Currently pursuing a degree in computer science, robotics, electrical engineering, or a related field, or have equivalent hands-on research experience.
Have strong Python programming skills and experience implementing and testing machine learning or computer vision methods.
Have a foundation in 3D geometry, computer vision, or robot learning, with the ability to turn research ideas into working prototypes.
Be comfortable working with imperfect real-world data, debugging pipelines, and designing clear, controlled experiments.
Have clear technical communication skills and the ability to take ownership of a scoped research project in an early-stage team.
Responsibilities
Develop real-to-sim workflows that turn real-world images, video, and sensor data into simulation-ready scenes, objects, and environments.
Build tools to process, align, and validate robotics data, including visual observations, 3D geometry, and robot trajectories.
Investigate how reconstruction fidelity, scene coverage, and physical properties affect the usefulness of reconstructed environments for robot learning.
Design experiments and benchmarks to compare reconstructed environments with real-world reference data and measure their impact on robot learning and task performance.
Work with engineers to turn research prototypes into reproducible data pipelines, datasets, and evaluation tools.
Document methods, experiments, and findings, and contribute to technical reports or research publications where appropriate.
Desired Qualifications
Have research or project experience in 3D reconstruction, neural rendering, or scene understanding.
Have experience with robotics simulators or tools for scene and asset generation.
Have hands-on experience training or evaluating robot policies in simulation or on physical systems.
Have experience building reproducible experiments, processing multimodal datasets, or contributing to open-source projects.
About the Role
At WorldEngine, we’re building the data infrastructure for Physical AGI. Real-world experience is central to that effort: the scenes robots operate in, the objects they interact with, and the data that captures what happens.
As a Research Intern, you’ll investigate how real-world observations and robot interactions can be converted into high-quality datasets and simulation-ready environments. You’ll work closely with our engineering and research teams on 3D reconstruction, real-to-sim workflows, and experiments that measure how data and simulation quality affect robot learning and evaluation.
What You'll Do
Develop real-to-sim workflows that turn real-world images, video, and sensor data into simulation-ready scenes, objects, and environments
Build tools to process, align, and validate robotics data, including visual observations, 3D geometry, and robot trajectories
Investigate how reconstruction fidelity, scene coverage, and physical properties affect the usefulness of reconstructed environments for robot learning
Design experiments and benchmarks to compare reconstructed environments with real-world reference data and measure their impact on robot learning and task performance
Work with engineers to turn research prototypes into reproducible data pipelines, datasets, and evaluation tools
Document methods, experiments, and findings, and contribute to technical reports or research publications where appropriate
What We're Looking For
Currently pursuing a degree in computer science, robotics, electrical engineering, or a related field, or equivalent hands-on research experience
Strong Python programming skills and experience implementing and testing machine learning or computer vision methods
A foundation in 3D geometry, computer vision, or robot learning, with the ability to turn research ideas into working prototypes
Comfortable working with imperfect real-world data, debugging pipelines, and designing clear, controlled experiments
Clear technical communication and the ability to take ownership of a scoped research project in an early-stage team
Nice to Have
Research or project experience in 3D reconstruction, neural rendering, or scene understanding
Experience with robotics simulators or tools for scene and asset generation
Hands-on experience training or evaluating robot policies in simulation or on physical systems
Experience building reproducible experiments, processing multimodal datasets, or contributing to open-source projects