What you'll do
- Build and improve pieces of the agent runtime: intent routing, query rewrite, RAG/FAQ answering, tool/skill orchestration (L0–L6 loop).
- Extend the Tool Server — add and optimize tools (web search, market data, report APIs), improve tool-call efficiency and output quality.
- Strengthen evaluation: write eval datasets and harnesses, measure intent-routing accuracy and answer quality, catch regressions before release
- Improve observability: traces, metrics, logging (Opik/Pinpoint), production dashboards and alerts.
Fix bugs across the pipeline (routing, skill loading, guardrail false-triggers) and write integration tests with mock-LLM cassette replay.
Must-have
- Solid Python and clean coding habits; comfortable in a real service codebase with CI, code review, and typing.
- Understanding of LLM application basics — prompting, RAG, tool/function calling, or agent frameworks (from coursework, projects, or internships).
- Able to reason about correctness and write tests; debug across async/services.
Available for a sustained internship and eager to iterate quickly on feedback.
Nice-to-have
- Experience with an agent framework (AgentScope / LangChain / LlamaIndex or similar).
- Familiarity with vector search / embeddings, evaluation frameworks (Opik, ragas), or LLM observability.
Exposure to Kafka, Redis/Lindorm, S3, config systems (Apollo), or Docker/K8s. - Interest in evaluation, guardrails/safety (prompt-attack defense), or latency/cost optimization.
What you'll get - Ship real features into a production AI agent used across the app.
- Mentorship from senior BE/DS/algo engineers; end-to-end ownership of scoped tasks.
- Hands-on exposure to the full modern LLM-app stack: RAG, tool servers, evals, LLMOps.
Skills Required
- Current university student or recent graduate
- Solid Python and clean coding habits
- Comfortable working in a service codebase with CI, code review, and typing
- Understanding of LLM application basics (prompting, RAG, tool/function calling, or agent frameworks)
- Able to reason about correctness, write tests, and debug across async/services
- Available for a sustained internship and eager to iterate quickly on feedback
- Experience with agent frameworks (AgentScope, LangChain, LlamaIndex)
- Familiarity with vector search/embeddings, evaluation frameworks (Opik, ragas), or LLM observability
- Exposure to Kafka, Redis/Lindorm, S3, config systems (Apollo), or Docker/Kubernetes
- Interest in evaluation, guardrails/safety, or latency/cost optimization
Binance Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Binance and has not been reviewed or approved by Binance.
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Career-Linked Recognition & Rewards — Performance-linked bonuses can be sizable in favorable crypto cycles, lifting total compensation. Attractive packages in engineering and specialized roles indicate strong rewards for in-demand skills.
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Flexible Benefits — Remote-first flexibility and work-from-anywhere options add meaningful value to the overall rewards package. Flexible schedules and location independence are presented as core perks.
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Retirement Support — Binance.US includes a 401(k) as part of its benefits. This provides a conventional retirement pillar alongside cash and bonus components.
Binance Insights
What We Do
Binance is the world’s leading blockchain and cryptocurrency infrastructure provider with a financial product suite that includes the largest digital asset exchange by volume. Trusted by millions worldwide, the Binance platform is dedicated to increasing the freedom of money for users, and features an unmatched portfolio of crypto products and offerings, including: trading and finance, education, data and research, social good, investment and incubation, decentralization and infrastructure solutions, and more. For more information, visit: https://www.binance.com







