Member of Technical Staff, Agentic Systems - Games

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 a real impact.

• Product strategy and opportunity identification: 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, prioritizing the highest return investments and distinguishing hype from value. Work across diverse teams in game studios and platforms to inform and advise on existing initiatives and how new tools will fit within the broader ecosystem.
• Prototyping to shipping: Bring product strategy skills and builder capabilities to every initiative, defining what is worth building. 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, Open Claw, custom agents, and bots).
• Engineering production‑grade agentic systems: Contribute to systems that implement multi‑step reasoning pipelines, tool‑use agents, multi‑agent orchestration, and autonomous workflows. Design and build 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—to raise the floor for the whole organization and reduce duplicated effort across game studios and platform teams.
  
  Iterate on model capabilities through fine‑tuning, DPO to align outputs with quality preferences, LoRA/QLoRA for efficiency, and RLHF for long‑horizon agentic tasks.
• Data‑driven decision making: Define AI evaluation as a first‑class discipline, including overall product evaluation strategy, curating offline evaluation sets, automated scoring pipelines, and regression gates. Build online evaluation—A/B testing, production telemetry, user feedback loops, and anomaly detection for agent behavior drift.
• External collaboration and continuous learning: 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.
• System scalability and compliance: Collaborate with engineering and platform teams to build robust solutions and scale core capabilities—including model inference, data pipelines, responsible AI compliance, safety guardrails, and graceful degradation at scale.
• Team leadership: Manage a small team of engineers.

• 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, and running production development work streams.
• 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 (Lang Chain, Lang Graph, Auto Gen, Google ADK, or equivalent).
• Proven experience designing and operating evaluation infrastructure for AI systems—offline benchmarks, automated scoring pipelines, and online…