Staff Software Engineer, Perception

Job description

Job responsibilities

• Tackle end-to-end perception challenges, including tracking dynamic actors, recognizing actions and attributes, and developing deep semantic understanding of diverse traffic environments.
• Research and develop state-of-the-art deep learning and machine learning models to enhance perception performance in challenging, real-world scenarios, such as highway and urban driving, long-range multi-sensor…
• Collaborate cross-functionally to iterate on the Perception system, translating insights from on-road and simulated testing into robust, high-quality, and well-tested software solutions.
• Drive the software development lifecycle from prototype to production, focusing on large-scale data analysis and the deployment of performing, production-ready solutions on the real-time Aurora Driver platform.
• Contribute to architectural strategy and team collaboration, ensuring our perception systems remain scalable, efficient, and at the forefront of autonomous driving technology.
• Example projects that a candidate could potentially contribute to (list not exhaustive): Design and implementation of state-of-the-art object tracking algorithms.
• Design algorithms to extract, leverage, and fuse complementary information from different sensors.
• Design and implement state-of-the-art ML systems for object tracking and attribute estimation leveraging expertise in recursive state estimation, Hidden Markov models, structured prediction, and sequential models.

Job requirements

• Master's degree or higher in Computer Science, Robotics, Electrical Engineering, or a related field (or Bachelor with 2+ years of relevant industry experience in software engineering).
• Proficient software engineering skills in C++ and Python, with a focus on writing clean, scalable, and efficient production-quality code.
• Working knowledge in one or more focus areas of ML: deep learning, computer vision, recursive state estimation, structured prediction, optimization, and others.
• Experience with PyTorch, Tensorflow, and/or other deep learning frameworks
• Experience deploying production-grade CV/ML models within real-time computing environments.
• Strong grasp of fundamentals: linear algebra, discrete and continuous optimization, supervised and unsupervised methods, generative and discriminative methods, and Hidden Markov Models.
• Hands-on experience with VLM, foundational or world models.
• Hands-on experience in CUDA or high-performance GPU programming for accelerated computing.