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Applied IntuitionApplied IntuitionSunnyvale, CA

Research Scientist - World-Action Foundation Model, Robotics

Conducts research on world-action foundation models for robotics and autonomous driving, focusing on 3D vision, multi-modal pretraining, and Gaussian splatting. Requires MSc/PhD in ML/CV, strong publication record, and expertise in Python/PyTorch.

126k – 423k
On-siteEntry levelAI Research

About the role

Responsibilities

  • Conduct research on pretraining world-action foundation model with various world modalities including vision and physics associated with ego actions, serving the purpose for both robot action and simulation world generation.
  • Dive into relevant topics such as vision-physics modality association, feed-forward Gaussian splatting, world foundation model, human data incorporation, language modality, spatial reasoning, deformable object modeling and simulation.
  • Explore 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.
  • 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.

Requirements

  • Strong research record in the fields of 3D vision, reconstruction and generation for robotics and autonomous systems, 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.

Nice to Have

  • Hands-on experience in at least one of the following fields:
    • 3D foundation model and pretraining
    • Multi-modal foundation model
    • Feed-forward Gaussian splatting and reconstruction
    • World foundation model and generation
    • 3D/multi-view end-to-end models for autonomous driving or robotics
    • Human data processing and incorporation

Compensation

  • Base salary range: $126,000 - $423,000 USD annually.
  • Equity, comprehensive health/dental/vision/life/disability insurance, 401k with employer match, learning/wellness stipends, paid time off.

Skills

PythonPyTorchComputer Vision3D VisionMachine LearningRoboticsDistributed TrainingGaussian SplattingMulti-Modal ModelsFoundation Models

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