Kodiak World Model Research Scientist at Kodiak leading design and development of generative world models for autonomous driving. Focus on multi-sensor, multi-view, temporally coherent scenario generation conditioned on actions and 3D context.
Responsibilities
Design and train generative world models that synthesize realistic multi-camera video and LiDAR conditioned on ego trajectories, 3D scene context, and text
Research and implement conditional diffusion architectures for driving, including spatiotemporal attention, latent space design, and action-conditioned generation
Develop techniques for multi-view geometric consistency in generated outputs, drawing on neural rendering, cross-view attention, and 3D-aware generative approaches
Build methods for joint multimodal generation that maintain cross-sensor consistency between camera, LiDAR, and radar outputs
Design evaluation frameworks that measure world model quality beyond pixel-level metrics, including scenario fidelity and autoregressive stability
Scale training pipelines to learn from thousands of hours of real-world driving data across multiple sensor modalities
What you'll bring:
PhD in Computer Science, AI, Robotics, or a related field, with a focus on generative modeling, neural rendering, or video synthesis
Strong publication record or demonstrated research contributions in diffusion models, video generation, neural radiance fields, 3D-aware generative models, or world models
Experience with neural rendering and view synthesis and an understanding of multi-view geometric consistency
Proficiency working with multimodal sensor data (camera, LiDAR, radar) and familiarity with 3D representations such as BEV grids, voxel fields, or tri-planes
Strong implementation skills in Python and PyTorch, with experience training large generative models at scale using distributed training
Qualification
PhD in Computer Science
Required
PhD in Computer Science, AI, Robotics, or related field with focus on generative modeling, neural rendering, or video synthesis
Strong publication record or demonstrated research contributions in diffusion models, video generation, neural radiance fields, 3D-aware generative models, or world models
Experience with neural rendering and view synthesis
Understanding of multi-view geometric consistency
Proficiency with multimodal sensor data (camera, LiDAR, radar)
Familiarity with 3D representations such as BEV grids, voxel fields, or tri-planes
Strong implementation skills in Python and PyTorch
