Machine Learning Researcher

Location: Poland

We Are:

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. As an ML Researcher, you will be responsible for its development.

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.
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You Will:
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• 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

**Desired Experience:
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• 4+ years of hands-on experience with Machine Learning / Data Science 
• Interest and willingness to constantly develop in the area of ​​Machine Learning
• Knowledge of statistics and probability
• Proficiency in programming
• C1 level in Polish

Selected technologies used:

• Python, Java, Scala
• PyTorch, NumPy, Pandas
• Jupyter Notebooks

Additional advantages will be:

• Experience in the ML industry
• Experience in designing recommendation systems
• Practical knowledge of the tools we use

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

We Offer:

• Cooperate in a team of enthusiasts with experience in machine learning who willingly share their knowledge and skills