Machine Learning

2 days ago


Mountain View, California, United States Aurora Innovation Full time
Aurora Innovation Overview
Aurora Innovation is a leader in self-driving technology, delivering safe, efficient, and reliable transportation solutions. Our mission is to make transportation safer, more accessible, and increasingly efficient than ever before. We are committed to providing access to anyone who seeks information from our website and invite candidates with diverse backgrounds to apply.

Job Description
We are seeking a talented Machine Learning / Computer Vision Developer to join our Perception Team. You will work collaboratively with other engineers to develop innovative solutions for complex scenarios, such as on highways and city streets. Your expertise in machine learning, computer vision, and software engineering will help us drive performance improvements and own validation for a perception capability.

Key Responsibilities
• Collaborate with the Perception autonomy team to research and develop state-of-the-art deep learning/machine learning models
• Design, prototype, and evaluate machine learning algorithms and DNN architectures for detection of traffic actors, their action recognition, and semantic understanding of various traffic scenes
• Integrate, test, and deploy production-ready solutions into the production code that powers our self-driving system

Benefits
Aurora Innovation offers competitive medical, dental, and vision benefits, as well as additional healthcare support including medical transportation reimbursement, fertility, adoption, and surrogacy benefits. Our Learning and Development offerings include Aurora Academy, where our people learn, develop, and practice the essential skills that drive our mission.

Company Culture
We operate with integrity, set outrageous goals, and build a culture where we win together – all without any jerks. Our team is empowered to take an active role in building an inclusive, collaborative, and unified culture that leverages our diverse strengths, perspectives, and backgrounds.

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