5 months ago
Remote, Americas +3 moreSenior
Base Salary
$155k - $269k/yr
Responsibilities
- Design, implement, and scale generative and predictive world-modeling systems for video, multimodal driving scenes, traffic participants, and controllable simulation.
- Develop latent diffusion, autoregressive, flow-matching, model-distillation, and LLM/VLM/VLA-based methods for scene understanding, reasoning, and control.
- Translate research prototypes into robust, large-scale distributed training and inference pipelines in collaboration with Research Scientists.
- Optimize model training and inference for efficiency, speed, and reliability on large-scale datasets.
- Build data pipelines for high-quality model-training datasets.
- Maintain the quality, stability, and maintainability of the world-model codebase and infrastructure.
- Track advances in generative AI, distributed systems, and efficient model deployment in robotics.
Requirements
- Strong Python and PyTorch or JAX skills, software-engineering fundamentals, and 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.
- Demonstrated experience building and deploying generative or predictive models of the physical world for robust, efficient, real-world applications.
- Experience working closely with research scientists and engineers in collaborative, fast-paced teams.
- Preferred experience with large-scale ML training infrastructure and tooling such as cloud platforms, Kubeflow, or Ray.
- Preferred experience with efficient model serving and deployment using ONNX or TensorRT.
- Publications or research at leading ML, computer vision, or robotics conferences such as CVPR, ECCV, or NeurIPS are a bonus.
Benefits
- Competitive compensation, equity awards, and an annual performance bonus.
- Medical, dental, and vision coverage for full-time employees.
- Unlimited vacation.
- Flexible hours and work-from-home support.
- Daily drinks, snacks, and catered meals when working in the office.
- Regular on-site, off-site, and virtual team-building activities and social events.
Categories
AI ResearchML Engineering
