Member of Technical Staff, Agentic Systems - Games

Netflix•Los Angeles, CA

3d•$890,000 - $1,690,000

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. The next challenge for Netflix Games will be applying AI at the scale and quality consumers expect to make real impact. Come be a part of what’s next. We're seeking a technical leader to help shape the strategy, development & delivery of GenAI tools and agentic systems across Netflix Games. This is a pivotal role that will drive how AI creates real benefit and support across the organization, needing a leader who can work on product strategy, internal alignment, and bring state-of-the-art engineering hands-on execution to building agentic systems and machine learning capabilities.

Requirements

We're looking for a technical leader who can help shape the strategic direction for agentic systems across Netflix Games , and a hands-on builder of those systems.
7+ years of experience in AI product strategy, machine learning, and AI engineering with a strong hands-on engineering foundation.
3+ years of experience in the game development industry.
Product management experience, identifying user needs, defining product roadmaps, running production development workstreams.
Experience in games or interactive entertainment, shipping game features, working in game engines (Unreal, Unity), or building AI experiences for players.
Deep, practical experience building and deploying agentic AI systems in production, multi-step reasoning, tool use, multi-agent orchestration, or autonomous workflow automation.
Strong Python engineering skills and production experience with agentic frameworks (LangChain, LangGraph, AutoGen, Google ADK, or equivalent).
Proven experience designing and operating evaluation infrastructure for AI systems , offline benchmarks, automated scoring pipelines, and online experimentation.
Deep understanding of the Gen AI ecosystem , open models, data requirements, infrastructure and tooling, safety frameworks, and the trade-offs between hosted and in-house solutions.
Comfortable navigating from research prototypes to production-ready systems in a fast-paced, cross-functional setting, in partnership with engineering teams, AI research, and game studios.
Strong communicator who can convey complex AI system design to technical and non-technical stakeholders, and build alignment across game studios, platform teams, and leadership.

Nice To Haves

Familiarity with Model Context Protocol (MCP) or similar agent-to-system connectivity standards.
Experience with inference optimization for agentic deployments: latency reduction, cost management, streaming responses.
Background in responsible AI: safety evaluation, prompt injection defense, output moderation, and content policy compliance.
Experience with the full LLM fine-tuning lifecycle: SFT, DPO, LoRA/QLoRA, and RLHF for long-horizon tasks. Comfortable taking a fine-tuning project from dataset curation through deployment and ongoing maintenance.
Published research or public contributions in agentic systems, RLHF, or LLM evaluation.

Responsibilities

Identify key opportunities where AI can add value to teams, defining the product strategy and architecture of new enablement systems.
Derive insights from user research and quantitative data to create product roadmaps, which identify the highest return investments, distinguish hype from value.
Work across diverse teams in game studios and platforms, to inform and advise on existing initiatives, as well as how new tools will fit within the broader ecosystem.
Bring product strategy skills and builder capabilities to every initiative, define what's worth building, not just how to build it. Prototype fast to validate ideas with real systems, identify where agentic AI genuinely transforms player experience or team productivity, and take features from concept to production without a handoff layer.
Work with technologies across the game organization stack, from game engines, to services, to Agentic systems (Claude Code, Cowork, OpenClaw, custom agents and bots)
Contribute to engineering production-grade agentic systems: multi-step reasoning pipelines, tool-use agents, multi-agent orchestration, and autonomous workflows. Design and build the code harnesses and scaffolding connecting frontier models (or open-weight alternatives) to tooling, game engines, and platform APIs.
Build reusable agent primitives and infrastructure , MCPs (Model Context Protocols) and shared agentic libraries , that raise the floor for the whole organization and reduce duplicated effort across game studios and platform teams.
Where needed, iterate on model capabilities through model fine-tuning, DPO to align outputs with quality preferences, LoRA/QLoRA for efficiency, and RLHF for long-horizon agentic tasks. With the aim of improving overall tools performance.
Define AI evaluation as a first-class discipline, including defining overall product evaluations strategy, curating offline eval sets, automated scoring pipelines, and regression gates. Define and build online evaluation: A/B testing, production telemetry, user feedback loops, and anomaly detection for agent behavior drift.
Partner with external researchers, developers, and companies pioneering work in the agentic AI space. Stay on the forefront of emerging frameworks, open models, and infrastructure patterns, and bring those learnings back to accelerate our own efforts.
Collaborate with our engineering and platform teams to build robust solutions and scale core capabilities: model inference, data pipelines, responsible AI compliance, safety guardrails, and graceful degradation at scale.
Managing a small team of engineers