• Build reusable CI/CD workflows for model training, evaluation, and deployment • Automate model versioning, approval workflows, and compliance checks across environments • Partner with engineering and data science to embed AI models into real-time applications • Drive AI reliability and governance, enabling security and uptime

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next. Data Science and Engineering (‘DSE’) at Netflix is aimed at using data, analytics, causal inference, machine learning (ML), and sciences to improve various aspects of our business. The AI initiative at Netflix Games is dedicated to pioneering the next generation of interactive entertainment. We have the ambition to transform how players interact with stories, characters, and worlds by empowering gameplay experience with AI. We work at the intersection of creative game design and cutting-edge machine learning, ensuring that dynamic storytelling is not only novel but also coherent, immersive, and safe for our players. We are seeking an experienced L5 ML Scientist specialized in forecasting and audience research. In this role, you will: - Build Foundational ML Building Blocks: Develop sophisticated embeddings and models that incorporate deep game-specific signals to solve high-impact business problems, including audience insights, opportunity sizing, and forecasting. - Accelerate Product Development: Build the tools, models, and pipelines required to accelerate DSE workflows across games portfolio, studios, product, and platform. - Bridge the Netflix Ecosystem: Act as a key liaison with the broader Netflix DSE and AI teams to adopt, adapt, and tailor global Netflix capabilities for the unique requirements of the gaming space. - Design Scalable Pipelines: Create end-to-end ML pipelines that accelerate and enable DSE members across games to uncover actionable insights and build data-intensive game features. - Elevate ML Practices: Establish the technical standards for how ML capabilities are applied across game domains. Who Will Succeed in This Role: - Ph.D. in Computer Science, Machine Learning, or a related quantitative field. - 5+ years of experience leading complex, end-to-end ML projects that impact end-customer experiences. - 3+ years of experience navigating large-scale technical organizations to align roadmap priorities and share infrastructure - You can digest the latest research paper in the morning and ship a functional prototype or foundational model by the afternoon. - You can bridge the gap between technical ML architecture and business objectives, translating product needs into rigorous technical specifications. - You thrive in zero-to-one environments, enjoying the freedom to choose your stack and define the engineering standards for a new domain. - You have a foundational understanding of causal inference principles, allowing you to discern when a predictive model is sufficient vs. when a causal approach is required. - You have a passion for developing reusable ML capabilities to unlock and accelerate development work broadly. Nice to Have: - Experience working with game development teams, particularly in game design and engineering. - Experience with building production-grade ML systems, including MLOps best practices. - Have strong engineering skills, particularly in designing and optimizing evaluation frameworks (e.g., Python, PyTorch, LangChain). Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is . This compensation range will vary based on location. Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here. Netflix is a unique culture and environment. Learn more here. Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner. We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service. Job is open for no less than 7 days and will be removed when the position is filled.

• Lead the design and development of personalization in the P+ sign-up flow and Pluto TV registration flows. • Own end-to-end machine learning pipelines—from data and feature engineering to training, deployment, serving, and monitoring. • Partner closely with product, design, content, platform engineering, and data science to define roadmaps and deliver measurable user outcomes. • Advance our semantic search and browse experience through state-of-the-art embeddings, query understanding, and domain-specific model architectures. • Establish high-integrity experimentation practices, improve offline→online correlation, and guide feature rollouts with strong scientific rigor. • Mentor engineers and scientists, develop technical talent, and help shape the culture of our growing Applied ML organization.

• Design, develop and maintain scalable data pipelines for data ingestion, processing and delivery; • Develop and optimize data processing routines using Python and Spark; • Create and optimize SQL queries, ensuring performance and data quality; • Work with data architecture in AWS environments, ensuring scalability and security; • Integrate data from different sources and corporate systems; • Support the deployment, monitoring and maintenance of Machine Learning models (MLOps); • Implement versioning practices and pipeline automation; • Work with messaging tools such as Kafka; • Monitor pipelines and applications using tools like Datadog; • Collaborate with business teams to translate needs into data solutions.