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Super Annotation Jobs (NOW HIRING)

... super user, participates in committees related to modality, department, or facility, participates ... Assesses diagnostic images for technical quality, proper annotation, and patient identification ...

... super user, participates in committees related to modality, department, or facility, participates ... Assesses diagnostic images for technical quality, proper annotation, and patient identification ...

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Super Annotation information

How does a Super Annotation specialist typically collaborate with data scientists and machine learning engineers in a project setting?

Super Annotation specialists play a crucial role in supporting machine learning projects by meticulously labeling data and ensuring high-quality annotations. They often work closely with data scientists and machine learning engineers to understand project objectives, clarify annotation guidelines, and provide feedback on ambiguous cases. Regular meetings and open communication help align annotation efforts with the evolving needs of the model development team, ensuring the dataset supports effective training and evaluation. This collaborative environment not only enhances the accuracy of the data but also fosters continuous learning and improvement for all team members.

How much does SuperAnnotate pay?

SuperAnnotate offers competitive salaries for annotation and data labeling roles, with pay rates typically ranging from $12 to $20 per hour depending on experience and location. Compensation may also include benefits such as flexible schedules and opportunities for skill development in annotation tools and AI data preparation.

What are the key skills and qualifications needed to thrive as a Super Annotation Specialist, and why are they important?

To thrive as a Super Annotation Specialist, you need excellent attention to detail, a solid understanding of data labeling processes, and familiarity with domain-specific guidelines, often supported by a relevant degree or training. Proficiency with annotation tools such as Labelbox, Supervisely, or CVAT, and knowledge of data management systems are typically required. Strong communication, problem-solving skills, and the ability to follow complex instructions make someone stand out in this role. These skills ensure high-quality, consistent data annotations, which are vital for developing reliable machine learning and AI models.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-paying position in artificial intelligence, such as senior data scientist, machine learning engineer, or AI research director, often requiring advanced skills in programming, data analysis, and deep learning. These roles usually involve leading projects, developing algorithms, and may require extensive experience or specialized certifications. Compensation at this level reflects the complexity and impact of the work in the AI industry.

What are Super Annotation jobs?

Super Annotation jobs involve labeling, tagging, and categorizing data such as images, text, audio, or video for use in machine learning and artificial intelligence projects. Professionals in these roles use specialized tools to accurately mark up data, ensuring that algorithms can learn from high-quality, well-structured information. The goal is to create precise datasets that improve the performance of AI models in tasks like object detection, text classification, and speech recognition.

What is the difference between Super Annotation vs Data Labeler?

AspectSuper AnnotationData Labeler
Required CredentialsBasic understanding of annotation tools, sometimes with specialized trainingTypically no formal credentials, on-the-job training common
Work EnvironmentRemote or on-site, often in tech or AI companiesPrimarily remote or on-site data annotation tasks
Industry UsageUsed in AI, machine learning, and data science projectsCommon in data preparation for AI and machine learning
Search & Comparison IntentUnderstanding roles in AI data annotationEntry-level data annotation roles

Super Annotation involves more advanced annotation tasks, often requiring specialized training, while Data Labeler typically performs basic labeling tasks with minimal credentials. Both roles are essential in AI development, but Super Annotation usually involves more complex data and tools, making it suitable for those with some experience or training in data annotation.

Is SuperAnnotate legit?

SuperAnnotate is a company that provides data annotation tools used in machine learning and AI projects. It is a legitimate organization with a platform that supports tasks like image and video annotation, often requiring skilled annotators to use specialized software. Job seekers should verify specific roles and company details through official channels.

Which 3 jobs will survive AI?

Super Annotation involves labeling data for AI training, a task that requires human judgment and understanding. Jobs that involve complex problem-solving, creativity, and emotional intelligence—such as healthcare professionals, creative designers, and mental health counselors—are less likely to be fully replaced by AI. These roles often require nuanced decision-making and interpersonal skills that AI cannot replicate fully.
More about Super Annotation jobs
What cities are hiring for Super Annotation jobs? Cities with the most Super Annotation job openings:
What states have the most Super Annotation jobs? States with the most job openings for Super Annotation jobs include:
Infographic showing various Super Annotation job openings in the United States as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.
Data Study Participant (Columbus, Ohio)

Data Study Participant (Columbus, Ohio)

HumanSignal

Columbus, OH

$100/day

Full-time

Posted 27 days ago


Job description

About HumanSignal

Real-world data is the competitive edge in AI.

HumanSignal is a human data partner for companies building AI models and products. Our customers ship better AI, faster, because we partner with their researchers from real-world data creation to annotation to delivery.


We design and create datasets from scratch, recruit and manage the domain experts who evaluate model output, and run everything through our own platform, Label Studio, the open-source standard for data labeling and evaluation, used by over 1 million practitioners worldwide.


We specialize in the operationally complex: real-world data collection, multimodal pipelines, and multi-step workflows. Advanced ML and AI teams use our enterprise platform to run their own data factories, and our services team to extend their reach where in-house capacity runs out.


If you want to do work that materially shapes how the next generation of AI products gets built, we'd love to talk.

Get Paid $100 for 90 Minutes of Fun!

Love being active? Enjoy trying new things? Want to make some quick cash while helping build the future of AI?

We're looking for energetic people in Columbus, Ohio to participate in a super casual, fun data collection study. Think of it as getting paid to hang out, move around, and be yourself in front of a camera—no experience needed, no memorization, just you doing simple everyday actions!

What You'll Do

Visit our Columbus location for a 90-minute session where you'll:

  • Perform easy, everyday activities like walking, gesturing, talking, and moving around
  • Work with our friendly team who'll capture images, videos, and audio
  • Try different setups with various props, backgrounds, and lighting
  • Just be yourself—no acting skills required!
  • Share 8-10 photos from your photo library to aid the study
Why You'll Love This
  • Quick cash — $100 for 90 minutes
  • Zero pressure — just be yourself and have fun
  • Bring your people — friends and family can join
  • Referral bonuses — earn $25 per successful referral
  • Help shape AI technology that learns from real human movement and expression
  • Flexible scheduling — find a time that works for you

Ready to make some easy money while contributing to cutting-edge AI development? Sign up today!


Important: This opportunity is a 1099 contractor assignment and does not constitute employment with HumanSignal.

Demographic Information Disclosure: This research study requires the collection of demographic information (including but not limited to age, gender, ethnicity, and physical characteristics) to ensure appropriate diversity and representation in AI training data. All demographic information collected will be used solely for research purposes and handled in accordance with applicable privacy laws. Participation in this study is completely voluntary, and you have the right to decline participation or withdraw at any time without penalty. By participating, you consent to the collection of this demographic information as part of the study requirements.