Multilingual Data Annotation Specialist

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 guidelinesMake judgment calls on nuanced content to provide cognitive and cultural understanding Apply and refine labeling framework and guidelinesProvide valuable insights on content trends and contribute to the enhancement of an efficient labeling interfaceBe willing to work with sensitive content including varying religious and political views, violence, and adult contentYou 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 Spanish, Portuguese, German, Arabic, Turkish, Japanese, Mandarin, or ThaiFamiliarity 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 consistencyExceptional critical thinking, problem-solving, and communication skillsAbility to work in a fast-paced environment, adept at rapid learning, and skilled in effectively prioritizing tasksCapable in both collaborative teamwork and independent workProficiency in Google Workspace applicationsSQL familiarity is a plusPreference 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 PTOMedical, dental, and vision insurance with FSA optionsCompany-paid life insurancePaid parental leave401(k) with company matchProfessional development opportunities10+ paid holidays offFlexible hybrid work schedules“Summer Fridays” (shorter work days on select Fridays during the summertime)In-office lunches and lots of free foodOptional 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.