Senior Machine Learning Engineer

About VYBS

VYBS is on a mission to empower mobile gamers worldwide to discover their perfect games and be rewarded for their passion. We leverage data, cutting-edge technology, and deep understanding of gamer preferences to deliver personalized recommendations, exclusive rewards, and a vibrant community that celebrates the joy of mobile gaming.

We’re forming the core of our ML team and are looking for engineers who want to own high-impact ML systems end-to-end, from modeling to production. Your work will directly influence revenue, user experience, and platform integrity.

With hundreds of thousands of active users and rapid growth, this is a chance to build foundational ML systems at scale.

About the Role

We're seeking talented individuals to form the foundation of our ML team. Joining us, you will get to build state of the art ML algorithms and infrastructure that directly impact the bottom line. Some of the algorithms we work on are dynamic pricing, real time bidding, game economy generation, feed recommendation systems, fraud detection and more. You’ll be the first ML engineer, working directly under the CTO.

With hundreds of thousands of active users on our platform we're growing fast. Join us!

What You’ll Do

• Work with rich, high-volume behavioral data from millions of mobile gaming sessions across the gaming ecosystem.
• Design and deploy ML models for marketplace matching, dynamic pricing, personalized recommendations, fraud detection, and game economy simulation.
• Build and maintain ML infrastructure: training pipelines, feature stores, model serving, and monitoring systems.
• Partner closely with product and engineering to ship models that move core business metrics.

Must-haves

• 5+ years of software engineering experience.
• 3+ years of hands-on ML engineering or applied data science experience.
• Proven experience running real time ML systems in production.
• Strong foundation in ML methods (e.g., neural networks, tree-based models, linear models, Bayesian approaches).
• A bias toward shipping, iterating, and taking ownership in a fast-moving environment.