- Develop models for 3D spatial perception, geometric reasoning, and dynamic scene understanding in real-world environments.
- Design and prototype multimodal learning systems that integrate vision, language, video, and temporal signals for spatial reasoning.
- Build robust scene understanding systems capable of handling long-tail, ambiguous, and edge-case scenarios using large-scale data, simulation, or generative approaches.
- Develop world-modeling and predictive-reasoning methods, including learning-based dynamics models, video prediction, and imagination-driven planning.
- Investigate multimodal representation learning approaches that align spatial, visual, linguistic, and temporal information.
- Train, fine-tune, evaluate, and optimize multimodal models such as VLMs, MLLMs, video-language models, or related architectures.
- Conduct benchmarking, error analysis, and experimental evaluations to improve robustness, generalization, and real-world performance.
- Collaborate with research teams to develop prototypes, publications, patents, and technical innovations.
Minimum Qualifications
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- Master's degree or Ph.D. in Computer Science, Electrical Engineering, Robotics, Machine Learning, or a related field.
- Strong experience designing and developing multimodal models, including VLMs, MLLMs, video-language models, or related architectures.
- Solid foundation in 3D spatial perception and geometric reasoning, with the ability to model spatial relationships in dynamic environments.
- Experience building robust scene understanding systems for real-world or simulated environments.
- Familiarity with world models or predictive reasoning methods, such as learning-based dynamics models, video prediction, or planning-oriented representations.
- Experience with multimodal representation learning that integrates vision, language, and temporal signals.
- Proficiency in Python and modern deep learning frameworks, with the ability to rapidly prototype research ideas and systems.
- Strong communication, presentation, and collaboration skills.
Bonus Qualifications
- Experience with vision-text embedding alignment, vision and language encoders, adapters, MLLM training stages, or multimodal fine-tuning pipelines.
- Knowledge of generative models such as Variational Autoencoders, Diffusion Models, or Generative Adversarial Networks.
- Experience with action understanding tasks such as action segmentation, temporal alignment, action anticipation, or activity recognition.
- Publications or research contributions in leading AI, machine learning, computer vision, or robotics venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, AAAI, RSS, CoRL, or ICRA
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| Desired Start Date |
9/14/2026 |
| Position Keywords |
Multimodal learning, world models, spatial perception, geometric reasoning, scene understanding,
video-language models, vision-language models, temporal reasoning, embodied AI
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