AI Engineer 6 - AI Foundation & Tooling, Ads Platform

Netflix•Los Gatos, CA

1d•$466,000 - $750,000

About The Position

Netflix launched a new ad-supported tier in November 2022 to offer members more choice in how they consume their content. This new tier allows Netflix to attract new members at a lower price point, while also creating a compelling path for advertisers to reach audiences that are deeply engaged. The Ads Platform Engineering teams build advertising systems and integrations that power the delivery of ads using Netflix's world-class content delivery ecosystem. They use a unique mix of client and server side ad insertions, state-of-the-art content delivery system, ad encoding recipes, content understanding and metadata to deliver ads in a manner that’s thoughtful of the member’s viewing experience and drives great outcomes for advertisers. They also ensure that advertiser brand safety is ensured during serving, and members only see the most appropriate ads for them. We're looking for a Staff AI Engineer to join us as the first dedicated AI engineer. The core mission is to build the AI infrastructure and agentic workflows that transform how the team develops and operates software. This is a greenfield opportunity where the engineer will be designing and building AI systems from scratch. The team has active AI champions and early tooling already in production, but needs someone with deep hands-on experience to architect the foundational layer and accelerate the path to AI-native engineering. A core part of this role is striking the right balance between AI-driven speed and quality — shipping faster without accumulating slop, regressions, or accountability gaps. This role focuses on applied AI — building production systems with existing models and tools, not training LLMs. The engineer will leverage large language models, agentic frameworks, and retrieval-augmented generation to solve real infrastructure and product problems, with a direct impact on how quickly and confidently the team can deliver in a competitive market.

Requirements

3+ years of significant focus on applied AI systems
Proven experience building and deploying agentic AI systems in production — agent architectures, tool integration, orchestration, and evaluation frameworks
Hands-on experience setting up AI infrastructure for end-to-end software development workflows (AI coding assistants, context engineering, automated testing)
Strong software engineering fundamentals — you build production-grade systems, not just prototypes
Deep experience with retrieval-augmented generation — document indexing, embedding strategies, retrieval pipelines, and grounding techniques
Proficiency in Python and/or JVM languages
Demonstrated ability to drive technical adoption across a team — you can demonstrate value, build trust through pairing and architecture reviews, and bring engineers along on new workflows

Nice To Haves

Prior experience as the first or early AI engineer on a team — standing up AI capabilities where none previously existed, with the ownership and initiative of an early-stage environment
Familiarity with LLM application patterns: context engineering, tool use / function calling, structured outputs, multi-agent coordination, and evaluation / hill-climbing methodologies
Experience integrating AI into CI/CD pipelines (automated PR review, test generation, deployment validation)
Background in building operational tooling — incident response automation, log analysis, diagnostic workflows

Responsibilities

Architect and build a centralized context layer that gives AI agents grounded, team-specific knowledge
Design and implement agentic workflows for the full development lifecycle: AI-assisted code generation, automated test creation, PR pre-review, and deployment validation
Build AI-powered operational workflows — automated incident triage, log and metric correlation, root cause analysis, and guided resolution
Develop multi-agent orchestration where parallel agents handle implementation, testing, and documentation as coordinated workflows
Set up standardized AI development environments so every engineer can work in an AI-first workflow from day one
Drive team-wide AI adoption through hands-on enablement — pairing sessions, architecture reviews, workflow demonstrations, and continuous feedback loops