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 &

• E ngineering
• : 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