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