Data Scientist, LLM & AI Agent Engineer (Applied AI)

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by over 280 million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world. About the Role As a core member of the AI Risk team, you will build specialized AI Agents that directly impact financial security. You will leverage LLMs (Large Language Models) to process unstructured data, automate complex investigation workflows, and optimize the signal-to-noise ratio in our alert systems. Responsibilities

Design “Sherlock”-style autonomous agents to investigate suspicious fraud activity

Build reasoning-capable agents that analyze user behavior, device fingerprints, and trading history to assess whether withdrawals are fraudulent or safe

Develop market manipulation detection agents that analyze unstructured social sentiment alongside order book data

Identify pump-and-dump schemes and wash trading

Build LLM-based triage systems to handle high volumes of risk alerts

Use agents to pre-screen alerts by analyzing alert context and historical false positives

Automatically decide whether to close alerts or escalate them to human analysts

Reduce alert fatigue for Ops teams by filtering noise with high semantic understanding

Use LLMs to extract features from messy, unstructured data such as chat logs, support tickets, KYC documents, and chat groups

Convert vague risk signals into structured features (e.g., JSON) for downstream risk models or rule engines

Build RAG pipelines that allow agents to query repositories of past fraud cases and identify recurring patterns

Develop analyst copilot tools to draft investigation reports (SARs) and summarize complex cases

Enable human-in-the-loop workflows where agents gather evidence, propose decisions, and humans review and approve outcomes

Requirements

LLM application experience: 2+ years building applications with GPT, Claude, or other open-source LLM models

Agent frameworks: Deep familiarity with LangGraph, LangChain, or CrewAI

RAG for domain knowledge: Experience building retrieval systems that fetch relevant contextual information

Evaluation (crucial): Proven ability to design evaluation sets to measure agent performance, such as correctly identifying fraudsters versus regular users

Python mastery: Ability to write production-ready, maintainable code (not just scripts)

Data engineering: Proficiency in SQL and experience working with data pipelines (e.g., Kafka, Spark) to provide agents with real-time data

Domain interest (bonus): Understanding of Trust & Safety, anti-fraud, or financial risk domains

Crypto familiarity (bonus): Knowledge of on-chain analysis, DeFi concepts, and wallet addresses

Anomaly detection (bonus): Experience with anomaly detection concepts and techniques

Educational background: Master’s degree with 2+ years of experience, or equivalent hands-on expertise in Data Science, AI Engineering, or Applied ML

Scale & systems experience: Experience working with terabyte-scale datasets and real-time systems

Financial systems knowledge (preferred): Understanding of financial markets, trading systems, or risk management

Adaptability: Comfortable working in fast-changing, ambiguous 0→1 environments with the ability to prototype, iterate quickly, and drive execution

Passion for AI: Strong interest in AI agents, autonomous systems, LLMs, or intelligent automation

Communication skills: Strong English reading and writing skills for technical documentation and agent prompt engineering