Philo - Sr. Machine Learning Engineer (Recommendation Systems)

• Lead development of recommendation systems: Design, build, and optimize advanced algorithms for SVOD, Live TV, and FAST personalization. • Lead development of recommendation systems: • Drive ML innovation at scale: Conduct deep dives into models and system components, ensuring performance, scalability, and robustness across regions and product areas. • Drive ML innovation at scale • Own the ML pipeline: Build and maintain reliable pipelines for data extraction, feature engineering, model training, testing, and deployment. • Own the ML pipeline: • Collaborate with Product, Data Science & Engineering: Translate product requirements into ML solutions, set clear expectations, and deliver measurable improvements in user engagement. • Collaborate with Product, Data Science & Engineering: • Advance deep learning in recommendations: Apply frameworks such as TensorFlow, PyTorch, or similar to develop state-of-the-art recommendation models. • Advance deep learning in recommendations • Experimentation: Conduct rigorous A/B testing and ML experiments to understand model performance and iterate rapidly based on feedback. • Experimentation • ML Vision and Roadmap: Contribute to the strategic planning of the recommendations roadmap, aligning engineering efforts with business objectives and user needs. • ML Vision and Roadmap: • Explore advanced architectures: Experience with frameworks like Two-Tower models and Deep Cross Networks (DCN) is a strong plus. • Explore advanced architectures: