(Senior) Machine Learning Scientist (Modeling & Evaluation)
Salary not listed • Taipei, Taiwan • Full-time
Posted 24d ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Appier is seeking a Machine Learning Scientist (Modeling & Evaluation) to join the Enterprise Solution Science Team. This role focuses on helping enterprises leverage AI to achieve tangible ROI. You will be responsible for the complete development lifecycle of solutions, from initial problem definition through to driving models and solutions into production. This involves designing, developing, and deploying optimization solutions powered by machine learning models and rigorous evaluation techniques to enhance efficiency and quality.
Responsibilities
- Design, develop, and deploy optimization solutions using ML models and evaluation methods to improve efficiency and quality.
- Collaborate with Product Managers and engineers to translate product goals and customer needs into clear problems and priorities, integrating validated methods into the product.
- Build reliable evaluation methods for LLM outputs to assess solution quality and impact, determining applicability to real-world conditions.
- Formulate hypotheses and design A/B tests using real traffic to validate them.
- Stay updated on the latest research and industry solutions, evaluate them in the company's context, and propose new approaches, deciding on in-house development versus adoption.
- Monitor solutions post-launch, proactively communicating risks, trade-offs, and progress.
- Mentor junior scientists and interns as needed.
Requirements
- Master's or PhD degree in Computer Science, Machine Learning, Mathematics, Electrical Engineering, or a related field.
- At least 2 years of experience in machine learning or engineering roles (5+ years preferred).
- Hands-on experience with ML models (classification, regression, ranking, retrieval) and quality evaluation (including LLM-as-a-judge).
- Solid foundation in statistics and the ability to explain the business impact of ML techniques.
- Impact-driven mindset with the ability to prioritize tasks, collaborate across functions, and proactively drive work and surface risks.
- Experience with AI-native development, including working with coding agents for code review and iteration.
- Ability to explain and correct agent-produced code.
- Experience leading projects (preferred).
- Ability to form hypotheses, design experiments and A/B tests, interpret results, and take ownership of conclusions (preferred).
- Background in embeddings and causal inference (preferred).
- Engineering experience with AI/LLM applications, such as serving and integration (preferred).