ML Engineer

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

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