Salary
💰 $192,600 - $288,800 per year
Tech Stack
GoJavaPythonPyTorchTensorflowUnity
About the role
- Design, implement, and scale AI agents that assist in generating game code and executing goal-directed behaviors.
- Define orchestration logic for single- and multi-agent systems.
- Develop reusable agentic frameworks - planning modules, memory systems, or policy adaptation layers - for creator extensibility.
- Lead high-impact initiatives, including hierarchical reinforcement learning for scalable behaviors, AI planning and goal inference frameworks for NPCs and simulations, and agent memory and world modeling systems for persistent, believable behavior.
- Collaborate across internal and external teams to unlock innovative use cases in gaming, simulation, and interactive storytelling.
- Contribute to the long-term vision and roadmap for Unity’s agentic AI offerings, ensuring modularity, usability, and performance across platforms.
- Establish and champion best practices for model quality, simulation-based evaluation, and ML/AI safety within real-time systems.
Requirements
- Advanced degree (MS or Ph.D.) in Computer Science, Machine Learning, or a related field.
- 5+ years of hands-on experience developing ML systems in production, ideally within real-time or interactive environments.
- Experience with multi-agent systems, orchestration, or AI tool integration.
- Strong background in one or more of the following: LLM, reinforcement learning, decision-making under uncertainty, AI planning, or multi-agent systems.
- Proficiency in at least one general-purpose programming language (e.g., Python, C++, Go, Java) and familiarity with ML frameworks (e.g., PyTorch, TensorFlow).
- Strong system design skills and the ability to architect performant, scalable ML systems.
- Excellent communication and collaboration skills; ability to work effectively with cross-functional teams including engine developers, designers, and product managers.
- Familiarity with game development workflows (preferred).
- Publications or open-source contributions in reinforcement learning, agent-based modeling, or related areas (preferred).
- Experience with behavior trees, utility AI, or hierarchical task planning (preferred).
- Sufficient knowledge of English to have professional verbal and written exchanges.