[Hiring] Full Stack Engineer (Backend Oriented) @Binance

Role Description

As a Backend Engineer Intern, you'll work directly on the Agent Runtime and AI infrastructure behind these experiences — from intent routing and tool orchestration to evaluation, observability, and reliability.

• Build and improve core components of the Agent Runtime, including intent routing, query rewriting, RAG, tool/skill orchestration, and multi-step agent workflows.
• Design and optimize Tool Server / Tool Calling capabilities, including tool discovery, execution, result handling, and integration with web search, market data, and internal services.
• Develop evaluation datasets and evaluation harnesses to measure routing accuracy, answer quality, tool-use performance, and agent reliability.
• Investigate and improve Agent quality, including failure analysis, regression detection, prompt / workflow optimization, and guardrail effectiveness.
• Improve system observability and reliability through tracing, metrics, logging, dashboards, and production monitoring.
• Debug issues across asynchronous services and agent pipelines, and build integration tests using mocked LLM responses and replayable evaluation cases.
• Work closely with Backend, Data Science, and Algorithm engineers to experiment, benchmark, and bring AI capabilities into production.

Qualifications

• Strong Python/Java programming skills and solid software engineering fundamentals.
• Strong understanding of algorithms, data structures, and problem solving; able to reason about system behavior and trade-offs.
• Hands-on experience with LLM applications or Agent systems, through research, coursework, internships, or projects.
• Familiarity with concepts such as RAG, Tool Calling, Function Calling, Prompting, Agent workflows, or LLM evaluation.
• Able to work comfortably with real codebases, APIs, asynchronous services, testing, CI/CD, and code review.
• Strong debugging and analytical ability, with a mindset of "understand why it fails, not just make it work."

Requirements

• Experience with Agent frameworks such as LangGraph, LangChain, AgentScope, LlamaIndex, or similar.
• Experience with MCP / Tool ecosystems / Agent Runtime.
• Experience building Benchmark / Evaluation / LLM testing infrastructure.
• Familiarity with vector search, embeddings, RAG evaluation, or LLM observability.
• Experience with Redis, Kafka, Docker, Kubernetes, or cloud-native systems.
• Exposure to LLM security, guardrails, prompt-injection defense, or tool-use safety.
• Experience with model inference, latency optimization, or cost optimization.

Benefits

• Ship real features to a production AI Agent used by Binance users.
• Work on the Agent Runtime and AI infrastructure, rather than only application-layer development.
• Learn from senior Backend, Data Science and Algorithm engineers.
• Gain hands-on experience across the modern LLM application stack: Agent Runtime, RAG, Tool Calling, Evaluation, LLMOps and AI Safety.
• Own meaningful engineering projects end-to-end, from problem definition → implementation → evaluation → production.