Binance Accelerator Program - LLM Recommendation & Agentic AI Engineer
Binance · Remote · mid
salary undisclosed· posted 8h ago
Binance · Remote · mid
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.
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.
Current university students and recent graduates.
*Terms of employment / engagement shall be subject to contract and local applicable laws
- Design and develop LLM-powered recommendation and personalization systems, including candidate generation, ranking, reranking, user intent understanding, and context-aware recommendation for financial and Web3 scenarios.
- Explore and build agentic AI systems that leverage internal data, APIs, tools, and domain-specific capabilities to perform complex financial and trading-related tasks.
- Develop and optimize tool routing, tool retrieval, planning, and multi-step reasoning mechanisms, enabling LLM agents to efficiently select and utilize the appropriate capabilities from a large-scale tool ecosystem.
- Perform post-training of large language models, including SFT, preference optimization, reinforcement learning, and other techniques, to improve recommendation quality, tool-use accuracy, reasoning capability, and task completion performance.
- Build and maintain high-quality training and evaluation datasets, benchmarks, and evaluation pipelines for LLM recommendation and agentic systems, covering dimensions such as relevance, personalization, tool selection, task success rate, latency, and reliability.
- Prototype and iterate on LLM / Agent workflows, including retrieval, recommendation, planning, execution, verification, memory, and feedback loops, and integrate successful prototypes into production systems.
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