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

About Us

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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

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 production systems.
• Stay current with advances in AI research and apply state-of-the-art methods to advertising systems.

Basic Qualifications

• Bachelor's, Master's, or Ph.D. degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Mathematics, Statistics, Operations Research, or related fields.
• Strong programming skills in C++, Python, Java, Go, or similar languages.
• Strong foundation in algorithms, data structures, operating systems, distributed systems, and computer architecture.
• Strong interest in machine learning systems, AI infrastructure, and high-performance computing.
• Ability to solve complex engineering problems and quickly learn new technologies

Preferred Qualifications

• Experience with recommendation systems, ranking systems, retrieval systems, or online advertising algorithms.
• Experience with large-scale recommendation systems or real-time bidding (RTB) systems is preferred.
• Experience applying reinforcement learning, causal inference, or optimization methods to decision-making problems.
• Experience with PyTorch distributed training, large-scale deep learning frameworks, GPU acceleration, or ML systems.
• Research publications or projects in top-tier venues such as NeurIPS, ICML, ICLR, KDD, SIGIR, RecSys, or related workshops are a plus.
• Research experience in advertising, recommendation systems, reinforcement learning for decision making, or personalization is highly valued.
• Welcome candidates who are passionate about online advertising, causal inference, and reinforcement learning-based decision systems.

Why Join Us

• Work on challenging AI and machine learning problems at the intersection of advertising, recommendation, and large-scale decision systems.
• Build technologies that impact hundreds of millions of advertising requests through real-time intelligent decision-making.
• Directly contribute to improving advertiser performance and ROI through advanced AI systems.
• Collaborate with talented engineers and researchers working on cutting-edge ML infrastructure and algorithms. xhyhwjd
• Opportunity to work with large-scale models, GPU computing, distributed training, and production AI systems.