AI Vision R&D Engineer — Sports Tech

The Grind Limited; Hong Kong; On-site; HKD 420,000 - 780,000; Full time

Job summary

The Grind Limited, a sports tech startup in Hong Kong, seeks an AI Computer Vision R&D Engineer eager to experiment with AI solutions for sports. You will research, prototype, and benchmark deep learning models for detection, tracking, and action recognition, and build robust data pipelines for video dataset curation and quality control.

Collaborate with engineers to translate findings into structured technical documentation, document methodologies and results for milestones, and apply a

Qualifications

• Bachelor’s degree in Computer Science, AI, or related field.
• Experience with AI/ML frameworks.
• Strong foundations in Computer Vision and Deep Learning principles.
• Proficient in Python and standard frameworks (e.g., PyTorch, OpenCV, NumPy).
• Experience working with core vision tasks (e.g., object detection, tracking, action localization, or image/video segmentation).
• Familiarity with Git, reproducible experimentation, and clean coding practices.
• Strong analytical mindset and structured problem-solving approach.

Responsibilities

• Research, prototype and benchmark deep learning algorithms for CV tasks: detection, tracking, and action recognition.
• Build data pipelines for video datasets, annotation management and QC.
• Run error analysis and ablation studies to identify bottlenecks.
• Document methodologies, results and findings for milestone progress reports.
• Collaborate with engineering to translate research into technical documentation.

Skills

Python

Computer Vision

Deep Learning

PyTorch

OpenCV

NumPy

Education

Bachelor’s degree in Computer Science, AI, or related field

Tools

Git

Job description

The Grind Limited, a sports tech startup in Hong Kong, seeks an AI Computer Vision R&D Engineer eager to experiment with AI solutions for sports. You will research, prototype, and benchmark deep learning models for detection, tracking, and action recognition, and build robust data pipelines for video dataset curation and quality control.

Collaborate with engineers to translate findings into structured technical documentation, document methodologies and results for milestones, and apply a