Multimodal AI Engineer (Robotics & Foundation Models)

Asiacruit BPO, Inc.; United States; Hybride; USD 120 000 - 180 000; Plein temps

Résumé du poste

Asiacruit invites applications for a Multimodal AI Engineer to architect Vision-Language-Action models for autonomous robotic systems. You will refine perception, planning, and action-generation modules across edge deployments and collaborate with AI researchers and robotics engineers to translate research into practical AI capabilities.

The role focuses on fine-tuning multimodal models, building multi-camera perception pipelines, and optimizing models for deployment with state-of-the-art

Qualifications

• Master's degree or Ph.D. in Computer Science, AI, Robotics, ML, CV, or related field.
• Approximately 3–5 years of relevant experience in multimodal AI, deep learning, CV, foundation models, or robotic learning.
• Strong programming skills in Python and hands-on experience with PyTorch.
• Experience with Transformer-based architectures and multimodal models.
• Understanding of vision-language models and multimodal representation learning.
• Familiarity with LoRA or other PEFT techniques.

Responsabilités

• Develop, fine-tune, and adapt ViTs and multimodal models for robotic perception and action generation.
• Implement action-chunking architectures to support long-horizon planning.
• Build multi-camera fusion pipelines for VLA perception and decision-making.
• Develop multimodal feature extraction pipelines linking perception to planning and action.
• Integrate computer vision capabilities (face recognition, perimeter threat assessment) into multimodal workflows.
• Apply PEFT techniques like LoRA to adapt models.
• Work with transformers, visual embeddings, and vector search to improve multimodal representations.
• Profile and optimize models via pruning and other compression methods.
• Prepare model weights for deployment using NVIDIA TensorRT and related tools.
• Evaluate model performance through experiments and benchmarking.
• Collaborate with researchers across VLA, robotics controls, and edge AI.

Connaissances

Python

PyTorch

Transformer architectures

Vision Transformers

PEFT (LoRA)

Edge AI

Vector search

Formation

Master's degree in CS/AI/Robotics

Ph.D. in a related field (preferred)

3–5 years multimodal AI/robotics experience

Outils

NVIDIA TensorRT

NVIDIA DeepStream

PyTorch

Description du poste

About Asiacruit

At Asiacruit, we connect top talent with forward-thinking organizations across industries. Our mission is to help businesses grow through smart, strategic, and people-focused solutions. We support companies by providing high-quality talent for local and global markets.

Position Overview

We are looking for a Multimodal AI Engineer to develop and adapt Vision-Language-Action (VLA) foundation models for autonomous robotic systems. You will work at the intersection of multimodal perception, foundation models, and robotic learning, helping AI systems interpret visual inputs, plan complex tasks, and translate instructions into physical actions.

This role focuses on fine-tuning multimodal models, developing multi-camera perception pipelines, implementing action-generation architectures, and optimizing models for deployment on edge AI platforms. You will collaborate with AI researchers and robotics engineers to turn research concepts into practical, high-performance AI capabilities.

Key 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 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.

Qualifications

• 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.

• 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 the integration of visual information into AI decision‑making systems.

• Practical experience with parameter‑efficient fine‑tuning methods, such as LoRA or other PEFT 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 related computer vision pipelines.

• Understanding of model profiling, pruning, quantization, or other model optimization techniques.

• Familiarity with NVIDIA DeepStream, 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, with the ability to conduct experiments and translate research findings into practical engineering solutions.

• Willingness to work in a hybrid arrangement and be available for on‑site work at a designated research location, if required for future projects. The specific location and work arrangements will be communicated once confirmed.

Why Join?

• 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 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 working on challenging AI research and development projects.