Key Responsibilities
- Design and Development: Lead the end-to-end design, development, and implementation of robust AI/ML models (e.g., Deep Learning models for perception, prediction, and control) for production use in automotive platforms.
- Data Pipeline Management: Develop and manage large-scale data pipelines for the collection, cleaning, annotation, and augmentation of complex automotive sensor data (e.g., LiDAR, RADAR, camera, ultrasonic) required for model training and validation.
- Model Optimization and Deployment: Optimize ML models for performance, latency, and memory constraints on Edge devices and automotive-grade Electronic Control Units (ECUs), utilizing techniques like quantization, pruning, and hardware acceleration.
- Validation and Testing: Collaborate with verification and validation (V&V) teams to rigorously test and evaluate AI models against safety-critical standards and real-world driving scenarios.
- System Integration: Integrate developed AI software components with the vehicle’s operating system and other hardware/software subsystems, ensuring seamless functionality and reliability.
- Research and Innovation: Stay abreast of the latest AI/ML research and automotive technologies, proactively proposing and prototyping innovative solutions to enhance product performance and features.
Required Qualifications
- Experience: 3–5 years of professional experience in developing and deploying AI/ML solutions, with a significant portion of this experience directly in the automotive domain.
- Education: Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Robotics, or a related quantitative field.
- Technical Proficiency:
- Expertise in common ML/DL frameworks (PyTorch, TensorFlow).
- Strong programming skills in Python and C++ (essential for production-level embedded systems).
- Hands-on experience with Computer Vision algorithms (e.g., object detection, semantic segmentation, tracking) and libraries like OpenCV.
- Proven experience with automotive sensor data processing and Sensor Fusion techniques.
- Automotive Domain Knowledge:
- Familiarity with automotive communication protocols (CAN, Ethernet) and architectures.
- Understanding of safety-critical systems and standards (ISO 26262).
✨ Preferred Skills (Nice to Have)
- Experience with MLOps practices and tools for model lifecycle management.
- Familiarity with automotive operating systems like AUTOSAR or QNX.
- Knowledge of simulation tools (e.g., CARLA, IPG CarMaker) for testing AI models.
- Experience in reinforcement learning or predictive modeling for vehicle diagnostics or user behavior.
Key Responsibilities
- Design and Development: Lead the end-to-end design, development, and implementation of robust AI/ML models (e.g., Deep Learning models for perception, prediction, and control) for production use in automotive platforms.
- Data Pipeline Management: Develop and manage large-scale data pipelines for the collection, cleaning, annotation, and augmentation of complex automotive sensor data (e.g., LiDAR, RADAR, camera, ultrasonic) required for model training and validation.
- Model Optimization and Deployment: Optimize ML models for performance, latency, and memory constraints on Edge devices and automotive-grade Electronic Control Units (ECUs), utilizing techniques like quantization, pruning, and hardware acceleration.
- Validation and Testing: Collaborate with verification and validation (V&V) teams to rigorously test and evaluate AI models against safety-critical standards and real-world driving scenarios.
- System Integration: Integrate developed AI software components with the vehicle’s operating system and other hardware/software subsystems, ensuring seamless functionality and reliability.
- Research and Innovation: Stay abreast of the latest AI/ML research and automotive technologies, proactively proposing and prototyping innovative solutions to enhance product performance and features.
Required Qualifications
- Experience: 3–5 years of professional experience in developing and deploying AI/ML solutions, with a significant portion of this experience directly in the automotive domain.
- Education: Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Robotics, or a related quantitative field.
- Technical Proficiency:
- Expertise in common ML/DL frameworks (PyTorch, TensorFlow).
- Strong programming skills in Python and C++ (essential for production-level embedded systems).
- Hands-on experience with Computer Vision algorithms (e.g., object detection, semantic segmentation, tracking) and libraries like OpenCV.
- Proven experience with automotive sensor data processing and Sensor Fusion techniques.
- Automotive Domain Knowledge:
- Familiarity with automotive communication protocols (CAN, Ethernet) and architectures.
- Understanding of safety-critical systems and standards (ISO 26262).
✨ Preferred Skills (Nice to Have)
- Experience with MLOps practices and tools for model lifecycle management.
- Familiarity with automotive operating systems like AUTOSAR or QNX.
- Knowledge of simulation tools (e.g., CARLA, IPG CarMaker) for testing AI models.
- Experience in reinforcement learning or predictive modeling for vehicle diagnostics or user behavior.
