AI Agent Engineer

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. Binance is looking for a research-minded engineer to join the AI Infra team — sitting at the intersection of frontier model capabilities and real-world agent deployment. You'll work directly with researchers and engineers to push the boundaries of what AI agents can do from Agentic RAG and context management to task execution self-evolving agents and multi-agent coordination. This is not a pure engineering role and not a pure research role. It's both. You'll be expected to generate original ideas run experiments ship prototypes and iterate fast based on real user feedback. The best candidate is someone who has already internalized agent tools into their daily workflow and has strong opinions about model behavior. ➡

Responsibilities

➡ Agentic RAG & Engineering Design and operate next-generation retrieval pipelines — moving beyond static retrieve-once patterns to adaptive self-correcting and multi-hop retrieval workflows architect Agentic RAG systems with dynamic retrieval control query decomposition iterative retrieve-reflect-refine loops and multi-agent retrieval collaboration Frontier Harness Collaborate deeply with researchers and engineers to define and implement model-capability-driven innovations — including context management long-term memory subagent and multi-agent architectures self-evolving agents and real-word task execution Benchmarking & Evaluation Propose harness-domain and RAG-domain benchmarks and evaluation methodologies construct benchmark datasets define annotation strategies and systematically measure and improve agent intelligence across domains — including retrieval efficiency latency groundedness and task success rate Real-world Feedback Loops Leverage multi-channel user feedback and real-world task data as primary research signals design experiments and datasets to continuously improve agent and retrieval performance in production scenarios

Requirements

➡ 1+ Year hands-on experience with LLM RAG and AI agent systems in production RAG & Agentic RAG Engineering Hands-on experience building production retrieval pipelines end-to-end — embedding models (BGE OpenAI etc.) vector stores (Qdrant Milvus Pinecone Weaviate) hybrid search (keyword + vector) reranking models deep understanding of chunking strategy text cleaning and multimodal data parsing experience implementing Agentic RAG patterns — Self-RAG Corrective RAG adaptive retrieval multi-hop decomposition retrieve-reflect-refine loops Agent Harness Engineering — hands-on experience with Agent Harness runtimes (Pi Agent AgentScope 2.0 or equivalent orchestration frameworks) session recovery sandbox isolation middleware/hook systems multi-tenant runtime plan/execute loops and retrieval-grounded tool calling LLM & Agent Fundamentals Deep familiarity with LLM and agent mechanisms — LLM APIs KV Cache Agent Loop Tool Use Reasoning Planning

Skills

MCP Memory Subagent Multi-Agent strong grasp of Prompt Engineering Context Engineering Independent Research Capability Can analyze ambiguous problems from first principles generate original ideas and drive research from 0 to 1 able to rapidly translate ideas into runnable prototypes with tight experiment iteration loops Heavy Agent User Power user of agent products (coding agents general-purpose agents) agent tools are already integrated into your daily work and life you have taste and judgment about model behavior AI-native Engineering Proficient in vibe coding — ships fast using AI-assisted workflows across unfamiliar languages frameworks and domains strong learning velocity in software development

Nice to Have

Deep hands-on experience with agent products such as Claude Code OpenClaw Cowork Manus or equivalent — already integrated into your workflow or daily life RAG evaluation Experience with RAGAS TruLens or custom benchmarking pipelines for retrieval quality groundedness and latency profiling GraphRAG / knowledge graph-augmented retrieval experience Experience with Pi Agent AgentScope 2.0 or other Agent Harness middleware composition multi-tenant session management plugin architecture sandbox backends Background in model training RLHF or model–system co-design LiteLLM / multi-provider proxy experience Kubernetes/EKS pod isolation resource management secrets handling Security engineering prompt injection defense sandbox hardening guardrail design

Nice to Have

➡ Deep hands-on experience with agent products such as Claude Code OpenClaw Cowork Manus or equivalent — already integrated into your workflow or daily life RAG evaluation Experience with RAGAS TruLens or custom benchmarking pipelines for retrieval quality groundedness and latency profiling GraphRAG / knowledge graph-augmented retrieval experience Experience with Pi Agent AgentScope 2.0 or other Agent Harness middleware composition multi-tenant session management plugin architecture sandbox backends Background in model training RLHF or model–system co-design LiteLLM / multi-provider proxy experience Kubernetes/EKS pod isolation resource management secrets handling Security engineering prompt injection defense sandbox hardening guardrail design ➡ Why Binance• Shape the future with the world’s leading blockchain ecosystem• Collaborate with world-class talent in a user-centric global organization with a flat structure• Tackle unique fast-paced projects with autonomy in an innovative environment• Thrive in a results-driven workplace with opportunities for career growth and continuous learning• Competitive salary and company benefits• Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team)Binance is committed to being an equal opportunity employer. We believe that having a diverse workforce is fundamental to our success.By submitting a job application you confirm that you have read and agree to our Candidate Privacy Notice.