Technical Architect (Business Applications)

Join Roku as a Senior Machine Learning Engineer, where you'll drive the AI-native transformation of our advertising business operations. You'll work closely with advertising business stakeholders, product management, and engineering teams to apply machine learning and agentic AI systems across the entire advertising business lifecycle. Your work will directly influence how a multi-billion-dollar revenue base is planned, executed, and measured. This role requires a strong foundation in machine learning and statistical modeling, as well as experience in building and scaling production ML and AI systems.

• Conduire l'évolution de l'entreprise en utilisant l'apprentissage automatique et les systèmes d'IA agentique.
• Développer et superviser la stratégie technique de la plateforme de Roku pour soutenir diverses applications commerciales.
• Concevoir et expédier des systèmes de ML et d'IA agentique de production de bout en bout, y compris la recommandation, la personnalisation, le classement, la prévision, la détection d'anomalies.
• Experience in advertising, marketplaces, e-commerce, travel, or similar data-rich, decision-driven platforms is a strong plus
• Demonstrated technical leadership, including setting direction, influencing across teams, and elevating the work of other engineers
• Proven experience building and scaling production ML and AI systems, including LLMs, RAG architectures, embeddings, retrieval-based systems, and multi-agent or agent-based system design
• Experience with cloud platforms (AWS, Azure, Google Cloud), microservices, containerization (Docker, Kubernetes), and DevOps
• Hands-on experience designing, training, tuning, and deploying models for ranking, prediction, recommendation, forecasting, classification, or NLP use cases
• 10+ years of hands-on engineering experience, with a track record of technically leading and delivering large-scale assistant or autonomous systems
• Master's or PhD in Computer Science, Mathematics, Statistics, or a related technical field, or equivalent practical experience
• Strong foundation in machine learning and statistical modeling, including clustering, classification, regression, decision trees, neural networks, SVMs, and anomaly detection, with deep understanding of supervised and unsupervised learning, feature engineering, model evaluation, bias-variance tradeoffs, and offline vs. online metrics