About the role
<div class="content-intro"><p>Kodiak Robotics, Inc. was founded in 2018 and has become a leader in autonomous ground transportation committed to a safer and more efficient future for all. The company has developed an artificial intelligence (AI) powered technology stack purpose-built for commercial trucking and the public sector. The company delivers freight daily for its customers across the southern United States using its autonomous technology. In 2024, Kodiak became the first known company to publicly announce delivering a driverless semi-truck to a customer. Kodiak is also leveraging its commercial self-driving software to develop, test and deploy autonomous capabilities for the U.S. Department of Defense.</p></div><div><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">Kodiak is building AI that doesn't just perceive the world, it learns how the physics of the world works. We are developing large-scale generative world models that learn to predict realistic, physically consistent futures from real-world sensor data. This capability serves as the foundation for scalable closed-loop training, validation, and long-tail scenario generation, and is distilled into the onboard models that drive our autonomous trucks. We are looking for a research scientist to lead the design and development of world models capable of generating multi-sensor, multi-view, temporally coherent driving scenarios conditioned on actions, 3D scene context, and text.</span></div> <div>&nbsp;</div> <div><strong><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">In this role, you will:</span></strong></div> <ul> <li style="font-family: helvetica, arial, sans-serif; font-size: 12pt;"><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">Design and train generative world models that synthesize realistic multi-camera video and LiDAR conditioned on ego trajectories, 3D scene context, and text</span></li> <li style="font-family: helvetica, arial, sans-serif; font-size: 12pt;"><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">Research and implement conditional diffusion architectures for driving, including spatiotemporal attention, latent space design, and action-conditioned generation</span></li> <li style="font-family: helvetica, arial, sans-serif; font-size: 12pt;"><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">Develop techniques for multi-view geometric consistency in generated outputs, drawing on neural rendering, cross-view attention, and 3D-aware generative approaches</span></li> <li style="font-family: helvetica, arial, sans-serif; font-size: 12pt;"><span style="font-