Research Engineer, World Models

Waabi Waabi · Robotics · Toronto, ON +3 · Remote · Autonomy & Algorithms

Research Engineer focused on developing and productionizing large-scale world models for autonomous transportation, including video, multimodal, LLM/VLM/VLA, and predictive models. The role involves designing, implementing, and scaling generative and predictive systems, optimizing training and inference, building data pipelines, and ensuring code quality, with a focus on robotics applications.

What you'd actually do

  1. Design, implement, and scale state-of-the-art generative and predictive world-modeling systems:
  2. Collaborate closely with Research Scientists to translate cutting-edge model prototypes into robust, large-scale, distributed training and inference pipelines.
  3. Optimize model training and inference for efficiency, speed, and reliability on large-scale datasets.
  4. Build large scale data pipelines to build high quality datasets for training
  5. Ensure the quality, stability, and maintainability of the world model codebase and infrastructure.

Skills

Required

  • Python & PyTorch (or JAX) skills
  • strong software-engineering fundamentals
  • extensive experience with distributed training and large-scale model deployment
  • Master's degree in Computer Vision, Machine Learning, Robotics, or a related field, or equivalent industry experience in model development and scaling
  • built and deployed generative or predictive models of the physical world, focusing on scale, efficiency, and robustness for real-world applications

Nice to have

  • 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)

What the JD emphasized

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

Other signals

  • develop algorithms and productionize the next generation of World Models
  • large-scale world models for temporal reasoning and generation
  • video models, multimodal generative models, LLM/VLM/VLA models, and predictive models
  • Model distillation
  • large-scale, distributed training and inference pipelines
  • large scale data pipelines
  • generative AI, distributed systems, and efficient model deployment in robotics