Machine Learning Engineer, Risk Control

Machine Learning Engineer, Risk Control

About the job

TikTok is the leading destination for short-form mobile video. Our mission is to inspire creativity and bring joy. TikTok has global offices including Los Angeles, New York, London, Paris, Berlin, Dubai, Mumbai, Singapore, Jakarta, Seoul, and Tokyo.

The Business Risk Integrated Control (BRIC) team is missioned to:

– Protect TikTok users, including and beyond content consumers, creators, advertisers;

– Secure platform health and community experience authenticity;

– Build infrastructures, platforms and technologies, as well as to collaborate with many cross-functional teams and stakeholders.

The BRIC team works to minimize the damage of inauthentic behaviors on TikTok platforms (e.g. TikTok, CapCut, Resso, Lark), covering multiple classical and novel community and business risk areas such as account integrity, engagement authenticity, anti spam, API abuse, growth fraud, live streaming security and financial safety (ads or e-commerce), 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 machine learning solutions to respond to and mitigate business risks in TikTok products/platforms. Such risks include and are not limited to abusive accounts, fake engagements, spammy redirection, scraping, fraud, etc.

– Improve modeling infrastructures, labels, features and algorithms towards robustness, automation and generalization, reduce modeling and operational load on risk adversaries and new product/risk ramping-ups.

– Uplevel risk machine learning excellence on privacy/compliance, interpretability, risk perception and analysis.

Qualifications:

– Master or above degree in computer science, statistics, or other relevant, machine-learning-heavy majors.

– Strong machine learning background with solid engineering capabilities. Proficiency in at least two of Linux, Hadoop, Hive, Spark, Storm. Proficiency in modern machine theories and applications such as deep neural nets, transfer/multi-task learning, reinforcement learning, time series or graph unsupervised learning.

– Ability to think critically, objectively, rationally. Reason and communicate in result-oriented, data-driven manner. High autonomy.

Preferred Qualifications:

– PhD degree in computer science or statistics. Publications in top academic conferences on relevant machine learning topics about social communities and content platforms (e.g. botnet, interest group mining, fraud detection).

– 2+ years of industry experience (internship included) in machine learning applications. Strong coding (backend, infra, algo) skills and system design ability.

– Strong industry experience justifying great product and/or data science analytical senses.

TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We believe individuals shouldn’t be disadvantaged because of their background or identity, but instead should be considered based on their strengths and experience. We are passionate about this and hope you are too.

TikTok is committed to providing reasonable accommodations during our recruitment process. If you need assistance or accommodation, please reach out to us at USRC@tiktok.com.

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