- Location
- Taipei, Taiwan · Tokyo, Japan
- Department
- Scientist
- Seniority
- Senior
- Experience
- 4+ years
- Education
- PhD
- Source
- Greenhouse
Description
About Appier
Appier is an AI-native Agentic AI as a Service (AssA) 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.
The Impact You’ll Make at Appier
Appier is seeking a Senior Machine Learning Scientist to join our Advertising Cloud Optimization team, which leads the development of core machine learning algorithms driving campaign efficiency and advertiser ROI. Our programmatic advertising platform operates at a massive scale, handling over multi millions queries per second (QPS), all powered by our proprietary deep learning models for bidding, pricing, and personalized content delivery.
In this role, you’ll directly impact the efficiency and profitability of ads campaigns by improving models for bidding, pricing, and personalized content recommendation, while ensuring system robustness and scalability in a dynamic market environment.
What You’ll Work On
- Design, implement, and productionize state-of-the-art ML models to improve campaign outcomes.
- Analyze large-scale user and auction data to discover predictive patterns and alpha signals that enhance bidding and personalization.
- Collaborate cross-functionally with engineering, product, and data teams to identify opportunities, define roadmaps, and deliver impactful solutions.
- Continuously improve system performance through offline experimentation and online testing (e.g., A/B tests, incremental learning).
What We’re Looking For
- Bachelor’s degree in Computer Science, Mathematics, EE, or related field; Master’s or PhD preferred.
- 4+ years of industry experience in ad tech, with a focus on performance optimization.
- Proven experience in applied machine learning, especially in CTR prediction, recommendation systems.
- Proficiency in Python and experience with modern ML frameworks (PyTorch, TensorFlow, etc.).
- Strong ownership and collaboration skills—able to lead end-to-end projects across product, data, and engineering.
- Bonus: Experience working on high-throughput, low-latency real-time systems (e.g., RTB engines, stream inference).
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