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.

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