At Netflix, the Software Engineer (L4) - Personalization role is pivotal in crafting the algorithms and systems that power our world-class recommendation engine, directly influencing how millions of members discover and enjoy content daily. This position offers the chance to impact billions of viewing hours by creating personalized experiences that keep users engaged and loyal to our platform. Your work will shape the future of streaming entertainment, leveraging massive datasets to deliver hyper-relevant suggestions that drive subscriber satisfaction and retention. As a Software Engineer in Personalization, you will design, build, and optimize scalable machine learning models for content recommendations, collaborating daily with data scientists, product managers, and other engineers to iterate on personalization features. Weekly, you'll analyze user behavior metrics like click-through rates and watch time to refine algorithms, conduct A/B tests on new ranking systems, and deploy updates to production environments handling petabytes of data. Monthly responsibilities include leading cross-functional projects to integrate personalization into new product areas, such as homepages or search, while mentoring junior engineers on best practices in distributed systems. You'll deliver key metrics improvements, like boosting recommendation accuracy by 5-10%, and partner with content acquisition teams to incorporate metadata signals into models. Expect hands-on coding in high-stakes environments, troubleshooting latency issues in real-time services, and contributing to open-source tools that advance the field. Candidates must hold a Bachelor's or Master's degree in Computer Science, Statistics, or a related field, with at least 4-6 years of professional software engineering experience in large-scale systems. Proficiency is required in programming languages like Java, Scala, Python, or C++, along with expertise in machine learning frameworks such as TensorFlow or PyTorch, and big data technologies including Spark, Kafka, and Hadoop. Strong knowledge of recommendation systems, natural language processing, and distributed computing is essential, particularly experience with A/B experimentation platforms and handling terabyte-scale datasets. Familiarity with cloud platforms like AWS or GCP and containerization tools like Docker and Kubernetes is expected. Beyond technical prowess, success demands excellent problem-solving abilities, strong communication skills to articulate complex ideas to non-technical stakeholders, and a collaborative mindset for thriving in agile teams. You should exhibit curiosity about user psychology and personalization trends, adaptability to fast-paced iterations, and a track record of innovation in ML-driven products. Proficiency in data visualization tools like Tableau and version control with Git will further set you apart. Netflix, the world's leading streaming entertainment service, boasts over 260 million paid memberships in 190 countries, fueled by a mission to entertain the world with unparalleled storytelling. Our engineering culture emphasizes freedom and responsibility, fostering small, autonomous teams that innovate without bureaucracy. We prioritize diverse perspectives, continuous learning, and a results-oriented environment where top talent pushes boundaries in content personalization. We offer competitive compensation, though salary details are not specified, paired with comprehensive benefits including health, dental, and vision insurance, 401(k) matching, and generous paid time off. Enjoy unique perks like unlimited vacation policy, wellness stipends, annual learning budgets, and stock options that align with our growth. Netflix supports flexible hybrid work arrangements, parental leave, and employee resource groups for holistic well-being. Advance your career through internal mobility to senior roles or leadership in personalization, with abundant professional development via conferences, hackathons, and mentorship programs. To apply, visit Netflix's careers page and submit your resume highlighting relevant experience.