About the job
About Appier
Appier is a cutting-edge software-as-a-service (SaaS) company leveraging artificial intelligence (AI) to enhance business decision-making. Established in 2012 with the ambition of democratizing AI, our mission is to transform AI into tangible returns on investment (ROI) by creating intelligent software solutions. With 17 offices spanning across APAC, Europe, and the U. S., we are proud to be listed on the Tokyo Stock Exchange (Ticker number: 4180). For more details, please visit www.appier.com.
Role Overview
We are seeking an experienced Senior Machine Learning Scientist specializing in LLM to join our dynamic Enterprise Solution Science Team. This team is dedicated to employing advanced ML technologies to address real-world marketing challenges by integrating them with comprehensive omnichannel customer data. In this pivotal role, you will oversee the complete development lifecycle, from defining problems to implementing models in production.
Key Responsibilities
- Engage with various LLM-related projects such as agents, chatbots, copilots, and retrieval-augmented generation (RAG) systems.
- Collaborate with product teams to translate business objectives into clearly defined ML challenges during the planning phase.
- Partner with ML engineers to ensure seamless deployment of models into production, with a focus on reliability and maintainability.
- Conduct thorough offline and online evaluations, continuous monitoring, and optimization of models post-deployment.
- Stay informed about the latest research developments and proactively suggest innovative ML applications.
- (Optional) Mentor junior scientists and contribute to the development of the team's culture and standards.
Qualifications (Minimum)
- Master’s or PhD degree in Computer Science, Machine Learning, Mathematics, Electrical Engineering, or related discipline.
- 3+ years of experience in machine learning or engineering roles.
- Proven track record in building and deploying enterprise-grade LLM applications (e.g., intelligent agents).
- Familiarity with LLM evaluation methodologies, including hallucination detection and confidence scoring, emphasizing quantitative metrics and systematic benchmarking.
- Hands-on experience with complex reasoning, modular workflows (e.g., multi-component prompting, agent orchestration), and RAG pipelines.
- Demonstrated experience in cross-functional collaboration and leading industry-specific projects.
- A strong sense of impact, coupled with excellent analytical and problem-solving skills.
