LLM Recommendation & Agentic AI Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Create and implement LLM-driven recommendation and personalization systems tailored for financial and Web3 applications, including tasks like candidate generation, ranking, reranking, user intent comprehension, and context-aware recommendations.

• Investigate and construct agentic AI systems utilizing internal data, APIs, tools, and specialized capabilities for intricate financial and trading-related functions.

• Develop and refine mechanisms for tool routing, tool retrieval, planning, and multi-step reasoning within expansive tool ecosystems.

• Conduct post-training of large language models employing techniques such as SFT, preference optimization, reinforcement learning, and others.

• Create and sustain training and evaluation datasets, benchmarks, and evaluation pipelines specific to LLM recommendation and agentic systems.

• Prototype and iterate on LLM and agent workflows, subsequently integrating successful prototypes into operational systems.

• Investigate small and specialized language models for tasks involving routing, recommendation, classification, reranking, and latency-sensitive applications.

• Collaborate with senior engineers, researchers, product teams, and domain specialists on system design, experimentation, integration, deployment, and ongoing optimization.

⛳️ Requirements

• Must be a current university student or a recent graduate.

• Strong foundation in machine learning, NLP, information retrieval, recommendation systems, or large language models.

• Practical experience in at least one of the following areas: large language models and post-training; recommendation systems, ranking, or retrieval; LLM agents and tool-use systems; retrieval-augmented generation; reinforcement learning or preference optimization.

• Familiarity with SFT, RL, DPO/GRPO-style optimization, prompt engineering, structured generation, function/tool calling, and model evaluation.

• Understanding of embedding-based retrieval, learning-to-rank, reranking, personalization, user modeling, or generative recommendation.

• Proficient programming skills in Python.

• Experience with deep learning frameworks such as PyTorch.

• Capability to independently conduct experiments, evaluate model/system performance, and convert research concepts into production-ready solutions.

• Preferred: experience in building production-scale recommendation, search, or LLM systems.

• Preferred: familiarity with agent frameworks, tool routing, multi-agent systems, memory systems, or long-horizon agent workflows.

• Preferred: experience with LLM inference and serving frameworks such as vLLM, SGLang, TensorRT-LLM, or similar.

• Preferred: knowledge of Web3, cryptocurrency, financial markets, or trading systems.

• Preferred: experience 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 advantageous.

🏝️ Benefits

• Competitive salary and comprehensive company benefits.

• Work-from-home options (the arrangement may differ based on the business team's work nature).

• Opportunities for networking and professional development.

• Potential for career advancement and continuous learning.

• Independence in an innovative environment.

• Collaboration with top talent in a user-focused global organization with a flat organizational structure.