Engineering Manager, AI Observability

Netflix•Los Gatos, CA

About The Position

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. AI and ML powers innovation in all areas of the business, including helping members choose the right title for them through personalization, better understanding our audience and our content slate, creating high-quality subtitles, dubbings, images, trailers, and other assets, optimizing our payment processing, and much more. The Artificial Intelligence Platform (AIP) organization builds highly scalable, differentiated AI infrastructure to maximize the business impact of all AI/ML practitioners at Netflix, which is key to accelerating this innovation. The Opportunity The AI Observability team makes AI, ML, and Agentic systems transparent, reliable, and production-ready at scale. We build end-to-end observability for ML and GenAI workloads, capturing model inputs, features, predictions, outcomes, and behavior across online and batch systems. Our platform enables teams to monitor model performance, data quality, drift, latency, and failures, turning the ML system from a black box into an explainable, debuggable system. We provide developer-friendly libraries, dashboards, and alerts so teams can debug issues, respond to incidents, and ship AI-powered products with confidence. We are looking for an experienced AI/ML infrastructure engineering leader to build and lead the next generation of our AI observability platform. You will lead this newly formed team to architect, design, develop, test, and launch a brand-new platform to enable ML practitioners across different business domains to effortlessly collect model inputs, features, and predictions for thousands of large-scale models, including Large Language Models (LLMs), computer vision, and foundation models. We are a highly collaborative team. You will be highly cross-functional in partnering with other engineering, product management, machine learning, and data teams to take Netflix’s AI/ML initiatives to the next level. To succeed in this role, you will need a strong background in AI infrastructure and a passion for building scalable, robust systems that enable and accelerate the application of AI Observability to large, complex ML models across diverse domains.

Requirements

10+ years of software engineering experience and 3+ years of management experience.
Experience leading teams responsible for building high-traffic distributed systems and ML infrastructure
Deep familiarity with AI and ML operations, including model evaluation, drift detection, and continuous monitoring at scale.
Experience with AI observability and monitoring tools (e.g., Arize AI, Fiddler AI, Weights & Biases, Vertex AI Model Monitoring, SageMaker Model Monitor)
Exposure to LLM or generative AI systems, including prompt/result logging, evaluation metrics, LLM-as-a-judge frameworks, and human-in-the-loop review
Strong technical acumen and can act as a credible technical advisor to the team, set and enforce a high-quality bar for code and system design, and be a mentor for the team.
Strong communication and collaboration skills, and the ability to build strong relationships with internal customers and external partners.
A demonstrated ability to develop, drive, and execute a technical vision and roadmap.
Experience managing a hybrid team with partners and team members distributed across (US) geographies & time zones.

Responsibilities

Partner with ML researchers, engineers, and platform teams to embed “observability-by-default” into new AI services, ensuring telemetry, monitoring, and evaluation are built into systems from day one.
Lead the end-to-end observability strategy for AI workloads, including LLMs, generative AI systems, and classical ML models; driving build vs. buy decisions, and scaling solutions across model training, online inference, and agent orchestration
Drive the evolution of LLM evaluation frameworks, covering prompt instrumentation, response quality measurement, grounding correctness, hallucination rates, and human/LLM‑as‑a‑judge scoring.
Define and execute a platform roadmap focused on incremental delivery, with clear success metrics, migration goals, and strong adoption across teams.
Communicate progress to stakeholders, customers, and senior leadership.
Hire, grow, and mentor a high-performing engineering team while fostering an inclusive and collaborative culture.