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AI Crowd Simulation Intelligence Developer

Maya Patel

Full-time · Job · Los_Angeles

About the role

Simulating a crowd of 50 hand-animated characters looks expensive. Simulating a believable crowd of 50,000 requires a fundamentally different approach. We build the crowd simulation systems used in blockbuster film productions and AAA game environments — our work has appeared in 14 major releases over the past 3 years, though we can't name them. The problem we're solving now: current rule-based simulation produces crowds that look coordinated because they are — you can see the algorithm. We want crowds that look organic because individual agents make locally rational decisions that produce globally emergent behavior. That's a reinforcement learning problem, trained on motion capture data from real crowds, with a rendering feedback loop that evaluates visual authenticity. You'll design and train the agent policy models that govern individual character behavior: navigation, interpersonal spacing, emotional state, reaction to events, and group dynamics. The constraint is real-time rendering: policies need to run at 60fps for 10,000 simultaneous agents on game-grade hardware. You'll have access to our proprietary 200-hour crowd motion capture dataset and a team of technical artists who'll tell you when the simulation crosses from 'impressive' to 'convincing'.

Responsibilities

  • Design reinforcement learning policies for individual agent behavior in large crowd simulations
  • Train policies on motion capture data from real crowd scenarios
  • Optimize policy inference to run at 60fps for 10,000+ simultaneous agents
  • Implement emergent group dynamics: herding, scatter, queue formation, social distancing
  • Build visual authenticity evaluation metrics using computer vision feedback
  • Collaborate with technical artists on perceptual quality validation

Requirements

  • 6+ years in ML engineering with RL, simulation, or character animation experience
  • Deep expertise in reinforcement learning and multi-agent systems
  • Experience optimizing ML inference for real-time performance on constrained hardware
  • Familiarity with 3D graphics pipeline and character animation concepts
  • Portfolio demonstrating simulation or character behavior work

Benefits

  • Fully remote with occasional LA studio visits for mocap sessions
  • Top-of-market compensation
  • Health and dental insurance
  • $5,000 annual equipment budget
  • Credit on productions where your work appears

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Categories

AI & Machine Learning Engineers Automation & AI Workflows

Skills

python pytorch reinforcement learning computer vision docker

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Job Type

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Level

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Language

English

Salary Range

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AI Expertise

AI & Machine Learning Engineers Automation & AI Workflows

Skills

python pytorch reinforcement learning computer vision docker gcp

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