Contract: Independent Contractor Schedule: Flexible hours and availability for meeting/training, 40 hours per week Client Timezone: Central Time (US & Canada)
Client Overview Our client is a growing food waste management startup focused on reducing waste at university dining halls across the United States. Leveraging proprietary AI and machine learning technology, the company provides actionable insights to dining halls to optimize their supply chain and inventory based on consumer preferences. You'll be joining a passionate team that aims to reduce the environmental impact of food waste at scale.
Job Description We are seeking a detail-oriented individual to analyze and label images captured in university dining halls across the country. You will be responsible for identifying food items in images and matching them to our existing database, enabling our AI models to learn and become more accurate at automatic recognition. Attention to detail is paramount as the quality of your labeled data will directly impact the quality of our models. You will be working closely with our engineers and data scientists to continuously improve the labeling process.
Responsibilities - Label 1,000+ dining hall food images per day, identifying ingredients- Match images to our existing food dictionary/database - Provide feedback to improve food dictionary and labeling process - Ensure labeled images meet quality standards
Requirements
Passion for reducing food waste
Meticulous attention to detail
Comfortable working with image data
Strong communication skills
Basic computer and internet connection
Benefits
Independent Contractor Perks:
HMO Coverage for eligible locations
Permanent work from home
Immediate hiring
Steady freelance job
Please note that since this is a permanent work-from-home position and an “Independent Contractor” arrangement, the candidates must have their own computer and internet connection. They will handle their own benefits and taxes. The professional fees are on hourly rates and the rate depends on your performance in the application process.
