About ITRex
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.
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.
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 Requirements
Technical Skills
4+ years of experience as a Software Engineer, with at least 3 years in an ML Engineer role
Strong understanding of machine learning techniques, including supervised & unsupervised learning, NLP, deep learning fundamentals, and model evaluation
Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-Learn, Pandas, and NumPy
Hands-on experience in containerizing ML applications using Docker for scalable deployment
Practical experience with at least one cloud provider (AWS, GCP)
Strong background in working with large datasets, SQL/NoSQL databases
Ability to decompose complex problems into well-structured ML tasks
Skilled at assessing whether ML is the best approach or if a simpler solution (e.g., heuristic rules, statistical methods) would be more effective
Expertise in debugging, optimizing, and enhancing models for performance, efficiency, and interpretability
Experience maintaining ML workflows to ensure reproducibility, scalability, and operational efficiency Business & Collaboration
Excellent communication skills, capable of explaining ML concepts to both technical peers and non-technical stakeholders
Collaborative, product-focused approach within Agile, cross-functional environments
Proactive mindset with a strong sense of ownership with the ability to lead ML tasks end-to-end, from discovery and experimentation to production deployment and support
Experience working closely with Product, Engineering, Data, DevOps, and business teams to align technical solutions with business goals
Continuous learning mindset with awareness of current ML/AI trends, tools, and best practices
English proficiency at an Upper-Intermediate level or above Nice to have
Understanding the business impact of ML models and how to align them with organizational goals
Experience with feature stores, model registries, and ML model lifecycle management
Experience designing and developing Retrieval-Augmented Generation (RAG) solutions
Hands-on experience with AI tools in ML workflows Benefits
Why people stay
First, the foundation:
Remote flexibility: Work where and how you work best - we trust you to deliver
Fair compensation: Competitive salary + benefits that matter (medical, learning)
Then, the growth:
Ownership opportunities: See a problem worth solving? Own it. We back smart risks over bureaucratic safety
AI enhancement: We leverage AI to make you faster and stronger - complementing your abilities, not replacing them
Learning investment: English classes, professional development
Career progression: Real paths up, not just sideways shuffling
Finally, the people:
Responsive teammates: No ignored Slacks, no "not my problem" attitudes
Supportive culture: When you're stuck, people help. When things break, we fix them together
Human connections: Regular meetups, tech talks, and actual relationships beyond work
Curious? We are too. Let's talk
