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Multimodal AI Engineer (Robotics & Foundation Models)

Asiacruit BPO, Inc. United States

United States

Today

Full time Artificial Intelligence Computer Vision Model Deployment Engineer

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Job Description

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.

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