Research Engineer, World Models
Job Summary
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 Worlds 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:
Video generation and prediction
Latent diffusion / autoregressive / flow-matching models
multimodal foundation models for driving scenes
LLM / VLM / VLA methods for scene understanding reasoning and control
Generative scenario modeling and controllable simulation
Model distillation
- 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 Masters 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)
Required Experience:
IC