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Machine Learning Scientist (Intern) at Appier. Location Information: Taipei, Taiwan. . About Appier . . Appier is a software-as-a-service (SaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). . Visit . www.appier.com . for more information.. . . . About the role. . We are looking for a . Machine Learning Scientist Intern. to join the . Enterprise Solution Science Team. . . This team focuses on applying cutting-edge ML technologies to real-world marketing problems by combining them with omnichannel customer data.. . We are currently looking for individuals who can commit to an internship schedule of 2~4 days (16~32 hours) per week. This internship opportunity entails a minimum duration of 6 months, beginning from the present date. We advise prospective applicants to carefully assess their availability for this commitment before submitting their applications.. . [ Due to the hybrid work model, this position cannot be fully remote and requires working in the Taiwan office. ]. . . . What You’ll Work On. . . Work on one of the following ML or LLM topics: user prediction, recommendation, agents, and chatbots. . Collaborate closely with senior ML scientists to define ML problems, algorithm development, conduct evaluations, monitoring, and continuous optimization.. . Stay up to date with the latest research and proactively propose innovative applications. . . . . What We’re Looking For. . [Minimum qualifications]. . . Bachelor’s degree in Computer Science, Machine Learning, Mathematics, Electrical Engineering, or related fields (Master’s degree preferred). . 2+ years of experience in ML or LLM. . Proficient in Python . . Proficient in one of the below. . . Core ML and deep learning concepts: feature engineering, recommendation, regression, classification, clustering, etc.. . LLM applications development techniques: RAG, agent, chatbots. . . Able to evaluate models with systematic and quantitative analysis . . Strong data intuition and familiarity with basic statistical concepts. . . . . [Preferred qualifications]. . . Impact-driven mindset, strong analytical and problem-solving skills, and a continuous passion for learning cutting-edge technologies.. . Proficient in using LLM-powered tools (e.g., Github Copilot, ChatGPT) to boost development productivity. . . . . . .