Posted Aug 13
- 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.
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.
- 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.
• 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)
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