Machine Learning Researcher for Advertising Systems

Quick Facts

• Role: Machine Learning Researcher for Advertising Systems

Description

You will research, develop, and advance machine learning models and optimization algorithms that improve online ad selection. The work focuses on building and tuning ML models, analyzing large volumes of real production data, and translating research findings into production deployments. You’ll also experiment with new ideas and stay current with the latest AI/ML technologies.

Responsibilities

• Design, develop, and tune machine learning models and optimization algorithms for selecting the most relevant online ads for users

• Contribute to optimization of online ad auction systems

• Work with large-scale datasets from service operations; analyze data to uncover insights about users, advertisers, the market, and the business

• Evaluate new approaches and stay current with ML trends and technologies

• Turn research results into practice and support deployment in production environments

• Implement and test your own ideas to improve the quality of advertising services

• Collaborate with researchers, engineers, and product managers

Requirements

• Practical experience with machine learning, data analysis, and statistics

• Advanced Python programming skills and effective use of libraries for data work and ML development

• University education in IT or a related technical field (ideally Data Science, machine learning, or statistics)

• Ability to work independently on research: from reading papers and defining research problems to designing models and building prototypes

• Interest in solving complex technical problems and motivation to find innovative solutions

• Ability to collaborate and communicate solutions effectively within a team

• Excellent communication skills in Czech and English; able to explain problems, present conclusions, and defend proposed solutions

Benefits

• Work on unique, technically challenging ML/AI projects with direct impact on services used by millions

• Opportunity to research, develop, and deploy advanced ML models and optimization algorithms into real operation

• Hands-on work with large volumes of real data and modern tooling (Python, PyTorch, Spark, LLMs, MLflow)

• Space for your own ideas, experimentation, and innovation on complex problems

• Collaboration within a strong research team; share knowledge and grow expertise

• Ability to follow the latest AI/ML trends, participate in international conferences, publish articles, and present results

• Friendly, inspiring environment supporting autonomy, creativity, and teamwork

Selection Process

• Phone pre-screening

• First round: ~30-minute Zoom interview

• Home assignment

• Final round: in person or via Zoom