Research Scientist, Applied Intuition

• If you are hands-on and looking for a place to have a multiplying effect on making autonomous systems a reality, Applied is the place for you!
• Strong research skills who can work both independently and collaboratively with peers
• Strong research record in the fields of AI and autonomous systems with publications in top-notch venues or journals in the fields of computer vision, machine learning, robotics, etc
• MSc or PhD in machine learning and computer vision with autonomy and robotics applications or closely related fields
• Passion for next-generation, scalable autonomy with data-driven, differentiable paradigm and large data/model deployment for real-world autonomous systems

Desirable

• The AI Research Team at Applied Intuition conducts world-class research for autonomous systems and closed-loop simulation, and creates scalable autonomy stacks from prototype towards mass production in a data-driven fashion
• You will have access to driving data exploding from millions of miles with various scenarios from a diverse set of sensors to develop industry-leading models and techniques at scale
• Improvements deployed to our system can immediately help our customers with their autonomy programs
• In addition to your research contributions, by working in our dynamic and customer-focused team culture, you will contribute to and learn from best practices in the nascent autonomy industry
• We move fast and we focus on excellence, for our products and for our business
• Conduct research in a focused area among topics including (but not limited to) differentiable/end-to-end autonomy stack with language modality, multi-modal foundation models, close-loop simulation for sensory and behavior generation with generative AI and novel view synthesis, closed-loop training/refinement of differentiable stack
• Work closely with other research scientists and intern students in the AI Research team towards high quality research publications on various topics
• Closely work with and supported by Research Engineers and engineering teams to test and deploy the algorithms created towards industry-leading performance for publications (and products)