camera, LiDAR, IMU, and other sensor data for improved robot perception and accuracy.
• Optimize Sensor Placement: Collaborate with hardware and mechanical teams to determine optimal sensor configurations for enhanced coverage and reliability.

3. Real-Time Image & Video Processing

• Optimize for Real-Time Processing: Develop efficient image processing pipelines for low-latency, real-time inference on robotic systems.
• Improve Visual Perception in Challenging Conditions: Implement robust algorithms to handle low-light conditions, occlusions, motion blur, and dynamic backgrounds.
• Deploy on Embedded Platforms: Optimize and deploy vision algorithms on edge devices using NVIDIA TensorRT, OpenCV, and CUDA acceleration.

4. Robotics Perception & Decision-Making

• Develop End-to-End Perception Systems: Collaborate with robotics and AI teams to develop perception-driven autonomous navigation and interaction systems.
• Support Autonomous Decision-Making: Utilize vision data for robotic path planning, obstacle avoidance, and object manipulation.
• Validate Vision Algorithms in Simulations: Use simulation tools such as Gazebo and RViz to test and validate robotic vision algorithms before deployment.

5. Research & Innovation

• Stay at the Cutting Edge of Computer Vision: Explore emerging vision-based AI techniques, including transformer-based vision models, generative AI, and neuromorphic vision.
• Prototype Advanced Vision Solutions: Build proof-of-concept models and vision-based AI innovations for robotics and industrial automation.
• Contribute to Open-Source & Research Initiatives: Engage in open-source computer vision projects, contributing to the broader AI and robotics community.

6. Testing, Validation & Optimization

• Test Vision Algorithms in Real-World Environments: Perform rigorous field testing of vision models in robotic systems under diverse conditions.
• Optimize for Resource-Constrained Devices: Adapt vision algorithms for low-power hardware, balancing accuracy, speed, and memory efficiency.
• Continuously Improve Performance: Monitor deployed vision systems, troubleshoot issues, and refine models based on real-world performance feedback.

7. Collaboration & Documentation

• Work with Cross-Functional Teams: Partner with robotics engineers, AI researchers, and embedded systems teams for seamless vision system integration.
• Maintain Detailed Documentation: Document vision algorithms, sensor calibration procedures, and deployment workflows for reproducibility and transparency.
• Communicate Research & Findings: Present technical insights and progress to stakeholders and contribute to a collaborative development culture.

Required Qualifications - Education & Experience

• Bachelors or Masters degree in Computer Science, Robotics, Electrical Engineering, or a related field.
• 3+ years of experience developing and deploying computer vision algorithms for robotics, autonomous systems, or industrial applications.
• Proven track record in designing and optimizing vision-based AI models for real-world robotic applications.

Technical Skills

• Computer Vision & AI Frameworks:
• Proficiency in OpenCV, PyTorch, TensorFlow, and Deep Learning architectures for vision-based applications.
• Experience in object detection, feature extraction, segmentation, and tracking algorithms.
• Programming & Software Development:
• Strong programming skills in Python and C++.
• Familiarity with CUDA programming for GPU-accelerated vision processing.
• Robotics & SLAM:
• Strong knowledge of SLAM techniques, feature-based localization, path planning, and obstacle avoidance.
• Experience in motion tracking and 3D reconstruction for robotics.
• ROS & Simulation Environments:
• Experience working with Robot Operating System (ROS) and simulation tools (Gazebo, RViz).
• Familiarity with real-time robotic vision system deployment.

Preferred Qualifications

• Edge AI Expertise: Experience deploying computer vision models on edge devices such as NVIDIA Jetson, ARM Cortex, or FPGA-based accelerators.
• Sensor Hardware Knowledge: Familiarity with camera systems, LiDAR, Time-of-Flight sensors, and calibration techniques.
• Model Optimization Techniques: Experience with TensorRT, OpenVINO, ONNX, and other AI model acceleration tools.