Research Scientist, HCI-Multimodality - Interaction Perception (PICO)

Beijing ByteDance Technology Co Ltd

  • San Jose, CA
  • 30+ days ago

    Highlights

    The team at Pico is dedicated to leverage technologies such as computer vision, deep learning, SLAM, 3D reconstruction, and multi-sensor fusion, we continuously expand the ways humans interact with the virtual world through handheld controllers, bare-hand tracking, eye-tracking, and XR interactive accessories, enhancing the overall interaction experience. Responsibilities: • Develop computer vision-driven VR interaction algorithms centered on intelligent input method systems.

    Numbers & Facts

    LocationSan Jose, CA

    Description

    About the Team

    The team at Pico is dedicated to leverage technologies such as computer vision, deep learning, SLAM, 3D reconstruction, and multi-sensor fusion, we continuously expand the ways humans interact with the virtual world through handheld controllers, bare-hand tracking, eye-tracking, and XR interactive accessories, enhancing the overall interaction experience.

    We focus on computer vision + NLP + LLM for next-generation VR intelligent interaction.

    Responsibilities:

    • Develop computer vision-driven VR interaction algorithms centered on intelligent input method systems. • Build LLM & NLP algorithms for prediction, error correction and completion. • Research lightweight LLM/NLP and vision-language multimodal fusion for VR. • Deliver technical innovations, patents and research translation. • Provide technical leadership for the algorithm team.

    Minimum Qualifications:

    • Master's/PhD in CS, AI or related field.
    • 5+ years NLP/LLM R&D experience, 2+ years leading core algorithms.
    • Expertise in Transformers, LLMs and sequence modeling.
    • Proficiency in PyTorch/TensorFlow.
    • Basic computer vision or multimodal background.

    Preferred Qualifications:

    • Experience in intelligent input methods, especially Chinese input methods.
    • LLM fine-tuning and optimization for interactive systems.
    • Vision-language multimodal fusion for VR.
    • Lightweight model deployment on VR/edge devices.
    • VR interaction and scenario understanding.

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