Member of Technical Staff - Embodied Agents

Introducing Moonlake, AI for creating world simulations.

Moonlake is building the frontier of interactive world models: systems that generate, simulate, and reason over 3D environments for robotics, embodied AI, simulation, gaming, and multimodal agents. We are developing AI-native tooling for creating dynamic worlds where geometry, physics, visuals, audio, and interaction remain coherent in real time.

Our team sits at the intersection of:

• Embodied AI

• Robotics simulation

• Interactive 3D worlds

• World models

• Real-time generation

• AI infrastructure

Moonlake is building the next generation of AI infrastructure for interactive digital worlds. Our mission is to enable anyone to create, simulate, and interact with rich environments using natural language and multimodal inputs, turning simple ideas into worlds with structure, logic, and agents that can perceive and act.

Our team has raised $28M in seed funding from NVIDIA Ventures, Threshold Ventures, AIX ventures and notable angels including Naval Ravikant and Jeff Dean to build the foundational layer for the future of AI - powering everything from creative tools and games to robotics training, simulations, and digital twins. Our goal is to make building and experimenting with these environments as accessible and scalable as publishing video on the internet.

We are looking for exceptional research engineers and applied researchers to help push the frontier of interactive AI.

We’re looking for a Member of Technical Staff — Embodied Agents to help build general-purpose agents capable of perceiving, reasoning, and acting inside interactive simulated worlds.

This role focuses on designing agents that can:

• Understand multimodal environments

• Maintain memory and long-horizon reasoning

• Plan and execute actions

• Operate across robotics, simulation, and interactive 3D tasks

You’ll work closely with teams building:

• World models

• Diffusion systems

• Interactive environments

• Simulation infrastructure

• Multimodal generation systems

This role sits at the core of Moonlake’s vision for interactive AI systems and embodied intelligence.

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  Design and train embodied AI agents operating inside simulated and interactive environments

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  Build systems that combine:

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    Vision

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    Depth

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    Language

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    Memory

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    Planning

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    Control

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  Develop agent architectures capable of long-horizon reasoning and interaction

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  Train policies for continuous and discrete action spaces

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  Improve robustness, generalization, and environment interaction capabilities

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  Work on simulation-to-agent training pipelines

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  Collaborate closely with world-modeling, infrastructure, and product teams

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  Push toward more general, adaptive embodied systems

Embodied Agent Architectures

• Multimodal agent systems

• Vision-language-action models

• World-aware policy architectures

• Hierarchical planning systems

• Long-horizon task execution

• Memory-augmented agents

Perception & Multi-Modal Understanding

• Visual perception

• Depth understanding

• Spatial reasoning

• Language grounding

• Multi-modal fusion systems

• Environment understanding

Reasoning & Planning

• Memory systems

• Long-context reasoning

• Task decomposition

• Decision-making under uncertainty

• Goal-conditioned behavior

• Planning and execution loops

Action & Control

• Continuous control systems

• Discrete action systems

• Policy optimization

• RL and imitation learning

• Agent-environment interaction systems

• Interactive simulation pipelines

Simulation & Training Infrastructure

• RL environments

• Robotics simulation

• Interactive 3D worlds

• Synthetic data pipelines

• Agent evaluation frameworks

• Scalable training systems

• Strong background in embodied AI, RL, robotics, or agent systems

• Experience training agents in simulated environments

• Strong ML and systems fundamentals

• Deep curiosity around general intelligence and interactive learning systems

• Ability to move quickly between research and implementation

• Comfort working across multimodal systems and interactive environments

• Strong coding and experimentation ability

Moonlake’s long-term vision requires agents that can:

• Understand environments

• Learn from interaction

• Adapt to new tasks

• Operate inside dynamic worlds

The embodied agents stack is central to making Moonlake’s world models useful, interactive, and autonomous.

You’ll help define how intelligent systems perceive, reason, and act inside the next generation of simulated worlds..

We are committed to being an on-site, in-person team currently based in San Mateo