Job Area: Engineering Group, Engineering Group > Systems Engineering General Summary: Summary - We are seeking experts with a robust background in the field of deep learning (DL) to design state-of-the-art low-level perception (LLP) as well as end-to-end AD models, with a focus on achieving accuracy-latency Pareto optimality. This role involves comprehending state-of-the-art research in this field and deploying networks on the Qualcomm Ride platform for L2/L3 Advanced Driver Assistance Systems (ADAS) and autonomous driving. The ideal candidate must be well-versed in recent advancements in Vision Transformers (Cross-attention, Self-attention), lifting 2D features to Bird's Eye View (BEV) space, and their applications to multi-modal fusion. This position offers extensive opportunities to collaborate with advanced R&D teams of leading automotive Original Equipment Manufacturers (OEMs) as well as Qualcomm's internal stack teams. The team is responsible for enhancing the speed, accuracy, power consumption, and latency of deep networks running on Snapdragon Ride AI accelerators. A thorough understanding of machine learning algorithms, particularly those related to automotive use cases (autonomous driving, vision, and LiDAR processing ML algorithms), is essential. Research experience in the development of efficient networks, various Neural Architecture Search (NAS) techniques, network quantization, and pruning is highly desirable. Strong communication and interpersonal skills are required, and the candidate must be able to work effectively with various horizontal AI teams. Minimum Qualifications: Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 1+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.ORMaster's degree in Computer Science, Engineering, Information Systems, or related field and 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.ORPhD in Computer Science, Engineering, Information Systems, or related field. Preferred Qualifications: Good at software development with excellent analytical, development, and problem-solving skills. Strong understanding of Machine Learning fundamentals Hands-on experience with deep learning network design and implementation. Ability to define network from scratch in PyTorch, ability to add new loss function, modify network with torch.fx. Adept at version control system like GIT. Experience in neural network quantization, compression, pruning algorithms. Experience in deep learning kernel/compiler optimization Strong communication skills Principal Duties and Responsibilities: Applies Machine Learning knowledge to extend training or runtime frameworks or model efficiency software tools with new features and optimizations. Models, architects, and develops machine learning hardware (co-designed with machine learning software) for inference or training solutions. Develops optimized software to enable AI models deployed on hardware (e.g., machine learning kernels, compiler tools, or model efficiency tools, etc.) to allow specific hardware features; collaborates with team members for joint design and development. Assists with the development and application of machine learning techniques into products and/or AI solutions to enable customers to do the same. Develops, adapts, or prototypes complex machine learning algorithms, models, or frameworks aligned with and motivated by product proposals or roadmaps with minimal guidance from more experienced engineers. Conducts complex experiments to train and evaluate machine learning models and/or software independently.
