Binance Accelerator Program - LLM Recommendation & Agentic AI Engineer

Responsibilities:

- 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.

- 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:

  1. Large Language Models and post-training
  2. Recommendation systems / ranking / retrieval
  3. LLM agents and tool-use systems
  4. Retrieval-Augmented Generation (RAG)
  5. Reinforcement Learning or preference optimization

- Familiar 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. We believe that having a diverse workforce is fundamental to our success.

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