Job Title: Autonomous Vehicle EngineerJob Summary We are seeking an Autonomous Vehicle Engineer to design, develop, and deploy AI-powered autonomous driving systems. The ideal candidate will have expertise in robotics, computer vision, sensor fusion, machine learning, and autonomous navigation. This role involves developing perception, localization, mapping, planning, and control algorithms while collaborating with multidisciplinary teams to build safe, scalable, and production-ready autonomous vehicle solutions.
Key Responsibilities - Design, develop, and optimize autonomous driving software for perception, localization, planning, and vehicle control.
- Develop computer vision algorithms for lane detection, object detection, traffic sign recognition, pedestrian detection, and obstacle avoidance.
- Build sensor fusion solutions using cameras, LiDAR, radar, GPS, IMU, ultrasonic sensors, and vehicle telemetry.
- Implement localization and mapping algorithms using Simultaneous Localization and Mapping (SLAM) and High-Definition (HD) maps.
- Develop path planning, trajectory generation, and motion control algorithms for autonomous navigation.
- Integrate AI and deep learning models into autonomous driving software stacks.
- Build and validate autonomous vehicle software using simulation platforms and real-world testing environments.
- Optimize inference performance for embedded and edge computing platforms.
- Perform system integration, debugging, validation, and performance testing.
- Develop safety mechanisms, redundancy strategies, and fault-tolerant systems for autonomous operation.
- Collaborate with robotics, AI, embedded systems, software, and hardware engineering teams.
- Maintain technical documentation, validation reports, and engineering best practices.
Required Qualifications - Bachelor's or Master's degree in Computer Science, Robotics, Artificial Intelligence, Electrical Engineering, Mechanical Engineering, Automotive Engineering, or a related field.
- 3+ years of experience in autonomous systems, robotics, computer vision, or automotive software development.
- Strong programming skills in C++ and Python.
- Experience with ROS or ROS2.
- Solid understanding of robotics, control systems, vehicle dynamics, and motion planning.
- Experience with Linux development environments and Git.
- Knowledge of computer vision and deep learning frameworks.
- Familiarity with embedded systems and real-time software development.
Preferred Qualifications - Experience with autonomous driving platforms such as Autoware, Apollo, NVIDIA DRIVE, or similar frameworks.
- Experience with simulation tools such as CARLA, LGSVL (SVL Simulator), Gazebo, NVIDIA Isaac Sim, or MATLAB/Simulink.
- Knowledge of perception models using YOLO, Faster R-CNN, Detectron2, or transformer-based vision models.
- Experience with sensor fusion techniques such as Extended Kalman Filters (EKF), Unscented Kalman Filters (UKF), or particle filters.
- Familiarity with reinforcement learning for autonomous navigation.
- Experience deploying AI models on NVIDIA Jetson, NVIDIA DRIVE, Qualcomm, or similar edge computing platforms.
- Understanding of automotive safety standards such as ISO 26262, SOTIF (ISO 21448), and AUTOSAR.
- Knowledge of Vehicle-to-Everything (V2X) communication technologies.
Technical Skills - C++
- Python
- ROS / ROS2
- Linux
- Git
- OpenCV
- PyTorch
- TensorFlow
- CUDA
- TensorRT
- ONNX Runtime
- YOLO
- LiDAR Processing
- Radar Processing
- Sensor Fusion
- SLAM
- Path Planning
- Motion Planning
- Docker
- Kubernetes (preferred)
- WS / Azure / Google Cloud Platform (preferred)
Soft Skills - Strong analytical and problem-solving skills
- Excellent communication and teamwork
- ttention to detail and engineering discipline
- bility to work in multidisciplinary environments
- Innovation and continuous learning mindset
- Strong debugging and troubleshooting abilities
Nice to Have - Experience with HD map generation and localization
- Knowledge of edge AI optimization and real-time inference
- Experience with autonomous fleet management systems
- Familiarity with Functional Safety, cybersecurity, and automotive compliance requirements
- Contributions to autonomous driving, robotics, or AI open-source projects
- utomotive, ROS, AI, or cloud certifications
Key Performance Indicators (KPIs) - utonomous driving accuracy and safety performance
- Object detection and perception accuracy
- Localization and navigation precision
- Path planning and obstacle avoidance success rate
- System latency and real-time performance
- Software reliability and production stability
- Successful simulation and field validation results
- Reduction in safety incidents and system failures
Location Hybrid / Remote / On-site (as applicable)
Employment Type Full-time
