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Binance Accelerator Program - Full Stack Engineer (Backend Oriented) Fully Remote

Binance
AUS
Mid
Onsite
Discovered 1 weeks ago
dockerjavakafkakuberneteslangchainpython

Full Job Posting

Requirements:

  • 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."
  • 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.
  • Why Binance
  • Shape the future of finance
  • Collaborate with world-class talent in a user-centric global organization with a flat structure
  • Tackle challenging projects in a fast-paced and innovative enviornment with autonomy
  • Thrive in a results-driven workplace with opportunities for career growth and continuous learning
  • Competitive salary and company benefits
  • Remote-first, work-from-home arrangement (may vary depending on the nature of the role and 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 acknowledge that you have read our Candidate Privacy Notice.
  • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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.
  • Why Binance
  • Shape the future of finance
  • Collaborate with world-class talent in a user-centric global organization with a flat structure
  • Tackle challenging projects in a fast-paced and innovative enviornment with autonomy
  • Thrive in a results-driven workplace with opportunities for career growth and continuous learning
  • Competitive salary and company benefits
  • Remote-first, work-from-home arrangement (may vary depending on the nature of the role and 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 acknowledge that you have read our Candidate Privacy Notice.
  • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
  • Nice-to-have

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.

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