Research and develop algorithms for deep-learning-based methods for prediction and planning. Design efficient model architectures that can run in real-time on the computing platform of our vehicles. Develop offline data-driven ML infrastructure for fast adaptation of the planning ML models. Deliver on target planning SW and closely work with the perception team to achieve the most intelligent autonomous driving systems. Work with massive field-testing data to continuously improve autonomous driving technologies. Designing, running, and analyzing experiments and testing to evaluate the efficiency of our solutions on real-world data. Partnering with system software engineering specialists to ship industrial strength ML models. Communicating and collaborating with multi-functional teams.

Minimum Skill Requirements: MS or PhD level education in Engineering or Computer Science with a focus on Deep Learning, Artificial Intelligence, or a related field, or equivalent experience. Strong experience in applied deep learning including model architecture design, model training, data mining, and data analytics. 1-3 years + of experience working with DL frameworks such as PyTorch, Tensorflow. Open to fresh graduates with strong research background. Strong Python programming experience with software design skills. Solid understanding of data structures, algorithms, code optimization and large-scale data processing. Excellent problem-solving skills. Preferred Skill Requirements: Hands on experience in developing DL based planning engine for autonomous driving. Experience in applying CNN/RNN/GNN, attention model, or time series analysis to real world problems. Experience in other ML/DL applications, e.g., reinforcement learning. Experience in DL model deployment and optimization tools such as ONNX and TensorRT.