Sr Software Engineer, Embedded Machine Learning

<p><em>We are CARIAD, an automotive software development team with the Volkswagen Group. Our mission is to make the automotive experience safer, more sustainable, more comfortable, more digital, and more fun. To achieve that we are building the leading tech stack for the automotive industry and creating a unified software platform for over 10 million new vehicles per year. We’re looking for talented, digital minds like you to help us create code that moves the world. Together with you, we’ll build outstanding digital experiences and products for all Volkswagen Group brands that will transform mobility. Join us as we shape the future of the car and everyone around it.</em></p> <p><strong><u>Role Summary</u></strong></p> <p>The Sr Software Engineer, Embedded Machine Learning is responsible for designing, optimizing, and deploying machine learning models on high-performance embedded hardware platforms. This role focuses on translating machine learning models from training environments into production-ready implementations on embedded ML accelerators, including selection of efficient model architectures, quantization, runtime performance analysis, and functional validation.</p> <p>The Sr Software Engineer, Embedded Machine Learning works independently on complex technical problems and collaborates closely with software, hardware, and systems teams to ensure reliable, real-time performance of machine learning workloads in production embedded systems.</p> <p><strong><u>Role Responsibilities</u></strong></p> <p><strong>Embedded ML Development &amp; Optimization&nbsp;</strong></p> <ul> <li>Design, train, and optimize machine learning models for execution on embedded ML accelerators</li> <li>Quantize and convert machine learning models from training frameworks to embedded runtime environments</li> <li>Analyze and optimize runtime performance to meet real-time and hardware constraints</li> <li>Develop and maintain production-quality code and artifacts supporting machine learning deployment on embedded systems</li> </ul> <p><strong>Validation &amp; Production Support&nbsp;</strong></p> <ul> <li>Verify functional correctness and performance of deployed models on target hardware</li> <li>Debug and resolve performance and accuracy issues across the machine learning deployment pipeline</li> <li>Collaborate with cross-functional teams to integrate machine learning models into embedded systems</li> <li>Support deployed machine learning models in production, including performance monitoring, issue triage, and iterative improvement</li> </ul> <p><strong>Technical Collaboration &amp; Continuous Improvement&nbsp;</strong></p> <ul>