LLM Application Engineer in Quant Trading

We are a top-tier company focused on cutting-edge technology and financial trading. Our hardware team is dedicated to developing ultra-low latency and high-performance digital design solutions, keeping pace with Wall Street’s best in FPGA design. Our strategies span stocks, futures, and derivatives, with a daily global trading volume reaching hundreds of millions of USD, underscoring our leadership and scale. - Engineering-driven trading: we turn research into quantifiable trading edge through robust data and systems engineering. - Global perspective with local agility: Taipei-based team operating across global markets with high-speed decisioning and strong compliance. - Leading infrastructure: proprietary low-latency trading stack, distributed data pipelines, and HPC/GPU/FPGA acceleration. - Culture & values: Ownership, performance obsession, transparent collaboration, fast iteration, and outcomes-first mindset. - Growth & resources: generous experiment budgets, top-tier cloud/compute, and support for academic and open-source engagement.

This role builds and strengthens our core capabilities in Large Language Model (LLM) Agents and Reinforcement Learning (RL) to directly power trading automation and deepen market insights. You will work closely with quants, data engineers, and software engineers to turn research prototypes into production-grade systems for live trading environments. Co-design experiments with quants to translate model outputs into tradable signals, with offline/online backtesting and A/B testing. Design and build AI agents (RAG, tool use, task planning) to boost strategy research and trading execution efficiency. Apply prompt engineering, MoE, LoRA, quantization, and distillation to improve the perf–cost curve and inference latency. Specialize fine-tuning and alignment (SFT/DPO) for financial text—disclosures, news, social, earnings—to improve event and sentiment inference. Adopt MLOps best practices (containerization, CI/CD, feature/model versioning) to increase velocity and compliance. Track frontier research and lead internal tech sharing to inform model selection and architecture evolution.

M.S. or above in CS/EE/Data Science preferred; equivalent practical achievements considered. Proficient with AI coding tools (Cursor, Claude Code); adept in spec engineering and test-driven development (TDD). Deliver rapid 0→1 with AI and drive 1→100 iterative improvement. 2+ years in LLM with PyTorch or TensorFlow; hands-on experience training and fine-tuning models end to end. Fluency in the LLM ecosystem (LangGraph, Ollama, OpenAI SDK) to rapidly prototype and ship services. Strong experiment design, problem decomposition, and cross-team communication with an emphasis on observability and operability. Effective communication in Mandarin and English; able to write technical docs and present externally.

Full package around NTD 3M, 13 month base, typically 18 months incl. bonus Negotiable sign-on bonus