Senior Applied AI Engineer

Responsibilities

• Design and develop multimodal transformer architectures that fuse camera, LiDAR, and radar into unified representations
• Research and implement cross-modal attention mechanisms, token fusion strategies, and efficient multi-stream tokenization
• Build scalable training pipelines for large-scale multimodal transformers across massive real-world datasets
• Explore self-supervised and contrastive pretraining objectives that learn transferable multimodal representations
• Optimize transformer models for real-time inference under latency and compute constraints

Requirements

• BS, MS, or PhD in AI, Computer Science, or a related field
• 4+ years experience with transformer architectures, particularly in multimodal or multi-stream settings
• Familiarity with cross-attention, token fusion, or modality alignment techniques
• Proficiency in Python and deep learning frameworks like PyTorch or TensorFlow
• Strong understanding of scalable training for large models, including distributed training and mixed-precision optimization

About the Company

Kodiak Robotics, Inc. is a leader in autonomous ground transportation, developing an AI-powered technology stack purpose-built for commercial trucking and the public sector.