AI / Computer Vision Engineer Lead

BinaBola; Kota Bandung; On-site; IDR 200.000.000 - 300.000.000; Full time

Job summary

A leading sports-tech company in Indonesia is seeking an AI / Computer Vision Engineer Lead to drive the design and implementation of AI-powered analytics for football match analysis. This role involves coordinating cross-functional collaboration and ensuring reliable, scalable solutions. Candidates should have 4-5+ years of experience in computer vision and deep learning, strong expertise in object tracking and OpenCV, and the ability to deploy models in production environments.

Qualifications

• 4–5+ years of experience in computer vision and/or deep learning.
• Strong expertise in Computer Vision and Pattern Recognition.
• Experience deploying machine learning models into production environments.

Responsibilities

• Lead the development and implementation of AI-based computer vision systems.
• Design and oversee end-to-end analysis pipelines for football match videos.
• Guide the integration of computer vision outputs with game context and tactical insights.

Skills

Computer Vision

Deep Learning

Object Tracking Algorithms

OpenCV

Linux/Ubuntu

Tools

StrongSORT

YOLO

Job description

Company Description

PT Bina Olahraga Teknologi (BinaBola) is a sports-tech company building AI-powered systems to transform how football is analyzed in Indonesia. Our core mission is to automatically convert raw match video into accurate, contextual, and actionable performance data that empowers coaches, analysts, academies, and clubs to make data-driven decisions.

We develop a full end-to-end computer vision pipeline, including multi-object detection and tracking of players and the ball, action and event recognition (passing, shooting, tackling, duels), and precise tactical and positional mapping using homography and 2D pitch coordinates. Our focus is not on simple score applications, but on production-grade AI systems that handle real-world challenges such as player occlusion, high-resolution video processing, low-latency inference, and scalable deployment.

At BinaBola, we are building the technical foundation for the future of Indonesian football. We seek problem solvers who care deeply about code quality, system architecture, and real-world impact—engineers who want to push computer vision and AI beyond research and into reliable, field-ready solutions.

Role Description

As an AI / Computer Vision Engineer Lead, you will lead the technical development of an intelligent sports analytics platform built by PT Bina Olahraga Teknologi (BinaBola). This role plays a critical part in driving the design, implementation, and optimization of AI-powered computer vision systems for football match analysis.

You will coordinate cross-functional collaboration, ensure technical excellence, and guide the team in delivering reliable, scalable, and production-ready solutions.

Qualifications

• Strong expertise in Computer Vision and Pattern Recognition
• 4–5+ years of experience in computer vision and/or deep learning
• Experience with object tracking algorithms, such as StrongSORT
• Proficient in OpenCV for image and video processing
• Experience deploying machine learning models into production environments
• Strong understanding of computer vision fundamentals, including homography, perspective transformation, and camera geometry
• Comfortable working in Linux/Ubuntu environments
• Experience with Dicoding programs or courses is a plus

Responsibilities

• Lead the development and implementation of AI-based computer vision systems to analyze football match videos into actionable team and player performance statistics, including positional data, ball action statistics, and event-based analysis.
• Design and oversee end-to-end analysis pipelines, from player and ball detection and tracking, action recognition (passing, shooting, dribbling, interception, duels, etc.), to the generation of advanced statistical metrics for end users.
• Guide the integration of computer vision outputs with game context and tactical insights, ensuring the generated data is highly relevant for coaches, analysts, and team management.
• Take ownership of and lead the AI model lifecycle management, including architecture design, training and evaluation (YOLO, StrongSORT, and supporting models), as well as performance optimization, efficiency, and scalability for production deployment.