LLM Algorithm Applications Engineer

【About Us】 · VICI Holdings is 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. · （HPC/GPU/FPGA）。 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. 【About The Position and Team】 · 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. 【R&R】 · 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. 【 Requirements】 · 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. 【Other Requirements】 · Financial experience (market prediction, event extraction, sentiment analysis, factor modeling, portfolio optimization). · Competitions/community (Kaggle, AI Challenger) or active contributor to open-source projects. · Publications or talks at top venues (NeurIPS/ICML/ICLR/CVPR). 【Character】 · Hands‑on: builds and ships; outcome‑oriented. · Goal‑driven & resilient: sets a high bar and delivers under pressure and ambiguity. · Passion for AI × markets: sustained curiosity and commitment to application. · Bias to action: experiment, measure, iterate quickly. · Owner mindset: accountable end‑to‑end; proactively aligns stakeholders. · Low‑ego, direct communication: candid feedback and fact‑based discourse.