AI Engineer 6 - AI Foundation & Tooling, Ads Platform

We launched a new ad-supported tier in November 2022 to offer our members more choice in how they consume their content. Our new tier allows us to attract new members at a lower price point, while also creating a compelling path for advertisers to reach audiences that are deeply engaged.

Our Team

The Ads Platform Engineering teams build advertising systems and integrations that powers the delivery of ads using our world class content delivery ecosystem. We use a number of Netflix investments and innovations to power our ads - unique mix of client and server side ad insertions, state of the art content delivery system, ad encoding recipes, content understanding and metadata etc. We deliver ads in a manner that’s thoughtful of our member’s viewing experience and drive great outcomes for advertisers. We also ensure that advertiser brand safety is ensured during serving, members only see the most appropriate ads for them.

About the Role

We're looking for a Staff AI Engineer to join us as the first dedicated AI engineer. Your core mission is to build the AI infrastructure and agentic workflows that transform how our team develops and operates software.

This is a greenfield opportunity. You won't be maintaining existing AI systems — you'll be designing and building them from scratch. Our team has active AI champions and early tooling already in production, but we need someone with deep hands-on experience to architect the foundational layer and accelerate our path to AI-native engineering. Our code footprint is reasonable today but expanding at an extraordinary pace; 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. You'll 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.

What You’ll Do

AI Infrastructure & Agentic Workflows

• 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

Skills & experience we’re seeking:

• 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