Algorithmic Innovation: Spearhead research into optimal quantization methodologies for advanced low-precision data formats, driving the strategic adoption and expansion of our low-precision computing ecosystem to secure a competitive edge in numerical computing.
Lead the research and development of AI training and inference acceleration algorithms for Agentic AI and Multimodal domains, specifically optimized for next-generation AI compute architectures to maximize compute efficiency and utilization.
Define and maintain the enterprise AI platform architecture, technology roadmap and reference architecture for the Central AI Kitchen (CAK) or equivalent AI platforms.
Experiment Tracking & Evaluation Reporting: Own MLflow experiment logging for assigned team model runs; conduct model evaluations using standard metrics; produce structured evaluation reports reviewed by team. Your reports directly inform model iteration decisions.
Participate in the R&D of TikTok Shop's Southeast Asia business, including end-to-end full-stack delivery spanning front-end pages, server-side APIs, data pipelines, and launch verification;
Act as a primary technical counterpart and architecture partner - designing, coding, and deploying a suite of custom, intelligent AI tools, and critically reviewing design decisions.
Lead a team of young and bright MLOps Engineers in conducting research, design, and prototype MLOps practices, tools, and frameworks to expedite the machine learning lifecycle at MoneyLion.
Design and build machine learning, statistical models to address business questions across client intelligence, CRM, retail performance, merchandising, marketing and operations.
Build end-to-end solutions:Translate business and data challenges into scalable, secure, observable technical architectures. Communicate trade-offs clearly and produce practical designs that can be implemented by client and delivery teams.
AI Solution Design & Development: Architect and build specialized AI systems tailored to client requirements, including custom NLP/LLM solutions, computer vision or data science pipelines, and retrieval-augmented generation (RAG) workflows for knowledge-intensive applications.
Engage in forward-looking exploration and research of foundational AI technologies, including but not limited to large model pre-training and post-training paradigm innovation exploration, architecture design for multimodal understanding and generative models, next-generation foundational architectures (e.g., Transformer-next), and acceleration of large-scale model training and inference.