AI Agent Engineer
Remote · Entry Level · Remote
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What we know about this role
HIGH
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First 72 hours
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Job description
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
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