Waabi’s autonomous-driving stack is powered by Waabi World, which delivers realistic, scalable, controllable, and efficient simulation. As a Research Engineer in the World Models team, you will develop algorithms and productionize the next generation of World Models that can reason about complex, dynamic 4D environments.

This role focuses on large-scale world models for temporal reasoning and generation, including video models, multimodal generative models, LLM/VLM/VLA models, and predictive models of traffic participants and scenes. Your work will directly power Waabi World’s ability to model future evolution, synthesize realistic safety-critical scenarios, and provide rich generative priors for downstream planning, testing, and training.

You will…

- Design, implement, and scale state-of-the-art generative and predictive world-modeling systems:

- Collaborate closely with Research Scientists to translate cutting-edge model prototypes into robust, large-scale, distributed training and inference pipelines.

- Optimize model training and inference for efficiency, speed, and reliability on large-scale datasets.

- Build large scale data pipelines to build high quality datasets for training

- Ensure the quality, stability, and maintainability of the world model codebase and infrastructure.

- Stay on top of emerging advances in generative AI, distributed systems, and efficient model deployment in robotics.

Qualifications:

- Strong software engineering and implementation: You have very strong Python & PyTorch (or JAX) skills; strong software-engineering fundamentals, and extensive experience with distributed training and large-scale model deployment.

- Demonstrated technical impact: You have a Master's degree in Computer Vision, Machine Learning, Robotics, or a related field, or equivalent industry experience in model development and scaling.

- Expert domain knowledge: You have built and deployed generative or predictive models of the physical world, focusing on scale, efficiency, and robustness for real-world applications.

- Team player: You have worked in a close-knit team of researchers and engineers and have strong communication to deliver successful projects in a fast-paced environment. 

Bonus:

- Experience with infrastructure and tooling for large-scale ML training (e.g., cloud platforms, Kubeflow, Ray).

- Experience with efficient model serving and deployment (e.g., ONNX, TensorRT).

- Publications or research at top ML/CV/Robotics conference (e.g., CVPR, ECCV, NeurIPS)