Machine Learning Engineer, Account Integrity

Posted yesterday

tiktokSan Jose (CA)

SENIORITY

Manager

Apply

About the role

Machine Learning Engineer, Account IntegrityResponsibilities TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and its offices include New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.
Why Join Us: Creation is the core of TikTok's purpose. Our platform is built to help imaginations thrive. This is doubly true of the teams that make TikTok possible. Together, we inspire creativity and bring joy - a mission we all believe in and aim towards achieving every day. To us, every challenge, no matter how difficult, is an opportunity; to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always. At TikTok, we create together and grow together. That's how we drive impact - for ourselves, our company, and the communities we serve. Join us. 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, 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
Minimum Qualifications: Master or above degree in computer science, statistics, or other relevant, machine-learning-heavy majors. Solid engineering skills. Proficiency in at least two of: Linux, Hadoop, Hive, Spark, Storm. Strong machine learning background. Proficiency or publications in modern machine learning 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: 2 years of industry experience in a software development environment. TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform

Before you apply

Applying takes about a minute. These four things decide how fast it moves after that.

Your profile is current

It's what we read first. Occupations, seniority and locations matter more than a long history.

Two examples you can talk through

Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.

A number in mind

What you're on now and what would make you move. We negotiate better when we know both.

Your notice period

Employers plan around it, and it's the question that stalls offers most often.

Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.

More like this