Machine Learning Engineer Automotive (Customer Facing) (f/m/div)

Key responsibilities

• Design, train, validate, and optimize machine learning models for automotive applications on Infineon microcontrollers such as AURIX™, Traveo™ and PSOC™
• Work with sensor, system, and application experts to identify suitable AI approaches for customer problems
• Collect, analyze, and preprocess automotive sensor and system data, define data collection and labeling strategies, and develop validation methodologies that ensure robust machine learning model performance under real-world operating conditions
• Support customers and internal teams in identifying and evaluating AI use cases and develop proof-of-concepts, demonstrations, and customer reference implementations demonstrating the value of Edge AI in automotive systems
• Participate in customer workshops, technical presentations, and solution reviews

Qualifications and Skills

• Proactive and collaborative engineer who takes ownership, adapts to evolving priorities, and consistently delivers high-quality solutions
• Enjoy working closely with colleagues and customers to drive successful outcomes
• Bachelor’s, Master's, or PhD degree in Computer Science, Machine Learning, Electrical Engineering, Embedded Systems, Artificial Intelligence, or a related technical discipline
• Strong understanding of machine learning fundamentals, including supervised and unsupervised learning, time-series analysis, signal processing, and deep learning, with hands-on experience in TensorFlow, PyTorch, Scikit-learn, ONNX, or equivalent frameworks
• Experience deploying machine learning models on resource-constrained devices including knowledge of TinyML, model optimization (quantization, compression), feature engineering, and real-time inference
• Strong programming skills in C, C++, and Python, with familiarity with RTOS concepts, performance profiling, and automotive-grade microcontrollers
• Understanding of automotive electronics, software architectures, and development processes, including familiarity with AUTOSAR and ISO 26262 would be an advantage
• Experience in software integration for safety-critical or real-time systems, with exposure to automotive radar, inertial/acoustic sensing, motor control, battery management systems, and industrial sensing applications would be an advantage
• Fluency in English