Binance Accelerator Program - Backend Engineer (AI Pro / Agent Infrastructure)

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. Binance is trusted by more than 320 million people in 100+ countries for its industry-leading security, transparency, trading engine speed, protections for investors, and unmatched portfolio of digital asset products and offerings from trading and finance to education, research, social good, payments, institutional services, and Web3 features. Binance is devoted to building an inclusive crypto ecosystem to increase the freedom of money and financial access for people around the world with crypto as the fundamental means.

About Binance Accelerator Program Binance Accelerator Program (BAP) is a 3-6 month internship program designed for Early Career talent to have firsthand experience in the rapidly expanding digital assets space. You will be given the opportunity to develop your skills at Binance and understand what it’s like to work at the world's leading blockchain ecosystem. As part of your internship in the BAP, there will also be opportunities for networking and development, which will expand your professional network and build transferable skills to propel you forward in your career. Learn about the BAP Program HERE.

Who may apply Current university students and recent graduates. *Terms of employment / engagement shall be subject to contract and local applicable laws

About the Team Binance AI Pro is building the next generation of AI-native experiences within Binance. At the core of AI Pro is an agentic system that can understand user intent, reason through multi-step tasks, retrieve information, call tools, and safely execute actions across Binance's ecosystem. 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. We're looking for strong technical students who enjoy going deep — people who are excited by LLM Agents, systems, algorithms, and experimentation, and wants to work on real production AI systems alongside experienced Backend, Data Science, and Algorithm engineers. You will have the opportunity to turn ideas from prototype → benchmark → production, and see your work directly impact how AI Pro behaves at scale.

Responsibilities: 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.

Requirements (Must Have): Strong Python 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."

Nice-to-have: 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.

What You'll Get: 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.