Senior Machine Learning Engineer, TikTok BRIC Community Health

TikTok Inc.; San Jose (CA); On-site; USD 162,000 - 388,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k) with company match

Parental leave

Short-term and long-term disability

Life insurance

Wellbeing benefits

10 paid holidays per year

10 paid sick days per year

17 days of Paid Personal Time

Job summary

TikTok Inc. in California is seeking a Senior ML Engineer on the BRIC team to design and implement machine learning solutions that mitigate business risks across TikTok, CapCut, and Lark.

You will help build scalable, privacy-friendly risk models and collaborate with engineering, product, data, and security teams to deliver secure and robust systems. The role requires a Master’s degree with hands-on ML experience and strong software skills in Python or Java/C++/Go, plus experience with

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 ML experience.
• 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 data-driven manner.
• Natural curiosity and a strong passion for solving complex problems; willingness to dig deep and explore better solutions.
• Strong collaboration and communication skills to work across engineering, product, data, and cross-functional teams.
• Ability to work with autonomy, learn quickly, and adapt to a rapidly evolving risk environment.

Responsibilities

• Build machine learning solutions to respond to and mitigate business risks in TikTok products and platforms.
• Improve modeling infrastructures, labels, features and algorithms towards robustness and automation.
• Advance ML capabilities in risk perception, interpretability, privacy, compliance, and adversarial robustness.

Skills

Machine learning fundamentals

Analytical skills

Cross-functional collaboration

Autonomy

Education

Master's degree or above in CS/Stats/ML or related field

Tools

Python

Java

C++

Go

Spark

Hadoop

Hive

LLMs

Job description

Responsibilities

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

• Protect TikTok 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 TikTok products and platforms, including TikTok, CapCut, and Lark. 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.

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.
• Advance machine learning capabilities in areas such as risk perception and analysis, model interpretability, privacy and compliance, and adversarial robustness.

Qualifications

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 research experience in relevant machine learning areas are a plus.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $162000 - $387600 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short‑term and long‑term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

• 1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
• 2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
• 3. Exercising sound judgment.