Company
Automotive
AIEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“AI Engineer. Skills: AI/ML models, Deep Learning, Computer Vision, Sensor Fusion. Lead AI/ML model design. Develop AI/ML models”
What You'll Achieve.
Enhance product performance; Enhance product features
Industry & Context.
Model optimization for performance; Model optimization for latency; Model optimization for memory
What They're Looking For.
Must Have
3–5 years AI/ML solutions experience, Automotive domain experience, Bachelor's or Master's degree, Python programming skills, C++ programming skills, Computer Vision algorithms experience, Automotive sensor data processing experience, Sensor Fusion techniques experience
Nice to Have
MLOps practices experience, Automotive operating systems familiarity, Simulation tools knowledge, Reinforcement learning experience, Predictive modeling experience
What You'll Do.
Lead AI/ML model design
Implement AI/ML models
Develop data pipelines
Manage data pipelines
Integrate AI software components
Propose innovative solutions
Prototype innovative solutions
How You'll Work.
Team & Collaboration
Verification and validation teams
Full Job Description
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 automo
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