Machine Learning Engineer – Ads Ranking, Recommendation & Optimization (New Grad / PhD Welcome)

🏢About Us

Mintegral is a leading programmatic and interactive mobile advertising platform. Focused on the APAC region and radiating out globally. Powered by advanced AI technology, we provide global advertisers and developers with innovative, comprehensive experiences. With our efficient mobile marketing and monetization solutions, we help our clients exceed their marketing goals.

As Mobvista’s self-developed programmatic platform, since launched in 2015, Mintegral has quickly grown to become one of the largest mobile advertising platforms in Asia. We offer a full stack of programmatic products and services, including our Self-service Platform, DSP, SSP, Ad Exchange and DMP. We have also created the Mindworks Creative Studio, which offers publishers and brands cutting-edge creative solutions, from traditional creative right through to the latest interactive ad formats. For more information, please visit our website: https://www.mintegral.com/en

🏢About the Team

We are building large-scale advertising intelligence systems using advanced machine learning, deep learning, and optimization techniques. Our team works on challenging problems including ad retrieval, ranking, recommendation, user modeling, prediction, real-time bidding, and decision optimization.

We are looking for Machine Learning Engineers who are passionate about applying cutting-edge AI techniques to real-world advertising problems. This role is designed for new graduates and early-career researchers who want to combine research innovation with measurable business impact.

✅Responsibilities

• ✅Develop and optimize large-scale machine learning models for advertising ranking, retrieval, recommendation, personalization, and prediction.
• ✅Build production-quality ML solutions using PyTorch and modern deep learning frameworks.
• ✅Develop scalable distributed training pipelines for large-scale recommendation and ranking models.
• ✅Research and apply advanced techniques including deep learning, reinforcement learning, causal inference, and optimization for advertising decision systems.
• ✅Explore reinforcement learning approaches for real-time bidding, budget optimization, and sequential decision-making problems.
• ✅Design and evaluate experiments to improve model performance, user experience, and advertiser outcomes.
• ✅Build and optimize embedding models, feature representations, and real-time inference solutions.
• ✅Collaborate with engineers to deploy machine learning models into large-scale p…