Gravity R&D is going to be a Taboola Company
Gravity R&D (or shortly Gravity) is being acquired by Taboola, the leading content discovery platform of the open web. Our office in Budapest, Hungary is to become Taboola’s R&D hub in Central Europe.
Gravity R&D is a personalization engine provider using machine learning to personalize digital customer experiences for SMEs and enterprises.
The Budapest-based company has been focusing on data science since 2009, using machine learning and Big Data analytics to create personalized customer experiences for brands in various industries.
Gravity’s products help clients deliver better brand experiences, drive revenue growth, and improve customer satisfaction. The company’s personalization solutions can provide 35+ billion personalized recommendations per month.
The company is also strongly represented in R&D, and proud to have a data mining team active in the research field of recommender systems, as well as, our researchers are regularly invited to present at top-notch conferences.
Founded in 2007, Taboola is the largest discovery and content recommendation platform in the world. Our clients include hundreds of thousands of publishers (web sites) and advertisers. Every day 3B internet pages present our clients’ content. We get 500K requests for recommendation and process a total of 1.5M HTTP requests per second. Every month, 1.5B unique users in the world are exposed to our recommendations.
In addition, we work with some of the leading carriers and OEMs and provide them with a built-in feed that is an integral part of their users’ experience.
About the Deep Learning Team
You’ll be joining the Deep Learning Team and occasionally will also work with the company’s other teams. The DL team, officially formed in December 2020, is led by Balázs Hidasi, the Head of Research of Gravity R&D.
The Deep Learning Team is responsible for researching, developing, testing, maintaining, and operating the deep learning-based recommender module of Gravity R&D. The deep learning module is separated from the core recommender system. Still, the two often work together to provide high-quality recommendations for our customers.
Occasionally, the team also provides machine learning-related expertise to other teams in the company.
- Developing and maintaining the framework that facilitates our deep learning algorithms.
- Integrating the framework with other systems (e.g. data pipeline of a customer).
- Deploying the deep learning module for new customers.
- Monitoring the performance and the health of the deployed module.
- Executing A/B tests among various algorithms and configurations within the framework and comparing performance with solutions outside the module.
- Improving the existing algorithms by adding new features or speeding up execution.
- Partaking in (mostly applied) research tasks to expand the capabilities of the module.
- Occasionally helping other teams of the company with machine learning-related tasks.
The position is mainly an engineering position with occasional research opportunities.
- You’ll gain hands-on experience on how to secure a robust and efficient service in a real-time, high-load environment.
- You’ll also learn about various machine learning models and what it takes to use these models in production in a cost-efficient way.
- You’ll learn about how recommender systems and algorithms work and how these systems determine what the user is interested in based on his browsing behavior.
- You’ll also have the chance to learn various technologies related to big data or deep learning and learn about online evaluation.
We’re planning to hire for the long-term, and we are committed to finding the most suitable position for each team member. Therefore, by the time, you might focus more on research (ML Research Scientist), engineering (ML Engineer), or development (ML Developer) tasks, depending on your skills, interests, and ambition.
- Knowledge of Python with the common packages used for data science and machine learning (e.g., numpy, pandas, etc.).
- Knowledge of C++ or Java.
- Familiarity with algorithm theory and algorithm complexity.
- Knowledge of general machine learning.
- Good written and oral English.
- Proactive personality, can-do attitude and eager to learn new things.
Nice to have
- You’ve an MSc degree in computer science with relevant studies (or experience) in machine learning. Since we are hiring at various levels of expertise, exceptional candidates with BSc degrees will also be considered and encouraged to apply.
- You know the basics of deep learning.
- You’re familiar with any deep learning frameworks (extra points if the framework is Theano).
- You’re familiar with the basics of CUDA.
- You know of recommender systems and machine learning algorithms for recommendations.
- You know any of the following technologies: MySQL, ClickHouse, Kafka.
- You’ve good Hungarian skills in writing and speaking.
- You are an EU citizen.
Why join us?
- Inspiring startup atmosphere
- We’re flexible about where you work from: our office, your home, or another location of your choice
- Take part in the research, creation, and development of a state-of-the-art technology
- Competitive salary
- Comfortable working conditions – we don’t have a dress code, but we have snacks and beverages, foosball, and table tennis waiting for you at the office
- A wide range of opportunities to grow professionally and build new skills
- Team activities and events
- Beautiful office with a huge garden, in the heart of Budapest
- Flexible working hours and holiday policy
If you are interested in this opportunity, feel free to contact us: firstname.lastname@example.org