June 4
🏢 In-office - Bay Area
• 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
• Education in Engineering or Computer Science with a focus on Deep Learning, Artificial Intelligence, or a related field, or equivalent experience • 2+ years of experience working with DL frameworks such as PyTorch, Tensorflow • 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 • 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 • Hands on experience in developing DL based planning engine for autonomous driving or robotic system • 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
• A fun, supportive and engaging environment • Opportunity to make significant impact on transportation revolution by the means of advancing autonomous driving • Opportunity to work on cutting edge technologies with the top talent in the field • Competitive compensation package • Snacks, lunches and fun activities
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