Senior Machine Learning Researcher

RTB House; Województwo mazowieckie; On-site; PLN 180,000 - 300,000; Full time

Job summary

RTB House is a global company providing state-of-the-art marketing technologies powered by Deep Learning, serving top brands and agencies worldwide.

We design and evaluate models to predict user behavior, analyze cutting-edge ML research, and conduct A/B tests of new solutions to optimize ad performance. The role emphasizes developing novel approaches to bidding strategies in first-price auctions and advancing our ML-driven AdTech platform.

Responsibilities

• Designing and implementing models, most often of deep neural networks, used to predict the behavior and preferences of Internet users
• Analysis of the latest works in the field of Machine Learning
• Conducting and analyzing A/B tests of new solutions
• Developing and testing new approaches to modeling key issues, such as bidding in first-price auctions

Job description

About RTB House

RTB House is a global company that provides state-of-the-art marketing technologies for top brands and agencies worldwide. Its proprietary ad-buying engine is the first in the world to be powered entirely by Deep Learning algorithms, enabling advertisers to generate outstanding results and reach their goals at every stage of the funnel. Founded in 2012, and now operating in 90+ markets, RTB House has always been private-by-design. It embraces first-party advertising and a relentless approach to innovation. RTB House offers end-to-end Deep Learning-powered AdTech products and solutions to maximize conversion, drive new customer acquisition, create engagement, and fuel long-term demand for a global base of clients.

Responsibilities

• Designing and implementing models, most often of deep neural networks, used to predict the behavior and preferences of Internet users
• Analysis of the latest works in the field of Machine Learning
• Conducting and analyzing A/B tests of new solutions
• Developing and testing new approaches to modeling key issues, such as bidding in first-price auctions

Sample topics

• Implementation and analysis of new model architectures, such as convolutional networks, transformer
• Analyze the difference in the distribution of training and production data. Estimating the scale of the problem, identifying the best solution, analysis, implementation, deployment
• Experiments with the use of transfer learning
• A completely new approach to generating product recommendations that is free from proxy targets and uses contrastive loss function