As a Machine Learning Engineer you will help us speeding up putting research results into production by working closely with both the Data Mining and the Core Development teams.

The Data Mining team is responsible for scientific research, data related software development, data analysis and tailoring the recommendation logic in order to increase recommendation efficiency. The Core Development team is responsible for constantly improving our recommendation engine and adding new features to the product.

• You will be responsible for improving and maintaining our framework that allows us to use our deep learning models efficiently in production.
• You will also be responsible for designing and implementing interfaces/frameworks that allow data miners to quickly and efficiently try new algorithms in production without needing to reimplement them in our own system. Later, it will be possible for you to get more involved in optimising algorithm implementations (to make them computationally less expensive) and you might even contribute to the research efforts of the company.

About us

Gravity R&D is a personalisation engine vendor offering a product portfolio called Yusp, filling multiple needs using the same underlying technology.

The team behind Yusp, as an R&D company, has been focusing on Data Science since 2006: the research and development of recommendation algorithms and their applications to a multitude of business models.

With customers in over 25 countries Yusp provides SaaS personalisation solutions in a close cooperation with Deloitte to Content Providers, Retailers, Marketplaces, TMT Companies and Retail Banks worldwide.

Requirements

• excellent mathematical and algorithmic skills
• relevant university degree or studies
• experience in Java or Python
• precise, creative and proactive personality
• good communication skills

What we offer

• You will gain hands-on experience on how to ensure robust and efficient service in a real-time, high load environment.
• You will also learn about various machine learning models and what it takes to use these models in production in a cost-efficient way
• You will also have the chance to learn various technologies related to big data or deep learning; and learn about online evaluation
• You will learn about how recommender systems and algorithms work and how these systems figure out what you are interested in based on your browsing behaviour.