Machine Learning

Job description of AI and ML research of NAVER AI Lab

Machine Learning (@NAVER AI Lab & CLOVA)

  • Main R&R: Publication at top AI venues and contribution to NAVER and AI communities through impactful research

  • Position

    • Full-time regular research scientist: OO

    • Research intern (remote work available): OO

  • Research topics (Including, but not limited to)

    • Next generation of backbones for image, video, and audio recognition.

      • 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 superresolution for images and videos

      • Neural render and superresolution

    • Hyper-scale language models and their extensions.

      • Controllable LM.

      • Multimodality extension.

      • New evaluation metrics and protocols

      • 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.

      • Automatic speech recognition (ASR).

      • Audio-visual speech recognition.

    • Other topics.

      • Healthcare AI.

      • AI for social good.

  • 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.

  • Application process:

    • Research scientist:

      • Algorithm coding test (more than one).

      • Tech talk.

      • Job interviews (more than two).

    • Research intern:

      • Algorithm coding test (more than two).

      • Job interviews (more than one).