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Stay22 is a Montreal-based monetization platform that helps creators and digital platforms transform existing traffic into additional revenue. The company powers over 6,500 creators and platforms with more than $1 billion in annual transactions, serving travel, retail, lifestyle, publishing, events, and transportation sectors.
You will join the Neuro Squad, a specialized team centralizing ML and AI innovation at Stay22. This senior role owns the full lifecycle of machine learning systems—from training pipeline design to real-time inference API optimization. You'll architect the intelligence behind Roam, Stay22's ML-powered redirection engine, and work with cutting-edge technologies including large language models.
Key responsibilities include: designing and maintaining backend logic and model behavior for Roam with high precision; continuously improving models for ranking quality, latency, and revenue efficiency using advanced statistics and deep learning; developing high-performance models for user intent prediction from complex tabular and booking data; designing and operating the complete ML lifecycle (data prep, training pipelines, model registries, real-time inference); managing cache layers, inference servers, and performance monitoring for low-latency, high-volume engines; deploying robust, maintainable, production-ready solutions aligned with CI/CD and security standards; collaborating with the Data Squad on training datasets and feature pipelines; exposing ML capabilities as robust APIs for other teams; and mentoring colleagues on ML prototyping and AI best practices.
Required qualifications: Master's degree in computer science, machine learning, data mining, statistics, or related technical field; 6+ years of machine learning or data science experience; minimum 2 years of software engineering experience including production backend code or APIs; expert-level Python with deep learning libraries (PyTorch, TensorFlow, scikit-learn); solid understanding of modern ML techniques (CNNs, LSTMs/RNNs, gradient boosting); demonstrated ability to implement and optimize LLMs, Transformers, custom embeddings, and LLM gateways; experience with Google Vertex AI (preferred) or Amazon SageMaker; strong expertise designing real-time inference systems and APIs; experience with MLOps tools (MLflow, Kubeflow, TFX) and containerization (Docker, Kubernetes); excellent SQL skills and feature store/ETL collaboration experience; ability to design reusable, robust systems for other teams; interest in translating complex ML metrics into business value; and commitment to staying current with AI infrastructure and modeling techniques. Fluent English required for daily communication with international colleagues.
