(Senior) Machine Learning Scientist (Modeling & Evaluation)

Appier (TSE: 4180) is an AI-native Agentic AI as a Service (AaaS) company that empowers businesses to create value through cutting-edge AdTech and MarTech solutions. Founded in 2012 with the vision of “Making AI Easy by Making Software Intelligent,” Appier helps businesses turn AI into ROI through its Ad Cloud, Personalization Cloud, and Data Cloud—each powered by Agentic AI that enables autonomous, adaptive, and real-time decision-making. Today, Appier operates 17 offices across APAC, the US, and EMEA, and is listed on the Tokyo Stock Exchange. Learn more at www.appier.com.

1. Design, develop, and deploy optimization solutions built on ML models and evaluation, to improve efficiency and quality.
2. Work with PMs and engineers to turn product goals and customer needs into clear problems and priorities, and integrate validated methods into the product.
3. Build reliable evaluation methods for LLM output to measure the quality and impact of solutions, and decide if the results apply to real-world conditions.
4. Form your own hypotheses, then design A/B tests that use real traffic to validate them.
5. Follow the latest research and industry solutions, evaluate them in our own context, and propose new approaches. Decide what to build in-house and what to adopt.
6. Monitor solutions after launch, and proactively communicate risks, trade-offs, and progress.
7. (Optional) Mentor junior scientists and interns, if the need arises.

1. Master's or PhD degree in Computer Science, Machine Learning, Mathematics, Electrical Engineering, or related fields
2. At least 2 years of experience in machine learning or engineering roles (5+ years preferred)
3. Hands-on experience with ML models (including classification, regression, ranking, and retrieval) and quality evaluation (including LLM-as-a-judge), a solid foundation in statistics, and the ability to explain how these techniques relate to business impact
4. Impact-driven mindset: able to judge task priority, collaborate across functions, and proactively drive work forward and surface risks
5. AI-native development: work daily with coding agents that read the codebase, run tests, and iterate on their own, while you set the scope and review the diffs. Can explain and correct what the agent produced

1. Experience leading projects
2. Ability to form hypotheses and validate them: design experiments and A/B tests, interpret results, and take ownership of conclusions
3. Background in embeddings and causal inference
4. Engineering experience with AI / LLM applications (for example, serving and integration)

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