Machine Learning Engineer

About the Role

• Machine Learning Engineer – Ads Ranking, Recommendation & Optimization position at Mintegral.
• Welcomes new graduates and PhD candidates.
• Designed for individuals passionate about applying cutting-edge AI techniques to real-world advertising problems, combining research innovation with measurable business impact.

About the Team

• Building large-scale advertising intelligence systems using advanced machine learning, deep learning, and optimization techniques.
• Working on challenging problems including ad retrieval, ranking, recommendation, user modeling, prediction, real-time bidding, and decision optimization.

Key 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.

Requirements & Tech Stack

• C++, Python, Java, Go or similar languages.
• PyTorch and modern deep learning frameworks.
• Large-scale models, GPU computing, distributed training, and production AI systems.

Qualifications

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, WWW, SIGIR, RecSys, or related workshops are a plus.
• Research experience in advertising, recommendation systems, reinforcement learning for decision making, or personalization is highly valued.
• Candidates passionate about online advertising, causal inference, and reinforcement learning-based decision systems are welcome.