Machine Learning Engineer - TikTok E-Commerce

The E-Commerce Risk Control (ECRC) team is missioned:

• To protect TikTok E-Commerce users, including and beyond buyer, seller, creator;
• By securing the integrity of our ecommerce ecosystem and providing a safe shopping experience on the platform;
• Through building infrastructures, platforms and technologies, as well as collaborating with many cross-functional teams and stakeholders.

The ECRC team works to minimize the damage of inauthentic behaviors on TikTok E-Commerce platforms (e.g. TikTok Shop, Jumanji, Fanno), covering multiple classical and novel community and business risk areas such as account integrity, incentive abuse, malicious activities, brushing, click-farm, information leakage etc.

In this team you'll have a unique opportunity to have first-hand exposure to the strategy of the company in key security initiatives, especially in building scalable and robust, intelligent and privacy-safe, secure and product-friendly systems and solutions. Our challenges are not some regular day-to-day technical puzzles -- You'll be part of a team that's developing novel solutions to first-seen challenges of a non-stop evolvement of a phenomenal product eco-system. The work needs to be fast, transferrable, while still down to the ground to making quick and solid differences.

Responsibilities

• Build rules, algorithms and machine learning models, to respond to and mitigate business risks in TikTok products/platforms. Such risks include and are not limited to account integrity, scapler, deal-hunter, malicious activities, brushing, click-farm, information leakage etc. - Analyze business and security data, uncover evolving attack motion, identify weaknesses and opportunities in risk defense solutions, explore new space from the discoveries. - Define risk control measurements. Quantify, generalize and monitor risk related business and operational metrics. Align risk teams and their stakeholders on risk control numeric goals, promote impact-oriented, data-driven data science practices for risks. - Support the production of scalable and optimised AI/machine learning (ML) models - Focus on building algorithms for the extraction, transformation and loading of large volumes of realtime, unstructured data to deploy AI/ML solutions from theoretical data science models. - Run experiments to test the performance of deployed models, and identifies and resolves bugs that arise in the process.