What we do:
Zefr is the global leader in brand suitability targeting and measurement across the world’s largest platforms. Zefr’s technology is helping to power the age of responsible marketing by putting advertisers in control of their content adjacencies based on their own unique brand safety and suitability preferences, mapped to the Global Alliance of Responsible Media’s (GARM) industry standards. As an official YouTube Measurement Program Partner, Meta for Business Partner, and TikTok for Business Partner, the company leverages patented machine learning and AI technology (Cognition AI) to offer brands and agencies more precise and transparent brand safety and suitability activation and measurement solutions on scaled platforms.
The company is headquartered in Los Angeles, California, with additional locations across the globe.
What you’ll do:
We are hiring a Multilingual Data Annotation Specialist responsible for annotating videos, images, and metadata for YouTube, TikTok, Facebook, and other social media content. High-quality human annotation data is an integral part of training Zefr’s sophisticated compound AI systems. Previous data annotation experience is not required. We are excited to welcome someone passionate and curious about machine learning and computer vision. We seek a self-motivated candidate who values high-quality work, adeptly follows instructions and manages tasks to completion, embraces a proactive approach, is unhesitant to seek clarification or offer constructive feedback to enhance processes, possesses strong analytical skills, and enjoys continuous learning and problem-solving.
Here’s what you’ll get to do:
Use in-house tools to label social media content based on Zefr’s various contextual category guidelines
Make judgment calls on nuanced content to provide cognitive and cultural understanding
Apply and refine labeling framework and guidelines
Provide valuable insights on content trends and contribute to the enhancement of an efficient labeling interface
Be willing to work with sensitive content including varying religious and political views, violence, and adult content
You will gain basic machine learning and computer vision knowledge and how annotation data powers Zefr’s machine learning and AI technology
Here’s what we’re looking for:
Fluency in English and Portuguese, Italian, Hindi, or Mandarin required (Spanish fluency, nice to have).
Familiarity using social media platforms, including TikTok, YouTube, Facebook, and Instagram
Strong attention to detail with the ability to perform repetitive tasks with high quality and consistency
Exceptional critical thinking, problem-solving, and communication skills
Ability to work in a fast-paced environment, adept at rapid learning, and skilled in effectively prioritizing tasks
Capable in both collaborative teamwork and independent work
Proficiency in Google Workspace applications
SQL familiarity is a plus
Preference for a candidate that is located in (or willing to relocate to) the great Los Angeles area
Zefr is an equal opportunity employer that embraces diversity and inclusion in the workplace. We are committed to building a team that represents a variety of backgrounds, skills, and perspectives because we know this only makes us better. We strongly encourage women, persons of color, LGBTQIA+ individuals, persons with disabilities, members of ethnic minorities, foreign-born residents, and veterans to apply even if you do not meet 100% of the qualifications.
Benefits (for US based employees):
Flexible PTO
Medical, dental, and vision insurance with FSA options
Company-paid life insurance
Paid parental leave
401(k) with company match
Professional development opportunities
10+ paid holidays off
Flexible hybrid work schedules
“Summer Fridays” (shorter work days on select Fridays during the summertime)
In-office lunches and lots of free food
Optional in-person and virtual events (we like to celebrate!)
Compensation (for US based employees):
The anticipated salary for this position is between $60,000 to $65,000. Within the range, individual pay is determined by factors such as job-related skills, experience, and relevant education or training. If your compensation expectations fall outside of this range, it may still be worth having a conversation.
