AI Research and Development Engineer, Faraday Future

• Master's or Ph.D. in Computer Science, Electrical Engineering, or a related field
• At least 8 years of experience in a similar role
• Strong programming skills in Python and experience with deep learning frameworks such as PyTorch
• Expertise in machine learning, deep learning, and computer vision
• Experience with transformer architecture and model optimization techniques
• Proven track record in developing and deploying large-scale machine learning models

Desirable

• Faraday Future (FF) is a California-based mobility company, leveraging the latest technologies and world’s best talent to realize exciting new possibilities in mobility. We’re producing user-centric, technology-first vehicles to establish new paradigms in human-vehicle interaction. We’re not just seeking to change how our cars work – we’re seeking to change the way we drive. At FF, we’re creating something new, something connected, and something with a true global impact
• FF is seeking exceptional AI Research and Development Engineer to join our foundation models team focused on self-driving technology. In this role, you will collaborate with the brightest minds in the industry to develop state-of-the-art neural networks for autonomous driving systems. Your work will contribute to the development of production ADAS/AD features for a world-class vehicle platform
• Develop and implement AI algorithms, models, and data pipelines for end-to-end autonomous driving tasks. Key responsibilities:
• Design, train, and deploy large deep learning models that leverage vast amounts of labeled and unlabeled data from a fleet of vehicles
• Develop novel state-of-the-art neural networks and push the boundaries of AI research and engineering for autonomous driving
• Work on cutting-edge AI technologies, including large multimodal models, multi-task learning, and generative models
• Optimize model efficiency for large-scale training and inference
• Collaborate with cross-functional teams to integrate foundation models into autonomous driving systems