Sr. Machine Learning Engineer (Recommendation Systems)

About the role

At Philo, we're a group of technology and product people dedicated to building the future of television, blending modern technology with the most compelling medium ever invented. As a Senior Machine Learning Engineer, you'll lead our content personalization efforts, shaping experiences that impact millions of users.

Responsibilities

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

Qualifications

- 8+ years of experience in backend engineering and/or data science, including 4+ years focused on machine learning. Experience with recommendation systems is a big plus. - Strong coding skills in Python, as well as proficiency in using ML frameworks like PyTorch or TensorFlow. - Excellent analytical and problem-solving skills, with the ability to translate complex technical challenges into business solutions. - Proven track record of leading projects and delivering impactful machine learning solutions. - Strong communication and documentation skills; capable of explaining complex, technical concepts to non-technical stakeholders and to diligently document your work to help the team as a whole learn and move quickly. - Experience with Amazon SageMaker or similar MLOps platforms