Robotics Software Engineer

We are seeking a Robotics Software Engineer to design, develop, test, and deploy software for robotic systems. The ideal candidate will have experience in robotics programming, motion planning, sensor integration, computer vision, and real-time control systems. The role involves working closely with mechanical, electrical, AI, and embedded engineering teams to develop reliable and scalable robotic solutions., * Design and develop software for robotic systems and autonomous platforms.

• Develop algorithms for robot perception, localization, mapping, and navigation.
• Integrate sensors such as LiDAR, cameras, IMUs, GPS, ultrasonic sensors, and encoders.
• Develop robot control software for motion planning and path optimization.
• Build and maintain software using the Robot Operating System (ROS/ROS2).
• Implement and optimize computer vision and AI-based robotic applications.
• Test, debug, and validate robotic systems in simulation and real-world environments.
• Collaborate with hardware engineers for actuator and sensor integration.
• Develop reusable software libraries and APIs.
• Create technical documentation and maintain code repositories.
• Troubleshoot field issues and improve system reliability., Version Control & DevOps
• Git
• GitHub
• GitLab
• Docker
• CI/CD Pipelines

Preferred Skills

• Autonomous Mobile Robots (AMRs)
• Autonomous Guided Vehicles (AGVs)
• Industrial Robotics
• Drone Software Development
• Reinforcement Learning
• Edge AI
• NVIDIA Jetson
• NVIDIA Isaac SDK
• Cloud Robotics
• Linux system programming

Daily Responsibilities

• Develop and test robotics software modules.
• Integrate and calibrate sensors.
• Debug robot navigation and localization issues.
• Optimize robot performance and safety.
• Run simulations before hardware deployment.
• Collaborate with multidisciplinary engineering teams.
• Participate in design reviews and sprint planning.
• Document software architecture and test results., * ROS architecture (nodes, topics, services, actions)
• Robot kinematics and dynamics
• SLAM algorithms
• Path planning (A*, Dijkstra, RRT)
• PID control and feedback systems
• Sensor fusion (Kalman Filter, Extended Kalman Filter)
• Computer vision using OpenCV
• C memory management
• Python for robotics
• Embedded Linux and RTOS concepts
• Motion planning with MoveIt
• Docker and CI/CD for robotics
• Real-time programming
• Autonomous navigation challenges
• Debugging robotic systems

• C
• Python
• C
• MATLAB (preferred)

Robotics Frameworks

• ROS (Robot Operating System)
• ROS2
• Gazebo
• MoveIt
• OpenCV
• TensorFlow or PyTorch (for AI-enabled robotics)

Robotics Concepts

• Kinematics
• Dynamics
• Motion Planning
• Path Planning
• Robot Control Systems
• Localization
• SLAM (Simultaneous Localization and Mapping)
• Navigation Algorithms

Embedded Systems

• Embedded Linux
• Real-Time Operating Systems (RTOS)
• Microcontrollers (STM32, Arduino, ESP32)
• CAN Bus
• UART
• SPI
• I2C

Sensors & Hardware

• LiDAR
• RGB/RGB-D Cameras
• IMU
• GPS
• Ultrasonic Sensors
• Wheel Encoders
• Servo and Stepper Motors

Computer Vision & AI

• Object Detection
• Image Processing
• Pose Estimation
• Machine Learning
• Deep Learning
• Sensor Fusion

Simulation Tools

• Gazebo
• RViz
• Webots
• CoppeliaSim (V-REP)
• MATLAB/Simulink, * Strong analytical and problem-solving skills
• Effective communication
• Team collaboration
• Innovation and creativity
• Attention to detail
• Adaptability
• Time management
• Continuous learning mindset

Education

• Bachelor’s or Master’s degree in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, Mechatronics, or a related field.

Preferred Certifications

• ROS Developer Certification
• NVIDIA Deep Learning Institute Certifications
• AWS Certified Machine Learning (preferred)
• Embedded Systems Certifications
• AI/ML Certifications

Tools & Technologies

• ROS / ROS2
• Gazebo
• RViz
• MoveIt
• OpenCV
• Python
• C
• Linux (Ubuntu)
• Docker
• Git
• MATLAB
• TensorFlow
• PyTorch
• NVIDIA Jetson
• Visual Studio Code
• CMake

KPIs

• Software reliability and stability
• Robot navigation accuracy
• Localization and mapping performance
• Sensor integration success rate
• Defect density
• Test coverage
• Code quality and maintainability
• Deployment success rate