Multimodal AI Engineer (Robotics & Foundation Models)

Asiacruit BPO, Inc. is hiring a Multimodal AI Engineer to build and adapt Vision-Language-Action (VLA) foundation models for autonomous robotic systems. This hybrid role in the United States focuses on bringing together multimodal perception, action generation, and deployment-ready optimization for edge AI environments.

What you will do

• Develop, fine-tune, and adapt Vision Transformers (ViTs) and other multimodal foundation models for robotic perception and action generation.
• Implement action-chunking approaches for long-horizon planning, including Action Chunking with Transformers (ACT) and Diffusion Policy.
• Build multi-camera fusion pipelines that combine visual inputs into coherent representations to support VLA perception and decision-making.
• Create and optimize multimodal feature extraction pipelines that connect visual perception with downstream robotic planning and action-generation modules.
• Integrate computer vision capabilities such as face recognition and perimeter threat assessment into multimodal AI workflows.
• Apply parameter-efficient fine-tuning (PEFT), including LoRA, to adapt foundation models to specific robotic applications.
• Use Transformer architectures, visual embeddings, and vector search techniques to improve multimodal representations and information retrieval.
• Profile and optimize models using pruning and other model compression methods to improve inference efficiency.
• Prepare and optimize model weights for deployment with NVIDIA TensorRT and related inference optimization tooling.
• Evaluate 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.

What you bring

• Master’s degree or Ph.D. in Computer Science, Artificial Intelligence, Robotics, Machine Learning, Computer Vision, or a related field.
• Approximately 3–5 years of relevant experience in multimodal AI, deep learning, computer vision, foundation models, or robotic learning (minimum 3 years required).
• Strong programming skills in Python and hands-on experience with PyTorch.
• Experience developing, training, or fine-tuning Transformer-based architectures, Vision Transformers, or multimodal deep learning models.
• Understanding of vision-language models, multimodal representation learning, and how to integrate visual information into AI decision-making systems.
• Practical experience with PEFT methods such as LoRA or similar techniques.
• Familiarity with robotic learning and action-generation approaches, including ACT, Diffusion Policy, imitation learning, or reinforcement learning.
• Experience with multi-camera perception, visual feature fusion, or comparable computer vision pipelines.
• Knowledge of model profiling and optimization techniques such as pruning and quantization.
• Familiarity with NVIDIA DeepStream and TensorRT or GPU-accelerated inference is an advantage.
• Knowledge of embeddings, vector search, and multimodal data processing is a plus.
• Strong analytical and problem-solving skills, including the ability to run experiments and turn research results into engineering solutions.
• Willingness to work hybrid and be available for on-site work at a designated research location if required for future projects.

Tools and technologies

• Python, PyTorch
• Vision Transformers (ViTs), Transformer architectures
• Action Chunking with Transformers (ACT), Diffusion Policy
• LoRA, Parameter-efficient fine-tuning (PEFT)
• Vector search, pruning, model compression
• NVIDIA TensorRT, NVIDIA DeepStream, GPU-accelerated inference
• Imitation learning, reinforcement learning

Why this role

• Work on emerging technologies at the intersection of multimodal AI, foundation models, and robotics.
• Develop AI systems that connect visual perception, intelligent planning, and physical execution.
• Explore VLA model adaptation, action generation, and multimodal learning approaches.
• Contribute to real-world AI applications involving autonomous systems and intelligent perception.
• Collaborate with researchers and engineers on challenging AI R&D projects.