обновлено 15 дней назад
Computer Vision Intern (AI)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Computer Vision Intern (AI): Developing and evaluating computer vision models for visual recognition and scene understanding in virtual environments with an accent on object detection, tracking, segmentation, and deep learning optimization. Focus on preparing datasets, benchmarking and fine-tuning models, and analyzing experiments to improve accuracy.
Location: Singapore Razer AI Center
Company
Razer develops gaming products and intelligent AI systems through a global, gamer-centric organization.
What you will do
- Develop and evaluate visual recognition and scene understanding models for image- and video-based tasks.
- Work with object detection, tracking, segmentation, CNNs, Transformers, detection models, and VLMs.
- Prepare, augment, annotate, and manage datasets for model training.
- Benchmark and fine-tune deep learning models to improve performance and accuracy.
- Analyze experimental results with mentors and document workflows, findings, and best practices.
Requirements
- Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- Proficiency in Python and experience with OpenCV, NumPy, and PyTorch or TensorFlow.
- Knowledge of machine learning fundamentals and image processing concepts.
- Detail-oriented, analytical, and motivated to learn in a research-driven engineering environment.
- Available for a minimum 16–20 week internship in Singapore.
Culture & Benefits
- Opportunity to contribute to products and intelligent systems used in the global gaming ecosystem.
- Work with a global team spanning five continents.
- Inclusive and respectful workplace committed to equal opportunity and reasonable accommodations.
- Hands-on learning in computer vision model development, AI experimentation, dataset management, and model evaluation.
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