Sr. Machine Learning Engineer (Recommendation Systems)

Job Overview Senior Machine Learning Engineer (Recommendation Systems) leads Philo’s 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, collaborating with data science, product, infrastructure, and backend engineering teams to deliver innovative, data‑driven personalization solutions. 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 diligently document your work to help the team as a whole learn and move quickly. Experience with Amazon SageMaker or similar MLOps platforms. Location & Status Full‑time position. Location: San Francisco, CA or remote within the U.S. Compensation Includes annual salary, company stock options, and health benefits. Salary ranges are determined by experience and location: San Francisco, New York City: $175K – $270K Boston, DC Metro, Los Angeles, Seattle: $165K – $250K Denver, Atlanta, Austin, Las Vegas, Sacramento, Chicago: $155K – $240K Texas, Florida: $150K – $230K Benefits Full health, dental and vision coverage for you and your family 401(k) plan with employer contributions (we match 100% of deferrals up to 3% of pay and 50% of the next 2% of pay) Flexible working hours Up to 20 weeks of fully paid parental leave Unlimited paid time off for vacation and sick leave $2,000 annual vacation bonus (we pay you to take a two‑week vacation) $5,250 annually for professional development and educational assistance $1,250 annual home office + TV stipend during first year of employment ($250 annually thereafter) $500/month ($6,000/year) bonus for employees who commit to working at least 3 days per week in our offices, plus generous commuter benefits ($315/month towards transit, rideshare, bike rental, or parking at our HQ office in San Francisco) Free Gympass subscription — an all‑in‑one corporate benefit that gives employees the largest selection of gyms, studios, classes, training and wellness apps Dog‑friendly office And much more! EEO & Diversity Philo is proud to be an Equal Opportunity Employer. We’re committed to supporting every candidate and employee. If you need an accommodation at any stage of the process, please email View email address on click.appcast.io and we’ll work with you to meet your needs. Philo is committed to diversity, equity, inclusion, and accessibility as we grow the team and shape the future of TV. We believe that a diverse range of voices and perspectives enables us to innovate faster and create the best experiences for our subscribers. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. #J-18808-Ljbffr