Applied Intuition
Research Engineer - 3D Vision and Generation, Self-Driving
About this role
Applied Intuition seeks a Research Engineer to advance 3D vision and generative AI technologies for autonomous vehicles and robotics. You'll conduct cutting-edge research on topics like Gaussian splatting and foundation models, collaborate with expert researchers, and deploy algorithms into production systems serving top global automakers.
What you'll do
- Conduct research on 3D vision, foundation models, and world models with applications to autonomous driving
- Publish high-quality research at top-tier conferences alongside Research Scientists and interns
- Deploy end-to-end algorithms with engineering teams for production autonomous vehicles
- Develop neural simulation and generation tools supporting autonomy development
- Work independently and collaboratively on research and engineering projects
- Contribute to best practices in autonomy and robotics within a customer-focused environment
What they're looking for
- 3D reconstruction or Gaussian splatting
- Multi-modal and 3D foundation models
- Python and PyTorch
- Computer vision
- Distributed machine learning model training
- Robotics systems knowledge
- End-to-end autonomous driving or robotics models
- Video generation or world foundation models
Benefits
- Base salary, equity (options/RSUs), and comprehensive health insurance
- Dental, vision, life, and disability coverage
- 401k retirement with employer match
- Learning and wellness stipends
- Paid time off
- Access to millions of miles of fleet data and diverse autonomous systems
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Applied Intuition
Applied Intuition builds autonomous vehicle and defense systems software, including motion planning algorithms, simulation infrastructure, and autonomy integration platforms for aerial and ground platforms. The company is hiring for security engineers, robotics/autonomy software engineers, hardware-in-the-loop specialists, and IT operations professionals to support its growing physical AI operations.
- Website
- appliedintuition.com
Likely interview questions
- Can you walk us through a specific project where you worked with 3D reconstruction, Gaussian splatting, or 3D foundation models? What were the key challenges and how did you solve them?
- Describe your experience deploying machine learning models in production systems. How have you handled scaling challenges with distributed training?