LLM Data Scientist/Algorithm Engineer (Top Talent Program)

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ 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.

We are seeking a highly skilled professional to join our team, focusing on advancing through innovative AI solutions.

The successful candidate will develop and refine Large Language Models (LLMs) to extract actionable insights, improve business decision-making, and optimize prompt design for more accurate outputs. Additionally, the role includes creating scalable and robust LLM/RAG frameworks tailored to customer service scheduling, fostering innovation and maintaining a competitive market edge.

This role is 100% Remote, Work from Home based.

Responsibilities

• Own the full LLM pipeline from data preparation to production real case usage.

• Design, iterate and optimize prompts (zero-/few-shot, chain-of-thought, tool-calling, etc.) to maximize model utility and safety across products and languages.

• Build and maintain Retrieval-Augmented Generation (RAG) QA/search systems that connect to multi-source knowledge bases.

• Familiar with vLLM/SGLang inference architectures and have proven experience deploying and operating LLM services on multi‑GPU or cluster environments.

• Design, implement and operate multi‑agent LLM architectures (e.g. LangGraph, CrewAI, AutoGen) including task decomposition, agent orchestration, memory sharing and tool‑calling workflows.

• Develop evaluation pipelines (automatic metrics & human feedback) to measure prompt and model quality, bias, and hallucination rates.