Data-Centric Vision AI Research Engineer

Dainvi; Hong Kong; On-site; HKD 700,000 - 1,000,000; Full time

Job summary

Dainvi is seeking a Computer Vision ML Research Engineer in Hong Kong to push the frontiers of data-centric AI, feature engineering, and labelless detection. You will explore domain adaptation, active learning, and scalable data pipelines, translating research into robust SDKs and dashboards.

You will collaborate across teams to deploy secure AI cores, optimise performance on heterogeneous hardware, and deliver practical insights from large R&D datasets to stakeholders.

Qualifications

• Degree in Computer Science, Data Science, Statistics, Mathematics, or AI.
• Proficiency in Python and C++, with research-to-SDK encapsulation experience.
• Experience in labelless detection, active learning, and domain adaptation for CV.

Responsibilities

• Research data-centric AI methods and feature engineering for robust labelless detection.
• Develop data orchestration pipelines and backend architectures for scalable AI cores.
• Implement domain adaptation and transfer learning for cross-vertical deployment.
• Build interactive dashboards and visualization for research insights.
• Design secure deployment methods and hardware-locked SDKs.
• Analyse large R&D datasets to drive system throughput and reliability.

Skills

Python

C++

Data-centric AI

Domain Adaptation

Transfer Learning

Domain Generalisation

Backend architectures

Databases

Computer Vision

Unsupervised learning

Active learning

Dashboard visualization

Education

Degree in Computer Science, Data Science, Statistics, Mathematics, or Artificial Intelligence

Tools

Backend frameworks

Databases

SDK development

Job description

Dainvi is seeking a Computer Vision ML Research Engineer in Hong Kong to push the frontiers of data-centric AI, feature engineering, and labelless detection. You will explore domain adaptation, active learning, and scalable data pipelines, translating research into robust SDKs and dashboards.

You will collaborate across teams to deploy secure AI cores, optimise performance on heterogeneous hardware, and deliver practical insights from large R&D datasets to stakeholders.