Binance Accelerator Program - LLM Recommendation & Agentic AI Engineer

About Binance

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

About Binance Accelerator Program

Binance Accelerator Program (BAP) is a 3–6 month internship program designed for Early Career talent to gain firsthand experience in the rapidly expanding digital assets space. Interns will have the opportunity to develop skills at Binance and understand what it’s like to work at the world’s leading blockchain ecosystem. The program also includes networking and development opportunities to expand professional networks and build transferable skills.

Who May Apply

• Current university students and recent graduates.
• Terms of employment/engagement are subject to contract and local applicable laws.

Responsibilities

• Design and develop LLM-powered recommendation and personalization systems, including candidate generation, ranking, reranking, user intent understanding, and context-aware recommendations 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 so LLM agents can efficiently select and utilize appropriate capabilities from large-scale tool ecosystems.
• 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 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.
• Explore the application of small and specialized language models for routing, recommendation, classification, reranking, and other latency-sensitive tasks, balancing model quality, inference cost, and system performance.
• Work closely with senior engineers, researchers, product teams, and domain experts to complete system design, experimentation, integration, deployment, and continuous optimization.

Requirements

• Strong foundation in machine learning, NLP, information retrieval, recommendation systems, or large language models.
• Hands-on experience with at least one of the following areas:
  • Large Language Models and post-training
  • Recommendation systems / ranking / retrieval
  • LLM agents and tool-use systems
  • Retrieval-Augmented Generation (RAG)
  • Reinforcement Learning or preference optimization
• Familiarity with modern LLM techniques such as SFT, RL, DPO/GRPO-style optimization, prompt engineering, structured generation, function/tool calling, and model evaluation.
• Good understanding of recommendation or search techniques, such as embedding-based retrieval, learning-to-rank, reranking, personalization, user modeling, or generative recommendation.
• Strong programming skills in Python and experience with deep learning frameworks such as PyTorch.
• Ability to conduct experiments independently, analyze model/system performance, and translate research ideas into practical production solutions.

Preferred Qualifications

• Experience building production-scale recommendation, search, or LLM systems.
• Experience with agent frameworks, tool routing, multi-agent systems, memory systems, or long-horizon agent workflows.
• Experience with LLM inference and serving frameworks such as vLLM, SGLang, TensorRT-LLM, or equivalent systems.
• Familiarity with Web3, cryptocurrency, financial markets, or trading systems.
• Experience working with large-scale datasets, distributed training, model serving, or high-throughput online systems.
• Publications, open-source contributions, or practical projects related to LLMs, recommendation systems, agents, search, or reinforcement learning are a plus.

Why Binance

• Shape the future with the world’s leading blockchain ecosystem.
• Collaborate with world-class talent in a user-centric global organization with a flat structure.
• Tackle unique, fast-paced projects with autonomy in an innovative environment.
• Thrive in a results-driven workplace with opportunities for career growth and continuous learning.
• Competitive salary and company benefits.
• Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team).

Binance is committed to being an equal opportunity employer and believes that having a diverse workforce is fundamental to its success. By submitting a job application, you confirm that you have read and agree to the Candidate Privacy Notice. Binance may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist the 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.