Machine Learning Engineer, BRIC Community Health

Staff Machine Learning Engineer, Tik Tok BRIC Community Health

Location:

San Jose

Regular

A75187A

• Protect Tik Tok users, including and beyond content consumers, creators, advertisers and other participants across the ecosystem.
• Safeguard platform health and community experience authenticity.
• Build scalable infrastructure, platforms, and technologies while collaborating closely with cross‑functional teams and stakeholders.

The BRIC team works to minimize the impact of inauthentic and abusive behaviors across Tik Tok products and platforms. Our scope covers a broad range of community and business risk areas, including account integrity, engagement authenticity, anti-spam, API abuse, growth fraud, live streaming security, and financial safety across advertising and e-commerce. 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 make quick and solid differences.

• Build machine learning solutions to respond to and mitigate business risks in Tik Tok 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.
• Advance machine learning capabilities in areas such as risk perception and analysis, model interpretability, privacy and compliance, and adversarial robustness.

• Minimum Qualifications:
  
  Master's degree or above in Computer Science, Statistics, Machine Learning, or another relevant technical field, with at least 2 years of hands‑on machine learning experience through industry, research, internships, or equivalent project work.
• Strong software engineering fundamentals and proficiency in Python or one of Java/C++/Go, with experience in large‑scale data processing technologies such as Spark, Hadoop, or Hive.
• Strong machine learning fundamentals, with research or hands‑on experience in areas such as deep learning, representation learning, graph learning, sequence/time‑series modeling, transfer/multi‑task learning, or unsupervised/self‑supervised learning.
• Strong problem‑solving and analytical skills, with the ability to reason and communicate in a result‑oriented and data‑driven manner.
• Natural curiosity and a strong passion for solving complex, ambiguous problems; willingness to dig deep, challenge assumptions, and continuously explore better solutions.
• Strong collaboration and communication skills, with the ability to work effectively across engineering, product, data, system and other cross‑functional teams.
• Ability to work with a high degree of autonomy, learn quickly, and adapt to a rapidly evolving risk environment.
• Preferred Qualifications:
  
  Industry experience in risk, fraud, spam, abuse detection, or related areas is preferred but not required.
• Experience building or deploying large‑scale machine learning systems/algorithms is a plus.
• Hands‑on experience with LLMs, generative AI, or agent development, including LLM‑powered applications, evaluation pipelines, retrieval or knowledge systems, or agentic workflows.
• Research publications or strong…