Staff ML Software Engineer (L6) — Platform Systems, AIMS Engineering

Netflix•Los Gatos, CA

5d•$600,000 - $1,066,000

About The Position

AI for Member Systems (AIMS) runs the AI systems behind every recommendation, search result, and personalized experience for 300M+ members. The stack powering it is large and battle-tested, built to meet the demands of its time, and remarkably effective at doing so. But AI/ML is moving fast, and the infrastructure that got us here needs to evolve to meet what's next: new model paradigms, tighter cost and efficiency expectations, and the operational maturity that comes with running AI at this scale. Migrating to a next-generation AI/ML platform is one of the highest-leverage programs in AIMS. So is building the observability and cost infrastructure that makes that platform trustworthy. This role owns that problem end-to-end. Platform Systems is the engineering foundation of AIMS, owning reliability, scalability, cost efficiency, and developer experience across the org. We are looking for a Staff ML Software Engineer to own the technical health of the AIMS AI/ML stack — modernizing it, and building the observability and cost infrastructure that makes that modernization trustworthy. This is a high-leverage, cross-cutting role — the work you do here will define how AIMS builds AI/ML systems for the next decade. While the initial migration marks our first major initiative, our ongoing goal is to establish sustainable practices for the long term.

Requirements

Significant experience designing, building, and operating large-scale production AI/ML systems, including training pipelines and familiarity with model serving and online inference at high-traffic scale
Hands-on experience migrating production AI/ML systems across technology generations; you have done this before and understand where it goes wrong
Strong software engineering fundamentals with deep Python expertise and working proficiency in at least one JVM language (Scala or Java)
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; you understand what good visibility looks like across training, serving, and data pipelines
Strong distributed systems background, including large-scale batch processing and real-time serving infrastructure
Collaborate with partner teams to drive cross-functional technical programs, setting direction, managing dependencies, and building consensus without formal authority
High technical judgment: able to identify common patterns, build reusable frameworks, and make pragmatic calls on what to migrate, what to rewrite, and what to leave alone
Comfortable operating without full information; you can scope a problem, define an approach, and course-correct as you learn more

Nice To Haves

Experience with compute and cost optimization for AI/ML workloads at scale, including capacity management and efficiency tooling
Hands-on experience building GenAI-powered tooling for operational automation, root cause analysis, or anomaly detection in AI/ML systems
Experience building developer tooling or platform abstractions that improve AI/ML practitioner velocity
Applied experience in personalization domains such as recommendation systems, search, or discovery
Familiarity with modern AI/ML infrastructure patterns including feature stores, model serving platforms, and experiment frameworks

Responsibilities

Define the end-state architecture for the modernized AIMS AI/ML stack: how it is organized, what contracts each layer exposes, and what the migration path looks like across training pipelines, AI frameworks, and data infrastructure
Drive end-to-end migration of AIMS AI/ML systems onto a modern, Python-native platform, coordinating across multiple AIMS teams and external platform partners, with dozens of production models in flight
Build migration tooling and shared abstractions that reduce the cost of adoption for individual teams, so modernization does not require each team to solve the same problems independently
Own scalability across training throughput and data pipelines, ensuring AIMS AI/ML systems stay performant as model complexity and member traffic grow
Design and build observability systems that give AIMS AIMS ML practitioners deep visibility into model behavior, training pipeline health, serving latency, and data quality, making issues detectable and diagnosable before they become incidents
Identify and drive cost optimization across AIMS training and serving infrastructure, developing frameworks and tooling that make compute efficiency a first-class concern, not an afterthought
Architect reliability improvements across the AIMS AI/ML stack, reducing toil, improving on-call ergonomics, and setting the standard for operational excellence across the org
Prototype and productionize GenAI-powered tooling for anomaly detection, root cause analysis, and operational automation, applying LLM-based systems to the problems of AI/ML reliability and cost at scale
Surface systemic cost, reliability, and migration gaps by embedding with AI/ML teams across AIMS, and translate their friction into concrete engineering investments with org-wide leverage
Set technical standards for the modernized stack and raise the engineering bar across AIMS through design reviews, architectural guidance, and leading by example
Own the long-term architectural evolution of the AIMS AI/ML stack — continuously evaluating emerging infrastructure patterns, model paradigms, and platform capabilities, and translating them into a forward-looking roadmap before they become urgent migrations