Senior Engineering Manager - Agent Platform, AI Platform

Netflix•Remote, OR

69d•$676,000 - $1,195,000•Remote

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. Machine Learning/Artificial Intelligence powers innovation in all areas of the business, from helping members choose the right entertainment for them through personalization, to better understanding our audience and our content slate, to optimizing our payment processing and other revenue-focused initiatives. Building highly scalable and differentiated ML infrastructure is key to accelerating this innovation. The Opportunity Agents are becoming a primary way software gets built and work gets done at Netflix — and the AI Platform is building the foundation they run on. The Agent Platform team owns the agent-specific layer that every agent at Netflix depends on: the runtime and execution environment, memory, identity and permissioning, discovery and registry, evaluation, and the tool/MCP interfaces agents use to act. We build the paved path agent developers across the company build on, and we operate the runtimes for the workloads the general platform can't reach — including at member scale. We're looking for a Senior Engineering Manager to lead this team. This is a small, senior, high-leverage group working at the front edge of a domain that is still being invented — where frameworks turn over in months and the durable bets are not obvious. The hardest part of this job is not the code — it is choosing what to build, and what will still matter once today's tools are gone.

Requirements

Experience leading and growing engineering teams that build and operate platform or infrastructure at scale — managing senior individual contributors and/or managers, with a track record of developing strong engineers.
Hands-on experience designing, building, and operating agentic systems in production — agent runtimes and harnesses, tool use, memory, and evaluation — with genuine technical depth, not oversight from a distance.
Breadth across business applications: you've built or led agents spanning a range of use cases, enterprise and internal applications (developer tooling, operations, knowledge work) or consumer-facing applications at scale.
A strong engineering foundation in distributed systems, cloud, and ML/AI infrastructure — deep enough to lead a senior team's hardest architectural decisions and earn its trust.
A track record of owning a platform's charter — setting its direction and holding its scope against pressure to do everything — and making build-versus-partner decisions on a multi-year cost horizon.
Cross-organizational credibility: a record of building shared accountability with senior leaders across adjacent platform, infrastructure, and security teams, and navigating ambiguous ownership boundaries.
Strong written and verbal communication; comfort with ambiguity; able to lead both 0-to-1 and 1-to-100 work; effective across a distributed (US) team.

Nice To Haves

Experience with the emerging class of agent concerns: agent identity and permissioning, guardrails and safety, and cost governance for LLM and agent workloads.
Experience running a platform that other engineers consume as a product — paved paths, migrations, deprecations, and developer experience.
Credibility in a relevant open-source ecosystem (agent frameworks, runtimes, or developer tooling).

Responsibilities

Own the charter and technical direction. Set the platform's technical strategy and roadmap — the agent-specific foundation every agent depends on, and the selective runtimes worth operating directly — and decide where to invest ahead of demand.
Lead and grow a high performing team. Hire, develop, and retain excellent engineers; set a high bar; and create the conditions for a small team to have outsized impact.
Make sharp build, adopt, or partner calls. Decide which capabilities the platform owns and which it draws from partner teams, define the interfaces between them, and keep a small team focused on the highest-leverage layer.
Turn the new hard problems into primitives. The most durable problems in this space are the ones that let agents get better from their own experience: capturing each run's full reasoning-and-tool-call trajectory as structured, replayable data; turning real-world outcomes into evaluation signal; memory that compounds across runs; and a feedback loop that turns that signal into improved behavior, promoted behind eval gates and safe rollout. Make these first-class platform capabilities with real contracts — so agents built on the platform measurably improve over time, not just scale.
Operate as part of the AI Platform leadership team. Set direction alongside peer leaders across the AI Platform and partner orgs, represent the team in senior technical forums, and build the cross-team alignment a foundational platform depends on.