Member of Technical Staff, Agentic Systems - Games
$890,000–$1,690,000 year
RemoteUnited States or Los Angeles, California, United States
Job Summary
Lead strategy and delivery of GenAI tools and agentic systems across Netflix Games by defining product roadmaps, prototyping fast, and shipping features from concept to production. Identify key opportunities where AI adds value, derive insights from user research and quantitative data, and work across diverse game studios and platforms to inform existing initiatives. Contribute to engineering production-grade agentic systems including multi-step reasoning pipelines, tool-use agents, and multi-agent orchestration while designing code harnesses connecting frontier models to game engines and APIs. Build reusable agent primitives, MCPs, and shared libraries to reduce duplicated effort, and define AI evaluation as a first-class discipline with offline eval sets, automated scoring pipelines, and online experimentation. Manage a small team of engineers and partner with external researchers to accelerate efforts in emerging frameworks and infrastructure patterns.
Required Qualifications
- 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
Desired Qualifications
- 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
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