Senior Machine Learning Scientist, Playable Ads

<p><strong>About Appier&nbsp;</strong></p> <p>Appier (TSE: 4180) is an&nbsp;AI-native Agentic AI as a Service (AaaS) company&nbsp;that empowers businesses to create value through cutting-edge AdTech and MarTech solutions. Founded in 2012 with the vision of&nbsp;“Making AI Easy by Making Software Intelligent,”&nbsp;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<a href="http://www.appier.com/" target="_blank" data-saferedirecturl="https://www.google.com/url?q=http://www.appier.com/&amp;source=gmail&amp;ust=1772524763166000&amp;usg=AOvVaw2VstxLR9Gjx8R3vPf40kL6">&nbsp;</a><a href="https://www.appier.com">www.appier.com</a>.</p> <p>&nbsp;</p> <p><strong>About the role</strong></p> <p>AI is reshaping how brands connect with consumers — and at Appier, we’re at the forefront. Our Playable Ads team builds AI-powered ad experiences — from interactive playable formats to video ads — that drive dramatically higher engagement and conversion for the world’s leading apps and games. We’re looking for a Senior Machine Learning Scientist to push the boundaries of what’s possible — applying cutting-edge ML, generative AI, and optimization techniques to create smarter, more compelling ad creatives at scale.</p> <p>If you’re excited about turning the latest advances in AI into products that reach millions of users, and you thrive at the intersection of research and real-world impact, we’d love to talk.</p> <p>&nbsp;</p> <p><strong>What You’ll Work On</strong></p> <ul> <li>Apply ML and generative AI techniques to problems such as creative optimization, user behavior prediction, engagement modeling, and dynamic content generation for various creative formats.</li> <li>Own the end-to-end ML lifecycle — from problem framing and experimentation through to production deployment and continuous improvement.</li> <li>Collaborate closely with product, engineering, and design teams to translate business goals into well-defined ML solutions.</li> <li>Conduct offline and online evaluation, A/B testing, and performance monitoring to continuously improve model quality and business impact.</li> <li>Stay current with the latest research in areas like LLMs, multimodal models, reinforcement learning, and recommendation systems, and proactively propose innovative applications to the team.</li> <li>Mentor fellow scientists and contribute to the team’s technical standards and culture.</li> </ul> <p><br><strong>What We’re Looking For</strong></p> <p>[Minimum qualifications]</p> <ul> <li>Master’s or Ph.D. in Computer Science, Machine Learning, Mathematics, Electrical Engineering, or a related field.</li> <li>Solid understanding of <strong>modern ML models and algorithms</strong>, with hands-on experience in at least one area such as prediction/optimization, generative models, recommendation systems, computer vision, or reinforcement learning.</li> <li>Proficient in <strong>Python</strong> and ML frameworks such as PyTorch or TensorFlow.</li> <li>Practical experience taking ML models from prototype to production, including pipeline development and performance monitoring.</li> <li>Strong analytical and problem-solving skills with the ability to diagnose model behavior and improve training pipelines.</li> <li>Clear communicator who can explain technical ideas to both technical and non-technical stakeholders and enjoys working in a fast-paced, collaborative environment.</li> <li>Proficient in using LLM-powered development tools (e.g., GitHub Copilot, Cursor, ChatGPT) to accelerate productivity.</li> <li>Ability to balance cutting-edge research with time-to-market constraints, delivering iterative PoCs that validate product hypotheses and translate into production-ready solutions.</li> </ul> <p>[Preferred qualifications]</p> <ul> <li>Publications in top AI/ML conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, KDD, RecSys).</li> <li>Experience in the advertising technology or MarTech domain.</li> <li>Contributions to open-source projects or active involvement in research communities.</li> </ul> <p>&nbsp;</p> <p>#LI-AK1</p>