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-size: 12pt; font-family: helvetica, arial, sans-serif;">Kodiak's autonomy stack is built on AI that fuses diverse sensor streams into a unified, actionable understanding of the world. We are developing GigaFusionNet – a large-scale multimodal transformer that learns rich, joint representations across camera, LiDAR, and radar through attention-based fusion. We are looking for engineers to push the boundaries of how transformer architectures combine and reason over heterogeneous sensor data.This role is open to all levels – from those eager to contribute to cutting-edge research to experts driving innovation at scale.</span><br><br><span style="font-size: 12pt; font-family: helvetica, arial, sans-serif;"><strong>In this role, you will:</strong></span></div> <ul data-list-tree="true" data-indent="0" data-border="0"> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;"><span style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">Design and develop multimodal transformer architectures that fuse camera, LiDAR, and radar into unified representations</span></li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;"><span style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">Research and implement cross-modal attention mechanisms, token fusion strategies, and efficient multi-stream tokenization</span></li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;"><span style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">Build scalable training pipelines for large-scale multimodal transformers across massive real-world datasets</span></li> <li style="font-size: 12pt; font-family: helvetica, arial, sans-serif;"><span style="font-size: 12pt; font-family: helvetica, arial, sans-serif;">Explore self-supervised and contrastive pretraining objectives that learn transferable multimodal representations</span></li