24 days ago
Base Salary
$600k - $1066k/yr
Responsibilities
- Design, build, and operate observability, evaluation, and tooling subsystems for next-generation ML architecture.
- Prove new subsystems through current AIMS operations, including anomaly detection, root cause analysis, and operational automation.
- Build observability systems covering model behavior, training pipeline health, serving latency, and data quality.
- Drive cost optimization across AI/ML training and serving infrastructure through increasingly automated frameworks and tooling.
- Architect reliability improvements, reduce operational toil, improve on-call ergonomics, and establish operational excellence standards.
- Contribute to the target architecture and migration path for the modernized AIMS AI/ML stack.
- Evaluate emerging infrastructure patterns, model paradigms, and platform capabilities and translate them into a forward-looking roadmap.
Requirements
- Significant experience designing, building, and operating production AI/ML systems at scale, including training pipelines and model serving or online inference under high traffic.
- Hands-on experience building orchestration or control subsystems for advanced agentic architectures, such as memory, trace, evaluation, replay, routing, or orchestration layers.
- Strong software engineering fundamentals, deep Python expertise, and working proficiency in Scala or Java.
- A proven track record of improving AI/ML system reliability, reducing infrastructure costs, and improving operational scalability.
- Experience building observability and monitoring systems for AI/ML workloads across training, serving, and data pipelines.
- Strong distributed systems background, including batch processing at scale and real-time serving infrastructure.
- Experience driving technical programs across functions, managing dependencies, setting direction, and building consensus without formal authority.
- Strong technical judgment and the ability to identify reusable patterns, prioritize investments, and make pragmatic tradeoffs.
- Ability to scope problems, define approaches, and adapt in ambiguous environments.
- Preferred: familiarity with LLM evaluation, trace or replay tooling, modern AI/ML infrastructure patterns, feature stores, model-serving platforms, experiment frameworks, production AI/ML migrations, and personalization domains such as recommendations, search, or discovery.
Categories
About Netflix
Netflix builds and operates a global streaming service for TV series, films, games, and live programming, and produces original content through its in-house studio. It serves consumers in 190+ countries via subscription and ad-supported plans on internet-connected devices. Founded in 1997 and headquartered in Los Gatos, California, Netflix is a public company traded on NASDAQ (NFLX).