NAVER AI LAB
NAVER AI LAB aims to achieve results that will surprise the world through impactful research, thus contributing to the global AI research community, as the mid&long-term AI research team of NAVER.
Introducing NAVER AI LAB l CLOVA Click!
Meet Research Scientists at NAVER AI LAB l CLOVA Click!
[NAVER AI LAB Job Description]
  • Position: Research scientists (OO) & Research interns (OO)
  • Research topics
    • Including, but not limited to -
    • Next generation of backbones for image, video, and audio recognition.
      • Multimodal hyperscale representation
      • Novel neural architecture design (e.g., NAS).
      • Object recognition (e.g., classification, detection, segmentation, retrieval, etc.).
      • Lightweight and energy-efficient models (e.g., pruning, quantization, compression).
      • Learning with large-scale insufficient annotations (e.g., weakly- / self- / semi-supervised learning).
      • Novel learning algorithms for NNs (e.g., normalization, optimization, etc.).
    • Generative models for image, video, text, and audio
      • Uncoditional & conditional image generation
      • Image-to-image and vid-to-vid translation
      • Disentanglement and controllability
      • Cross-modal generation
      • Audio and music generation
      • Effective learning algorithm for generative models
      • Style transfer and super-resolution for images and videos
      • Neural render (NeRF) and super-resolution
    • Hyperscale language models and their extensions
      • Controllable LM, Hallucination
      • Prompt optimization
      • Multi-modal and Multi-lingual extension.
      • New evaluation metrics
      • Extension to dialogs, QA, summarization, content generation, etc.
    • Human computer interaction and interactive AI.
      • Accessibility
      • Computational Interaction
      • Computational Social Science and Social Computing
      • Data-driven Interface Design
      • Human Computation
      • Visualization
    • Representation learning for semi-structed or structured data.
      • Graph representation learning
      • Time-series prediction and representation learning
    • Trustworthy AI.
      • Explainable AI and causal inference.
      • Robust machine learning (adversarial robustness, domain generalization).
      • De-biased and fair machine learning.
      • Proper uncertainty estimation (e.g. prediction calibration, probabilistic machine learning).
      • Privacy-preserving AI (e.g. differential privacy, federated learning, etc.).
    • Audio recognition.
      • Big representation learning for automatic speech recognition (ASR).
      • Audio-visual speech recognition.
    • Healthcare AI
      • EMR/EHR based foundation models (large-scale pre-trained language models) for healthcare
      • Clinical predictive modeling with EMR/EHR (e.g., disease prediction, ICD code mapping)
      • Clinical decision support system
      • Medical image analysis for otorhinolaryngology & dentistry
      • Interpretability of AI models (XAI)
      • Causal inference in machine learning & intervention modeling for healthcare services
    • Other topics.
      • AI for social good.
      • Reinforcement learning in the wild.
  • AI Research with External Collaboration
    • SNU-NAVER Hyperscale AI Center
      • Professors: Byung-Gon Chun, Gunhee Kim, Seungwon Hwang, Hyunoh Song, Byoung-Tak Zhang, Taesup Moon, Sang-Goo Lee, Kyomin Jung, Kyoung Mu Lee
      • Main topics (not limited)
        • Advanced hyperscale language models (multimodal, multi-lingual)
        • Reliable and efficient distributed training
        • Overcoming limitations of current hyperscale LMs (hallucination, prompt optimization, bias)
        • Advanced large-scale self-supervised learning
      • Some members will contribute as an adjunct professor of SNU.
    • KAIST-NAVER Hypercreative AI Center
      • Professors: Jaegul Choo, Jinwoo Shin, Sung Ju Hwang, Eunho Yang, Jae-Sik Choi, Juho Lee, Kee-Eung Kim, Alice Oh, Juho Kim, Edward Choi, Minjoon Seo
      • Main topics (not limited)
        • Multi- and cross-modal content generation
        • Generation controllability and quality measurement
        • Representation learning for content generation
      • Some members will contribute as an adjunct professor of KAIST.
    • Academic Advisors
  • Requirements
    • Research scientist
      • Strong track record of publications at top-tier conferences in machine learning, computer vision, natural language processing, audio, hci, and speech.
      • Relevant job experiences, e.g., laboratory researcher experiences or full-time industrial research experiences.
      • Preferred
        • PhD in CS, EE, mathematics or other related technical fields, or an equivalent work experience.
        • Strong programming skills in Python (PyTorch).
        • Experience with serving as an active member in the research community (e.g. reviewing activities, tutorial and workshop organization, and research code contributions).
      • Responsibilities
        • Organize and execute one’s own research agenda.
        • Lead and collaborate on ambitious research projects.
    • Research intern
      • Experience on research collaborations and paper writing in related fields.
      • Proficient programming skills in Python (PyTorch).
      • Preferred
        • Currently in an MS or PhD programme in CS, EE, mathematics or other related technical fields.
        • Proficient track record of publications at top-tier conferences in machine learning, computer vision, natural language processing, audio, hci, and speech.
  • Hiring process:
    • Research scientist:
      • Algorithm coding test (more than one) > Tech talk > Job interviews (more than two).
    • Research intern:
      • Algorithm coding test > Paper implementation or tech talk > Job interview
Copy link