Machine Learning Annotator Internship

NRI Indonesia was established in October 2015. NRI Indonesia supports the advancement of clients' Digital Transformation (DX) with our comprehensive services of IT consulting and software development, backed by the knowledge, proven track record and expertise of our parent company, Nomura Research Institute (NRI) Japan. We realize truly meaningful solutions for our clients, and support their growth by seamlessly combining our consulting and tech capabilities. At NRI Indonesia, we strive to solve social issues and contribute to a sustainable future.**Our activities cover the following**:- Planning, designing and implementing enterprise system- Designing/implementing loyalty program- Big data analytics- Managing and operating systems particularly in the area of retail and distribution, manufacturing, finance and service industriesAccording to NRI's basic philosophy, NRI Indonesia aims co-prosperity with our clients in the long term relationship, and wishes to contribute to economic growth with improvement of human life in Indonesia.**Key Responsibilities**:Annotate various machine learning datasets, in particular image and video datasets.Ensure high-quality annotations to assist in the accurate training of machine learning models.Collaborate with the AI team to understand specific annotation requirements and standards.Participate in training sessions to become proficient in ML annotator tools such as CVAT and Label Studio.Provide feedback and suggestions to improve the annotation process and tools.Strong attention to detail and a commitment to delivering high-quality work.Excellent observational skills and the ability to identify and label various elements in images and videos accurately.Ability to learn new tools and technologies quickly.Good communication skills and the ability to work effectively in a team.Recently graduated with a degree in Computer Science, Data Science, Engineering, or a related field is advantageous.Experience or strong interest in Machine Learning/AI is preferred.