Senior ML Engineer, Genai - Games

Netflix Netflix · Big Tech · United States · Remote · Data & Insights

Senior ML Engineer at Netflix Games focusing on integrating GenAI into game development, including gameplay mechanics, asset generation, NPC behaviors, and internal tools. The role involves optimizing model performance, bridging research and production, and mentoring junior engineers. Requires strong software engineering skills in C++/C# and experience with the ML lifecycle.

What you'd actually do

  1. Lead the technical integration of GenAI tools and techniques into existing game engines (Unity/Unreal/Proprietary) and development workflows.
  2. Work directly with designers to build functional, AI-native gameplay mechanics—such as dynamic narrative systems, procedural asset generation, or intelligent NPC behaviors.
  3. Own the performance side of GenAI. You will optimize model latency and memory footprints to ensure AI features don't compromise game frame rates.
  4. Develop and maintain "AI superpower" tools for internal teams, such as automated texture generation, level design assistants, or code-completion agents.
  5. Translate complex AI research papers and open-source models into stable, production-ready code that aligns with our player experience goals.

Skills

Required

  • 5+ years of experience in the games industry
  • 3+ years of hands-on experience with Machine Learning/GenAI tools/techniques in a product-driven environment
  • Strong proficiency in languages like C++ and C#
  • Experience with model fine-tuning
  • Experience managing the end-to-end ML lifecycle (data prep to deployment)

Nice to have

  • solid grasp of game loops
  • memory management
  • what it takes to get a feature through a "Live Ops" cycle
  • thrive in the "grey area" of emerging tech
  • pivot quickly when a specific model or approach doesn't meet the needs of the game design

What the JD emphasized

  • implementing GenAI models into our production pipelines
  • building the infrastructure and logic that brings those ideas to players
  • gameplay mechanics
  • optimize model latency and memory footprints
  • AI superpower tools
  • production-ready code

Other signals

  • implementing GenAI models into production pipelines
  • building the infrastructure and logic that brings those ideas to players
  • work directly with designers to build functional, AI-native gameplay mechanics
  • optimize model latency and memory footprints
  • Develop and maintain 'AI superpower' tools for internal teams
  • Translate complex AI research papers and open-source models into stable, production-ready code