Machine Learning Engineer Graduate

About the Role

The USDS Platform and Community Integrity (PaCI) team is the architectural backbone of TikTok's Risk & Integrity in the US. We drive end-to-end integrity for TikTok and its expanding ecosystem by leveraging localized US data and state-of-the-art AI/LLM capabilities. Our mission is to deliver AI-driven algorithmic solutions that detect and neutralize systemic threats across critical domains. We architect the core ML infrastructure and risk intelligence that protect our billion-user ecosystem from adversarial threats.

Key Responsibilities

• Global Ecosystem Governance: Lead the algorithmic architecture and governance for TikTok US across key domains, including account integrity, incentive abuse, content safety, and financial risk, ensuring a fair and secure ecosystem for a billion-user community.
• Universal Representation Modeling: Build high-precision, risk representation models leveraging massive heterogeneous data (video, live streams, audio, behavior sequences, and social graphs) to enable cross-scenario risk perception.
• Dynamic Adversarial Defense: Research and counter global adversarial threats (e.g., large-scale automated attacks, AIGC harmful content). Architect self-evolving defense systems that maintain robustness and resilience against rapid tactic shifts and data drift.
• Intelligent Decision-making: Drive the integration of Generative AI and Multimodal technologies into the risk lifecycle. Leverage LLMs and deep learning models to solve complex adjudication and interpretability challenges, balancing precision with automated, large-scale enforcement.

Requirements & Tech Stack

• Solid foundation in Machine Learning; proficient in applying Transformer and GNN architectures to solve large-scale integrity challenges with social graph and behavior sequence data.
• Experience designing model evaluation systems that balance technical performance with product goals.
• Strong engineering and coding skills. Proficient in at least one language among Python, C++, or Go; familiar with distributed computing (e.g., Spark, Flink) and deep learning frameworks (e.g., PyTorch).
• A passion for building world-class risk infrastructure and the ability to communicate effectively in a global, cross-cultural environment.

Qualifications

• Individuals who are completing or have recently completed a PhD's degree in Computer Science, Engineering, Math, Statistics, or a related discipline.
• Commitment to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Preferred Qualifications

• Agentic AI Experience: Hands-on experience building LLM-based Agents capable of task planning, tool-use, or closed-loop autonomous decision-making.
• LLM Security & Alignment: Experience in LLM Post-training or Fine-tuning (e.g., SFT, RLVR, RLHF) to optimize models for specific risk domains like fraud intent recognition or malicious redirection.
• Multimodal Expertise: Proven track record in video understanding or multimodal representation; ability to integrate visual, audio, and textual features to solve complex behavioral modeling challenges.
• Industry Vision: Experience in Integrity, Anti-Abuse, Safety, or Trustworthy AI at a large-scale internet platform.