Senior Engineering Manager, Model Inference & Serving, Machine Learning Platform job opportunity at Netflix Inc.



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Netflix Inc Senior Engineering Manager, Model Inference & Serving, Machine Learning Platform
Experience: General
Pattern: Remote
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Data & Insights

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degreeBachelor's (B.Sc.)
loacation United States Of America, United States Of America
loacation United States ..........United States Of America

Netflix is one of the world's leading entertainment services, with over 300 million paid memberships in over 190 countries enjoying TV series, films and games across a wide variety of genres and languages. Members can play, pause and resume watching as much as they want, anytime, anywhere, and can change their plans at any time.Machine Learning/Artificial Intelligence powers innovation in all areas of the business, from helping members choose the right title for them through personalization, to enhancing our understanding of our audience and content slate, to optimizing our payment processing and other revenue-focused initiatives. Building highly scalable and differentiated ML infrastructure is key to accelerating this innovation.More recently, rapid innovation in large language models (LLMs) has significantly advanced state-of-the-art technology in various areas of personalization, including search and recommendation experiences.The OpportunityWe are seeking a Senior Engineering Manager to lead the Model Inference & Serving pillar within the ML Platform organization. This is a pivotal, high-impact “leader of leaders” role, responsible for setting the strategic vision and execution across multiple teams that deliver the core model serving infrastructure for all of Netflix. Your leadership will shape the future of ML model delivery, experimentation, and operational excellence at a global scale.You will:Set the vision and strategyfor all aspects of model inference and serving at Netflix, ensuring our platform supports the next generation of ML innovation, including LLMs, GenAI, and real-time personalization.Lead and develop a cohort of engineering managers and technical leadsresponsible for core functions, including model routing, inference systems, experimentation, serving frameworks, and the performance and scalability of model serving at Netflix.Drive cross-team and cross-functional alignment, collaborating with ML researchers, product engineering, infrastructure, and platform partners to maximize business and member impact.Champion operational excellenceand continuous improvement, ensuring reliability, scalability, and cost-effectiveness across all model serving systems.Key ResponsibilitiesVision & StrategyDefine and communicate the pillar’s multi-year vision, technical strategy, and roadmap.Anticipate future platform and business needs, especially as ML architectures and use cases evolve.Drive the transition from legacy, domain-based serving to a unified, modular, and domain-agnostic serving platform.Leadership & People DevelopmentManage and mentor engineering managers and technical leads; build a strong leadership bench.Foster a culture of high performance, candor, innovation, and inclusion, aligned with Netflix’s values.Attract, hire, and retain outstanding talent across the pillar.Technical Direction & Operational ExcellenceSet and uphold technical standards for reliability, scalability, and performance across all teams.Oversee development of foundational serving infrastructure: real-time/batch inference, frameworks, experimentation, control plane, and tooling.Ensure robust support for diverse model types (deep learning, LLMs, bandits, etc.), hardware targets (CPU/GPU), and SLAs.Own operational health and reliability at scale, including observability, SLOs, and incident response.Cross-Functional & Stakeholder EngagementBuild and maintain strong partnerships with ML practitioners, product engineering, infrastructure, and platform teams.Represent the Model Serving pillar to Netflix senior leadership, clearly communicating the vision, progress, and priorities.Influence and drive alignment on platform direction, investment, and priorities.What You Need to SucceedProven success managing multiple managers in high-scale ML infrastructure/platform environments.10+ years of technical experience, with 5+ years in engineering management roles.Deep expertise in ML model serving, distributed systems, and high-scale production environments.Strategic thinking with a track record of delivering complex, cross-team initiatives.Excellent communication and stakeholder management skills.Experience driving organizational change and leading through ambiguity.Experience with modern ML frameworks (e.g., PyTorch, TensorFlow), inference engines (e.g., Triton, vLLM), and experimentation platforms is a strong plus.MS/PhD in Computer Science, Engineering, or related field, or equivalent experience preferred.Netflix CultureNetflix’s culture is built on values of high performance, candor, context over control, and inclusion. We celebrate diversity and are committed to building teams with a wide range of backgrounds, perspectives, and skills.We offer a flexible, market-driven compensation structure–choose your mix of salary and stock options, with a typical range for this role of 190,000 to 1,195,000. Learn more about ourbenefitsand uniqueculture.Ready to shape the future of ML/AI at Netflix?Apply now and help us deliver world-class ML experiences to millions of members worldwide.Inclusionis a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want anaccommodation/adjustmentfor 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 diversitybuilds 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.

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