What we do
A platform for data labeling that enables jobs in townships. Machine learning often relies on data labeled by humans. We ensure high-quality labeling of your data while creating jobs that really make a difference to the local community.
Enlabeler is a data annotation service provider with a mission to create jobs in the townships. Due to our flexible and scalable set-up, we are able to quickly respond to a client’s needs and deliver fast and reliable feedback on your dataset. We work for clients that need help with the organization, classification, clean-up and labeling of their datasets (images, speech, text, video). This data will be used to train machine learning and Artificial Intelligence models.
Why Work For Us
At Enlabeler our primary mission is to create data labeling jobs for (unemployed) youth in the township communities in South-Africa. We strongly believe in creating an inclusive and tech-driven environment, in which young people can get first (paid) work experience, develop themselves and get ready to grow into the data science/tech industry in South-Africa. We believe we can create an African data labeling community through a scalable, flexible mobile platform and label data within and for Africa We partner with local youth empowerment initiatives (such as Naspers Labs) to train and develop young people (18-30) in the township communities and bringing in new financial wealth. Together with our clients, we aim to fight youth unemployment. We work for international and domestic clients and serve commercial goals - by making a difference together!
Our Hiring Process
This is an outline as steps can be added and removed as it's dependant on each candidate's skillset.
- Step one is a call with the acting CTO to chat through technical ability and get a feel for personality.
- Step two is an interview with the co-founder.
- Step three is an assessment using a real client.
- Step four, after the team has internally made a decision there will be a final interview with Esther.
Our Engineering Processes