Tóm tắt công việc
Advanced Model Research: Lead the R&D of high-performance models for Fine-Grained Visual Categorization (FGVC) and Hierarchical Classification, focusing on detecting complex object attributes and nuances in unconstrained environments using both Supervised and Self-Supervised Learning (SSL).
Generative AI & VLM: Spearhead the exploration of Vision Language Models (VLM) and CLIP-based architectures to enable Text-to-Image / Image-to-Image retrieval and Open-Set Recognition, moving beyond fixed-class taxonomy.
Agentic Data Workflows: Architect and implement AI Agentic Workflows to automate the data lifecycle. Design intelligent agents for autonomous data mining, auto-captioning, and Synthetic Data Generation to overcome long-tail edge cases and reduce reliance on manual annotation.
Production-Grade AI: Drive the transition of models from research prototypes to production. Collaborate with Embedded
AI Engineers to guide the distillation and quantization of heavy Foundation Models for deployment on edge devices.
Deep Learning Mastery: Expert-level understanding of Deep Learning architectures, specifically Transformers (ViT, Swin), CNNs (EfficientNet, ResNet), and Object Detection/Segmentation frameworks.
Generative AI & Multi-modal: Hands-on experience with Multimodal Foundation Models (CLIP, LLaVA, etc.) and Generative approaches (Diffusion Models, GANs) for data synthesis.
Data-Centric AI: Proven track record in building automated data pipelines. Experience with Active Learning, Auto-labeling, or building "Agentic" systems for data curation is a critical differentiator.
Frameworks: Proficiency in PyTorch (preferred) or TensorFlow, and strong programming skills in Python and C++.
Practical Implementation: Strong ability to write clean, maintainable code and experience with MLOps practices (CI/CD for ML, Experiment Tracking).
Education: Bachelor's Degree or higher in Computer Science, Artificial Intelligence, Computer Vision, Mathematics, or a related technical field.
7+ years of relevant industry experience in Computer Vision and Machine Learning.
Proven track record of leading technical initiatives and successfully deploying AI models into commercial products (0-to-1 product lifecycle experience is highly preferred).
Demonstrated ability to lead and mentor technical teams in a fast-paced R&D environment.