Machine Learning Engineer – World Modeling

BSc or above in Machine Learning, Computer Science, Robotics, or a related field, or equivalent industry experience.

Hands-on experience with state-of-the-art video generative models and world models (e.g., Cosmos-3, LTX 2.3, Self-Forcing, Lingbot-World, or comparable systems).

Deep expertise in at least one of the following areas:

Full-stack data pipelines — large-scale video data pipelines and/or simulation data collection; annotation and filtering workflows for video / world model training.

Model training & infrastructure — training large-scale diffusion transformers on large GPU clusters.

Rendering engines & simulation — Unreal Engine and Blueprint-based gym environments, game-engine integration, building interactive simulated environments.

World action models & robotics — world action models / video action models, action-conditioned video generation, world-model applications in robotics.

Strong engineering expertise in deep learning frameworks such as PyTorch, with the ability to debug failures across the training/inference stack (memory issues, deadlocks, I/O bottlenecks).

Highly proficient with modern AI coding agents and web-based coding tools (e.g., Claude Code, Codex, Cursor), and skilled at leveraging them to dramatically accelerate engineering workflows.