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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 Engineering

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Job Description

Multimodal AI Engineer role focused on developing Vision-Language-Action (VLA) foundation models for autonomous robotic systems in a hybrid United States setting.

Responsibilities

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 to combine visual inputs into coherent representations for VLA perception and decision-making.
Create and optimize multimodal feature extraction pipelines that connect visual perception to downstream robotic planning and action-generation systems.
Integrate computer vision workflows, including face recognition and perimeter threat assessment, into multimodal AI pipelines.
Apply parameter-efficient fine-tuning (PEFT) approaches, including LoRA, to adapt foundation models for specific robotic applications.
Use Transformer architectures, visual embeddings, and vector search techniques to improve multimodal representation and information retrieval.
Profile and optimize multimodal models using pruning and other model compression techniques to improve inference efficiency.
Prepare and optimize model weights for deployment using NVIDIA TensorRT and related inference optimization tools.
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.

Requirements

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

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 Join

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

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