AI Agent Engineer
Binance · Hybrid
MeritLog read this listing from Binance's Lever job board and last checked it on September 12, 2026.
Source: the employer's Lever job board. Open the original listing for current details.
Job details
- Work model
- Hybrid
- Salary
- Not listed by source
- Location
- Asia; Taiwan, Taipei
Hiring context
How this role compares at Binance
Binance has 301 live roles in MeritLog’s catalog across 11 job families, and 86 of them are in engineering. 0 of those listings publish a pay range, a disclosure rate of 0%.
Binance concentrates this hiring in:
Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.
What the role asks for
What they're asking for
- Shape the future with the world’s leading blockchain ecosystemSkillPreferred
- Collaborate with world-class talent in a user-centric global organization with a flat structureSkillPreferred
- Tackle unique, fast-paced projects with autonomy in an innovative environmentSkillPreferred
- Thrive in a results-driven workplace with opportunities for career growth and continuous learningSkillPreferred
- Competitive salary and company benefitsSkillPreferred
- Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team)SkillPreferred
Parsed by MeritLog from the employer’s own posting. The full description follows below.
Job description
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 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.
Keep exploring