Staff Machine Learning Engineer

At Mercedes-Benz Research & Development North America (MBRDNA), we are committed to delivering world-class automotive technologies that push the boundaries of what is possible. Our teams of highly skilled engineers and designers use cutting-edge software and technology, to enhance the driving experience and reduce environmental impact. The Mercedes-Benz Data and AI team is seeking a hands-on machine learning expert for research, design, and development of cutting-edge technology applied in projects that will shape the future of Mercedes-Benz vehicles with the goal of enhancing our in-vehicle user experience. In this role, you will be responsible for design, development, and presentation of novel machine learning techniques and applications, as well as guiding junior members to become experts. You will be working closely with the development team to support the product development as well as evaluation and proposal of algorithm adjustments to ameliorate undesired learning behavior, towards a progressive future in the automotive industry. Job Responsibilities: Lead research topics through multiple phases related to automotive machine learning solutions: experimentation and validation, proof of concept, tuning and constraint adjustment. Provide support and insight to development teams responsible for implementation of machine learning techniques in a native head unit environment. Present and demo research topics to Mercedes-Benz internal groups, and at external events such as academic conferences and tradeshows. Define, interface and manage relationships with other teams within MBRDNA and Mercedes-Benz organizations. Have a deep understanding of the business and operational impact for different technology tradeoffs, and able to balance effectively based on company’s need. Work closely with a proactive, highly motivated engineering team to bring next generation infotainment and telematics solutions to Mercedes-Benz products worldwide. Work in an Agile/Scrum environment to deliver high quality software with a measurable customer value. Minimum Qualifications: Master’s or higher degree, in CS/CE/EE, Math, Statistics, or Physics. 5+ years of work experience. Strong programming skills in Python, with experience in ML frameworks such as PyTorch, TensorFlow. Experience in deploying machine learning and AI algorithms into product environments. Extensive experience in areas of Recommendation Systems, Generative AI, GNNs, NLP, Computer Vision. Deep understanding of machine learning fundamentals. Ability to keep up with the bleeding edge of research in the field, understand its ramifications, and immediately apply it to the betterment of our products. Excellent understanding of ML workflow: data preparation, model training and evaluation, ablation studies, deployment, and inference optimization on different platforms. Experience in leading research topics through a full research pipeline: from concept to proof and validation. Demonstrate excellent communication skills, proactive and effective cross-collaboration, problem solving, and effectively engaging with both technical and non-technical audiences. Valid CA Driver’s License. Preferred Qualifications: Experience architecting AI solutions for user-experience enhancement. Industry experience in integrating solutions within larger system architectures. Experience with Reinforcement Learning, Multi-modal Generative AI. Programming skills in C++. Experience with distributed data processing in Spark. Experience leading or mentoring engineers to tackle a large-scale AI problems. The successful candidate’s starting pay will be determined based on a wide range of factors, including, but not limited to, job-related education, skills, and experience, geographic location, and market conditions. The current salary range for this position is as follows and may be modified in the future: $152,300 - $194,100.

Mercedes-Benz Research and Development North America, Inc. is committed to delivering world-class automotive technologies that push the boundaries of what is possible.