Research Scientist- 3D Vision and Generation, Self-Driving

Research Scientist- 3D Vision and Generation, Self-Driving

Sunnyvale, California, United States

Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy.

Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.;
San Diego;
Ft. Walton Beach, Florida;
Ann Arbor, Michigan;
London;
Stuttgart;
Munich;
Stockholm;
Bangalore;
Seoul; and Tokyo. Learn more  .

We are an in‑office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments.

We are looking for multiple passionate Research Scientists to join the Research Group at Applied Intuition. The mission of the group is to create cutting‑edge technology enabling next‑generation physical AI, with emphasis on the two most challenging applications reshaping our everyday life: end‑to‑end autonomous driving and robotic generalist. We have a group composed of leading experts from top institutions and companies, recognized for their exceptional academic and industry contributions—including eight Best Paper awards at premier conferences and journals such as CVPR and ICRA.

Learn more at  .

Supported by industry‑leading tools and infra, researchers can access millions of miles of data from large fleets, and deploy methods they develop into various autonomous and robotic systems including self‑driving cars/trucks, autonomous mining/construction machines, humanoid robots and dexterous hands. In addition to your research contributions, you will contribute to and learn from best practices in the autonomy and robotics industries within our fast‑paced and customer‑focused culture.

Improvements deployed to our system immediately help our customers with their programs and deliver value to our business.

We are open to all years of experience as long as the necessary requirements are met, including those with potential Tech Lead and Manager capacity.

• Conduct research on 3D vision related topics including 3D/world‑action foundation model, multi‑modal pretraining, feed‑forward Gaussian splatting, world foundation model with applications to autonomous driving, and fundamental topics on 3D vision and generation with broader applications
• Work closely with other Research Scientists and interns on research publications for submission to top‑tier conferences
• Collaborate with Research Engineers and engineering teams to test and deploy algorithms to our autonomy and simulation products

• Strong research record in the fields of 3D vision, reconstruction and generation for autonomous systems and robotics, with publications in top‑tier conferences or journals in the fields of computer vision, machine learning, and robotics
• MSc or PhD in machine learning and computer vision with autonomy and robotics applications or closely‑related fields
• Passion for next‑generation, scalable autonomy and robotics for real‑world systems
• Strong research skills and the ability to work both independently and collaboratively on projects
• Technical experience in:
  Python, Pytorch, computer vision, robotics systems, and distributed machine learning model training

Hands‑on experience in at least one of the following fields:

• 3D foundation model and pretraining
• Feed‑forward Gaussian…