Introducing Moonlake, AI for creating world simulations.

About Moonlake

Moonlake is building the frontier of AI-powered world simulation.

We create systems that generate, simulate, and reason over rich 3D environments for robotics, embodied AI, and interactive applications. Our platform enables the creation of digital worlds, synthetic environments, and scalable simulation infrastructure used to train the next generation of intelligent systems.

Our work sits at the intersection of:

Robotics

Physical AI

World Models

Simulation Infrastructure

Synthetic Data Generation

Embodied Intelligence

Moonlake has raised $28M in seed funding from NVIDIA Ventures, Threshold Ventures, AIX Ventures, and notable angels including Naval Ravikant and Jeff Dean.

Our mission is to build the foundational infrastructure that enables robots to learn, reason, and operate effectively in the physical world.

The Role

We are looking for a Member of Technical Staff – Robotics to help build the bridge between simulation, world models, and real-world robotic systems.

This role spans the full robotics stack—from evaluating foundation models and policies in simulation, to training world models, to deploying and operating physical robots. You will work closely with researchers and engineers developing next-generation simulation environments and AI systems, while ensuring those capabilities transfer successfully into real-world robotic platforms.

This is a highly hands-on role combining robotics engineering, machine learning, simulation, and hardware deployment.

What You'll Do

Evaluate Robot Foundation Models & Policies

Benchmark and evaluate robot foundation models in simulated environments

Design evaluation frameworks for robotic reasoning, planning, manipulation, and navigation

Measure generalization, robustness, and task performance across diverse scenarios

Build infrastructure for large-scale simulation-based testing and validation

Train World Models for Robotics

Develop and train world models that enable robots to understand and predict environment dynamics

Build systems that learn from multimodal robot data including vision, depth, state, and actions

Improve environment understanding, forecasting, and decision-making capabilities

Work closely with simulation and AI teams to advance robotic world modeling systems

Build Real-World Robot Learning Pipelines

Collect and curate real-world robotics datasets

Train and fine-tune models using both simulated and physical robot data

Improve sim-to-real transfer for robotic policies and world models

Develop workflows connecting simulation, training infrastructure, and deployed robotic systems

Deploy and Operate Physical Robots

Set up, integrate, and maintain robotic hardware platforms

Bring learned policies and world models onto real robotic systems

Debug hardware, software, sensing, and control issues

Develop deployment pipelines for testing, validation, and continuous improvement

Work directly with robotic manipulators, mobile robots, sensors, and compute systems

Areas of Focus

Robot Foundation Models

Policy evaluation

Model benchmarking

Simulation-based testing

Generalization analysis

Performance measurement

World Models

Environment modeling

Predictive systems

Representation learning

Multimodal learning

Model-based reasoning

Simulation

Robotics simulators

Digital twins

Synthetic environments

Sim-to-real transfer

Evaluation infrastructure

Robotics Systems

Robot setup and integration

Sensors and perception systems

Robot control

Hardware debugging

Deployment workflows

What We're Looking For

Strong background in robotics, embodied AI, machine learning, or related fields

Experience working with physical robotic systems

Experience with robotic simulation platforms such as Isaac Sim, MuJoCo, Habitat, Gazebo, or similar

Familiarity with robot learning, foundation models, or world models

Strong software engineering skills in Python and robotics tooling

Experience deploying software onto real robotic hardware

Ability to debug across hardware, software, and machine learning systems

Comfort working in a fast-moving research and engineering environment