Develop, fine-tune, and adapt Vision Transformers (ViTs) and multimodal foundation models for robotic perception and action generation.

Implement action-chunking architectures, including Action Chunking with Transformers (ACT) and Diffusion Policy, to support long-horizon robotic task planning.

Build multi-camera fusion pipelines that combine visual inputs into coherent representations for VLA perception and decision-making.

Develop and optimize multimodal feature extraction pipelines that integrate visual perception with downstream robotic planning and action-generation systems.

Integrate computer vision capabilities, including face recognition and perimeter threat assessment, into multimodal AI workflows.

Apply parameter-efficient fine-tuning (PEFT) techniques, including LoRA, to adapt foundation models for specific robotic applications.

Work with Transformer architectures, visual embeddings, and vector search techniques to improve multimodal representation and information retrieval.

Profile and optimize multimodal models through pruning and other model compression techniques to improve inference efficiency.

Prepare and optimize model weights for downstream deployment using NVIDIA TensorRT and related inference optimization tools.

Evaluate model performance through systematic experiments, benchmarking, and iterative improvements.

• Collaborate with researchers and engineering teams across VLA, robotics controls, and edge AI to support end-to-end system development.