Backend Engineer (ML Foundation) - TikTok BRIC Singapore

Responsibilities

The TikTok-BRIC-ML Foundation team builds scalable ML and AI platforms that form the backbone of TikTok's risk control intelligence. It powers everything from model training and deployment to A/B testing and real-time inference. Our scope extends to cutting-edge AI infrastructure and LLM applications -- including AI agents, LLM inference services, and monitoring systems-that enable safer, smarter, and more trustworthy user experiences across the platform.

Responsibilities

In this team you'll have a unique opportunity to have first-hand exposure to the strategy of the company in key security initiatives. You'll be part of a team that's developing novel solutions to first-seen challenges of a non-stop evolution of a phenomenal product eco-system:
- Work on infrastructure and tools to enable construction, deployment, and serving of risk defence algorithms and machine learning models at scale. Improve capacities of risk prevention, perception and remediation across various TikTok businesses.
- Build tools and automated procedures to support risk analytics and defence enforcement.
- Keep risk control solutions state-of-art in front of data security, modularisation, privacy and compliance. Help improve end-to-end risk service/platform/tooling SLA.
- Lead technical direction for cross-team initiatives and mentor engineers in best practices for risk infrastructure and tooling.
- Build and maintain modeling infrastructures, LLM applications to mitigate business risks in TikTok products/platforms, reduce modeling, operational, and system load on risk adversaries and new product/risk ramping-ups.
- Uplevel risk machine learning excellence in privacy/compliance, interpretability, risk perception and analysis.

Qualifications

Minimum Qualifications:
- Experience with one or more general purpose programming languages including but not limited to: Go, C/C++, Java, Python
- Ability to think critically and formulate solutions to problems in a clear and concise way
- Solid communication and collaboration skills with the ability to work effectively with internal teams in a cross-cultural and cross-functional environment

Preferred Qualifications:
- Bachelor's or above degree in Computer Science, Computer Engineering or relevant majors.
- Experience in machine learning infrastructure that handles large traffic
- Experience in Risk related system (or known as Anti-abuse system, or Trust related domain), or large distributed systems.