Sr. Machine Learning Engineer (Recommendation Systems)

At Philo, we’re a group of technology and product people who set out to build the future of television, marrying the best in modern technology with the most compelling medium ever invented — in short, we’re building the TV experience that we’ve always wanted for ourselves. In practice this means leveraging cloud delivery, modern tech stacks, machine learning, and hand-crafted native app experiences on all of our platforms. We aim to deliver a rock solid experience on the streaming basics, while cooking up next generation multi-screen and multi-user playback experiences. Senior Machine Learning Engineer (Recommendation Systems) Philo’s recommendation system improves user engagement and customer satisfaction by tailoring content discovery to individual preferences and viewing habits. We want users to be confident that Philo will have something they want to watch every time they open the app. We are seeking a Senior Machine Learning Engineer to lead our content personalization efforts, shaping experiences that impact millions of users. In this role, you will research, design, and build advanced algorithms and large-scale systems that power Philo’s recommendation engine. As a senior member of a growing team, you will tackle complex machine learning challenges and collaborate with data science, product, infrastructure, and backend engineering teams to deliver innovative, data-driven personalization solutions. Your work will directly impact content discovery, deepen user engagement, and drive long-term retention. 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 More about Philo At Philo, we’re a company that puts people first—both our subscribers and our team. We empower our colleagues to do their best work while supporting one another in pursuing shared goals. We value pragmatism, pride in our work, and passion, with transparency and openness as fundamental parts of our culture. We’re committed to diversity, equity, inclusion, and accessibility as we grow the Philo team and shape the future of TV.