Machine Learning Engineer (LLM)- E-commerce Risk Control

Tiktok-posted about 1 month ago

Full-time • Mid Level

Seattle, WA

5,001-10,000 employees

Broadcasting and Content Providers

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The E-Commerce Risk Control (ECRC) team is responsible for securing TikTok's global e-commerce platforms, such as TikTok Shop and Toko. We safeguard buyers, sellers, creators, and the ecosystem from fraudulent, abusive, or malicious behavior. Our mission is to make TikTok the safest and most trusted online marketplace worldwide. We achieve this through: Advanced machine learning systems to detect and prevent evolving business risks (e.g., account takeovers, collusion, incentive abuse, brushing, click-farms); A hybrid approach combining machine learning models, retrieval-augmented reasoning, and multi-agent decision-making systems; Cross-functional collaboration with product, ops, security, and trust teams.

Develop and deploy machine learning models (supervised, unsupervised, hybrid) to proactively detect fraud, abuse, and anomalies across seller behavior, user interactions, and transactions.
Explore cutting-edge techniques including: Retrieval-Augmented Generation (RAG) LangChain-based agents for task decomposition and external knowledge integration
Design prompt engineering and reasoning workflows that connect structured features, risk indicators, and real-time LLM-based decisions.
Knowledge Distillation and BERT-style architectures
Build agentic workflows for complex cases, including modular task agents (e.g., structured data retrieval, open-source search, logical reasoning, decision reflection) orchestrated via a central controller agent.
Work with large-scale behavioral datasets to uncover fraud signals, design monitoring pipelines, and propose new feature generation strategies.
Collaborate with risk ops, product managers, and infra engineers to transform insights into scalable and explainable risk control strategies.

Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or a related technical field
2+ years of experience in delivering ML models in production environments
Strong coding skills in Python (preferred), and/or Java/C++
Familiarity with risk control systems or anomaly detection in large-scale, real-time environments
Experience with LLM post-training applications , especially for agent-based systems
Strong communication skills, with the ability to explain technical solutions to non-technical partners

PhD in Machine Learning, NLP, or a related field
Experience with: RAG, LangChain, or other agentic LLM systems
Building explainable ML workflows with SHAP, LIME, or counterfactual analysis
Knowledge distillation, BERT, Transformer models
Graph-based modeling, graph neural networks, or similarity search
Background in e-commerce, financial fraud, or trust and safety is highly valued
Familiarity with LLM integration in decision systems is a strong plus

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