About ITRex

THE PLACE

ITRex - AI pioneers who build systems that actually work in the real world, not just in demos. We're 250+ people spread across the US and Europe, creating solutions for companies like Procter & Gamble and Shutterstock. We keep it simple, build it right, and focus on what works.

THE PEOPLE

We're the kind of people who don't ignore messages in Slack, who jump in to help when you're stuck on a problem, and who offer solutions instead of blame when things go sideways. We believe in openness, accountability, and having each other's backs. No office politics, no hidden agendas - just people who care about doing good work together and supporting each other to get there.

THE ROLE

We are looking for an ML Engineer to join a large-scale live-streaming and social interaction platform that powers multiple mobile applications for dating, communication, video chats, and live broadcasts. Every month, the platform delivers more than 1 billion minutes of live-streaming sessions to users worldwide.

As an ML Engineer, you will take end-to-end ownership of ML initiatives: from problem discovery and requirements definition to solution design, implementation, deployment, and post-production optimization. You will work closely with Product, Engineering, Data, DevOps, and business stakeholders to design and deliver scalable ML-driven features that directly impact user engagement, matching quality, recommendations, moderation, and overall platform experience.

Your Responsibilities

• Design, develop, and deploy machine learning models for predictive analytics, classification, NLP, and other data-driven tasks
• Implement data pipelines for ingestion, preprocessing, feature engineering, and model training

• Containerize ML models and applications using Docker for scalable and reproducible deployments
• Deploy and maintain ML solutions in cloud environments (AWS/Snowflake)
• Optimize model performance, latency, and resource utilization for real-time or batch inference
• Monitor and troubleshoot ML models in production, ensuring reliability and robustness
• Сollaborate with Product, Engineering, Data, and business stakeholders to define project requirements and integrate ML models into production systems
• Conduct rigorous model evaluation using appropriate metrics to ensure performance and fairness
• Assess whether machine learning is necessary for a given problem or if alternative rule-based/statistical approaches are more appropriate