Ming Liu, Developer in Singapore, Singapore
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Ming Liu

Verified Expert  in Engineering

AI Engineer and Developer

Location
Singapore, Singapore
Toptal Member Since
June 16, 2021

Ming is a machine learning engineer specializing in recommender and anti-fraud systems. He has been working in data science and AI for the past four years, including building and improving the recommender system for Shopee and TikTok, which have hundreds of millions of users and items. Ming's industry experience is backed by a master's degree in computer science.

Portfolio

TikTok
C++, Python, Recommendation Systems, Neural Networks
Shopee
Algorithms, Anti-fraud, Graph Databases, Apache Airflow, PySpark, eCommerce
Shopee
Python, Recommendation Systems, Graph Databases, Neural Networks...

Experience

Availability

Part-time

Preferred Environment

Linux, PySpark, Python, Keras, TensorFlow, Docker

The most amazing...

...system I've developed was for TikTok, and it's popularity is due, in large part, to the successful recommender system I built.

Work Experience

Machine Learning Engineer

2021 - PRESENT
TikTok
  • Improved TikTok's live streaming recommendation system by adding multiple retrieval and recall queues.
  • Improved TikTok's live streaming recommendation system by using more complex neural networks and adding more concrete objectives and targets for the system.
  • Increased TikTok's overall live-streaming revenue by applying multiple revenue strategies, implementing those strategies in the system, and conducting A/B tests.
Technologies: C++, Python, Recommendation Systems, Neural Networks

Senior Data Scientist

2019 - 2020
Shopee
  • Led a team of five (plus two interns) to build and refactor a unique user identification system, which included setting up a graph database and building a data pipeline and business engine.
  • Collaborated with team members to build an interactive data dashboard for managers and other colleagues to explore the user-user relationship.
  • Assisted team members with improving JanusGraph, an open-source tool, according to business requirements.
Technologies: Algorithms, Anti-fraud, Graph Databases, Apache Airflow, PySpark, eCommerce

Data Scientist

2018 - 2019
Shopee
  • Increased the click-through rate on the homepage recommender system by 50% by using deep neural networks.
  • Increased the conversion rate on the shopping cart page by around 20% by using online learning to build a new module called "You may also like."
  • Created graph database clusters and used the database to store buyer-seller relationships.
Technologies: Python, Recommendation Systems, Graph Databases, Neural Networks, Deep Neural Networks, Machine Learning, eCommerce

Machine Learning Engineer

2017 - 2017
XRVision
  • Built object detections for CCTV cameras for buildings, factories, and roads, with more than 70% accuracy.
  • Developed image classification models for the company's core product that has the general ability to recognize gender, facial hair, eyeglasses, and other features, with an overall accuracy of 90%.
  • Built facial recognition models for several companies' check-in systems.
Technologies: Python, C++, Computer Vision, Object Detection, Facial Recognition, Machine Learning, Anti-fraud

Enhanced Recommendation System for eCommerce Website

As a machine learning engineer, I collaborated with a data engineer and a software engineer to enhance the recommendation system of the Shopee eCommerce website. By training neural networks on customer browsing and purchasing data, I used the deep learning method to improve the recommender system and improved the click-through rate by 50%+ compared to the previous baseline.

Object Detection for CCTV Cameras

An object detection neural network that allows CCTV cameras to detect different types of cars and people. It also applies facial recognition if the faces are big enough on the screen. At 10 frames per second, this function works in near real time.

Refactored Unique User Identification System for eCommerce Website

Led a team of five (plus two interns) to build and refactor a unique user identification system. This included setting up a graph database and building an interactive query graph dashboard, a data pipeline, and a business engine.

This project helps the company understand which users are highly likely to be fraudsters who created multiple accounts to get benefits. By using Go to replace Python, the system performance improved by 80% in terms of increased speed and decreased memory.
2017 - 2018

Master's Degree in Computer Science

National University of Singapore - Singpore

2012 - 2016

Bachelor's Degree in Information Technology

Southeast University - China

Libraries/APIs

PySpark, TensorFlow, Keras, OpenCV

Tools

Apache Airflow

Languages

Python, SQL, Go, C++

Frameworks

Spark, Flask

Platforms

Linux, Docker, Amazon Web Services (AWS)

Storage

Graph Databases, Neo4j, Elasticsearch

Other

Recommendation Systems, Machine Learning, Deep Learning, Deep Neural Networks, Data Analysis, Data Visualization, Data Structures, Algorithms, Neural Networks, eCommerce, Computer Vision, Anti-fraud, Object Detection, Facial Recognition, Team Leadership, System Design

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