Binance Accelerator Program - Full Stack Engineer (Backend Oriented) Fully Remote
Remote · Mid Level · Remote
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What we know about this role
HIGH
Binance is actively reviewing profiles and moving candidates through the pipeline right now.
First 72 hours
Window passed - posted 54d agoEarly applicants get seen before the pile builds.
Reposted role
This role was posted earlier, closed, and has now reopened - the company is accepting candidates again.
First posted Jul 28, 2026
Skills required
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Job description
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)
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.
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