... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
Strong understanding of machine learning algorithms, including supervised and unsupervised learning ... Experience with data annotation tools and processes. Benefits: * Competitive Salary * Stock Option
Strong understanding of machine learning algorithms, including supervised and unsupervised learning ... Experience with data annotation tools and processes. Benefits: * Competitive Salary * Stock Option
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
Strong understanding of machine learning algorithms, including supervised and unsupervised learning ... Experience with data annotation tools and processes. Benefits: * Competitive Salary * Stock Option
Strong understanding of machine learning algorithms, including supervised and unsupervised learning ... Experience with data annotation tools and processes. Benefits: * Competitive Salary * Stock Option
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
... actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling ... You do not need prior experience in data annotation. What matters is that you understand why models ...
Senior Robotics Data Engineer - Only W2
Warren, MI · On-site
$99K - $135K/yr
... supervised/self-supervised data labeling workflows to minimize manual annotation costs. · Enable simulation-to-real (Sim2Real) data workflows, including domain randomization and synthetic data ...
Quick apply
Senior Robotics Data Engineer - Only W2
Warren, MI · On-site
$99K - $135K/yr
... supervised/self-supervised data labeling workflows to minimize manual annotation costs. · Enable simulation-to-real (Sim2Real) data workflows, including domain randomization and synthetic data ...
Data Operations Supervisor- 2nd shift
Savannah, GA · On-site
$102K - $128K/yr
... Supervisor, who will be responsible for the execution of a team of Data Operations associates from ... Oversee contractors and internal employees responsible for ML annotation tasks, robot teleoperation ...
Data Operations Supervisor- 2nd shift
Savannah, GA · On-site
$102K - $128K/yr
... Supervisor, who will be responsible for the execution of a team of Data Operations associates from ... Oversee contractors and internal employees responsible for ML annotation tasks, robot teleoperation ...
Data Operations Supervisor- 2nd shift
Savannah, GA · On-site
$102K - $128K/yr
... Supervisor, who will be responsible for the execution of a team of Data Operations associates from ... Oversee contractors and internal employees responsible for ML annotation tasks, robot teleoperation ...
Data Operations Supervisor- 2nd shift
Savannah, GA · On-site
$102K - $128K/yr
... Supervisor, who will be responsible for the execution of a team of Data Operations associates from ... Oversee contractors and internal employees responsible for ML annotation tasks, robot teleoperation ...
Data Operations Supervisor- 2nd shift
Savannah, GA · On-site
$102K - $128K/yr
... Supervisor, who will be responsible for the execution of a team of Data Operations associates from ... Oversee contractors and internal employees responsible for ML annotation tasks, robot teleoperation ...
Data Operations Supervisor- 2nd shift
Savannah, GA · On-site
$102K - $128K/yr
... Supervisor, who will be responsible for the execution of a team of Data Operations associates from ... Oversee contractors and internal employees responsible for ML annotation tasks, robot teleoperation ...
Data Operations Supervisor- 1st shift
Savannah, GA · On-site
$90K - $115K/yr
... First Shift Supervisor, who will be responsible for the execution of a team of Data Operations ... Oversee contractors and internal employees responsible for ML annotation tasks, robot teleoperation ...
Data Operations Supervisor- 1st shift
Savannah, GA · On-site
$90K - $115K/yr
... First Shift Supervisor, who will be responsible for the execution of a team of Data Operations ... Oversee contractors and internal employees responsible for ML annotation tasks, robot teleoperation ...
Sr. Research Data Scientist
San Diego, CA · On-site
$150K - $180K/yr
Own the full data lifecycle for visual tasks: dataset curation, annotation strategy, augmentation ... Solid grasp of deep learning fundamentals: supervised and self-supervised learning, representation ...
Sr. Research Data Scientist
San Diego, CA · On-site
$150K - $180K/yr
Own the full data lifecycle for visual tasks: dataset curation, annotation strategy, augmentation ... Solid grasp of deep learning fundamentals: supervised and self-supervised learning, representation ...
Own the full data lifecycle for visual tasks: dataset curation, annotation strategy, augmentation ... Solid grasp of deep learning fundamentals: supervised and self-supervised learning, representation ...
Own the full data lifecycle for visual tasks: dataset curation, annotation strategy, augmentation ... Solid grasp of deep learning fundamentals: supervised and self-supervised learning, representation ...
Data Operations Supervisor- 1st shift
Savannah, GA · On-site
$90K - $115K/yr
... First Shift Supervisor, who will be responsible for the execution of a team of Data Operations ... Oversee contractors and internal employees responsible for ML annotation tasks, robot teleoperation ...
Data Operations Supervisor- 1st shift
Savannah, GA · On-site
$90K - $115K/yr
... First Shift Supervisor, who will be responsible for the execution of a team of Data Operations ... Oversee contractors and internal employees responsible for ML annotation tasks, robot teleoperation ...
$90K - $115K/yr
... First Shift Supervisor, who will be responsible for the execution of a team of Data Operations ... Oversee contractors and internal employees responsible for ML annotation tasks, robot teleoperation ...
$90K - $115K/yr
... First Shift Supervisor, who will be responsible for the execution of a team of Data Operations ... Oversee contractors and internal employees responsible for ML annotation tasks, robot teleoperation ...
Data Annotation Supervisor information
See salary details
$31K - $43.8K
7% of jobs
$43.8K - $56.6K
8% of jobs
$65.3K is the 25th percentile. Wages below this are outliers.
$56.6K - $69.5K
14% of jobs
$69.5K - $82.3K
18% of jobs
The median wage is $84.6K / yr.
$82.3K - $95.1K
15% of jobs
$95.1K - $107.9K
7% of jobs
$117.5K is the 75th percentile. Wages above this are outliers.
$107.9K - $120.7K
7% of jobs
$120.7K - $133.5K
6% of jobs
$133.5K - $146.4K
6% of jobs
$146.4K - $159.2K
4% of jobs
$159.2K - $172K
6% of jobs
$31K
$97.1K
$172K
How much do data annotation supervisor jobs pay per year?
As of Sep 11, 2026, the average yearly pay for data annotation supervisor in the United States is $97,145.00, according to ZipRecruiter salary data. Most workers in this role earn between $66,000.00 and $125,500.00 per year, depending on experience, location, and employer.
What is a data annotation supervisor?
Data Annotation Supervisors are professionals responsible for overseeing teams that label, tag, or annotate data used to train machine learning and artificial intelligence models. They ensure the accuracy, quality, and consistency of annotated data, manage workflow, and provide feedback or training to data annotators. Their role is crucial for maintaining data integrity, meeting project deadlines, and supporting the development of reliable AI systems. Data Annotation Supervisors often collaborate with data scientists and project managers to align annotation tasks with project goals.
What are the key skills and qualifications needed to thrive as a data annotation supervisor?
To thrive as a Data Annotation Supervisor, you need expertise in data labeling processes, quality assurance, and team leadership, often supported by a bachelor’s degree in a relevant field. Familiarity with annotation tools, data management systems, and project tracking software is typically required. Strong communication, attention to detail, and problem-solving abilities are crucial soft skills for ensuring high-quality outcomes and effective team management. These skills enable supervisors to maintain data accuracy, meet project deadlines, and support the development of reliable machine learning models.
What are some common challenges faced by data annotation supervisors, and how can they be managed effectively?
Data Annotation Supervisors often encounter challenges such as maintaining annotation quality across a diverse team, meeting tight project deadlines, and ensuring clear communication of guidelines. To manage these effectively, supervisors typically implement regular quality checks, provide ongoing training, and foster open communication channels for feedback and clarification. Leveraging annotation tools and establishing clear performance metrics also help in maintaining consistency and efficiency within the team.
What is the difference between Data Annotation Supervisor vs Data Labeling Specialist?
| Aspect | Data Annotation Supervisor | Data Labeling Specialist |
|---|---|---|
| Credentials | High school diploma or equivalent; experience in data annotation | High school diploma or equivalent; training in labeling tools |
| Work Environment | Supervisory role overseeing teams in office or remote settings | Hands-on labeling work, often in a collaborative environment |
| Responsibilities | Managing annotation teams, quality control, workflow coordination | Performing data labeling tasks, following guidelines, ensuring accuracy |
The Data Annotation Supervisor oversees and manages data annotation teams, focusing on quality and workflow, while Data Labeling Specialists perform the actual labeling tasks. Both roles require familiarity with annotation tools, but the supervisor has additional responsibilities in team management and quality assurance.
What cities are hiring for Data Annotation Supervisor jobs?
Cities with the most Data Annotation Supervisor job openings:
What states have the most Data Annotation Supervisor jobs?
States with the most job openings for Data Annotation Supervisor jobs include:
What are popular job titles related to Data Annotation Supervisor jobs?
For Data Annotation Supervisor jobs, the most frequently searched job titles are:
LLM Post-Training and Evaluation Researcher (Contract)
Hayward, CA • On-site
Cobalt
Other
This job post has expired today. Applications are no longer accepted.
Key responsibilities
Produce written reasoning traces and expert reference answers on technical problems.
Evaluate and rank model outputs on technical questions, articulating differences between responses.
Design rubrics, reward criteria, and partial-credit schemes for multistep tasks.
Job description
About the role:
Cobalt is seeking researchers and engineers with direct experience in language model post-training and evaluation, to produce the expert reasoning and evaluation data frontier labs use to improve model behavior.
This opportunity is suited to people who have worked on the parts of the stack closest to how a model actually behaves: supervised fine-tuning, preference optimization and RLHF, reward modeling, inference-time reasoning methods, and the design of evaluations that hold up. You may have done this in a lab, in industry, or in serious open-source work.
You do not need prior experience in data annotation. What matters is that you understand why models fail in the ways they do, and that you can write the kind of data and criteria that fix it.
What you'll do:
Depending on the project, you may:
- Produce written reasoning traces and expert reference answers on hard technical problems, at the standard of quality a post-training set requires rather than merely correct answers
- Author evaluation items and benchmark tasks with verifiable success criteria, including cases specifically designed to separate genuine capability from pattern matching
- Evaluate and rank model outputs on technical questions, articulating precisely what separates a strong response from one that is fluent but subtly wrong
- Design rubrics, reward criteria, and partial-credit schemes for multistep tasks, and identify where a criterion would be gameable or would reward the wrong behavior
- Classify observed failures into a consistent taxonomy, and assess whether a stated conclusion is supported by the underlying reasoning
Projects follow their own guidelines, formatting conventions, and quality standards, and you will work with feedback from reviewers and lab research teams.
Required qualifcations
- Direct experience with language model post-training or evaluation, such as supervised fine-tuning, preference optimization or RLHF, reward modeling, or benchmark and eval design, whether in research or industry
- A PhD in a quantitative discipline, or equivalent depth demonstrated through published work, open-source contributions, or production systems
- Strong coding ability in Python, and working command of at least one deep learning framework
- Understanding of common failure modes in current models, including reward hacking, sycophancy, and answers that are right for the wrong reasons
- Ability to explain each step of your reasoning clearly in writing, and to specify criteria precisely enough that another annotator would apply them the same way
Why join Cobalt AI:
- Advance frontier AI where it counts. Apply your expertise to data that frontier labs cannot obtain any other way, where your reasoning directly shapes how the next generation of models works through technical problems.
- Grow professionally. Expand your influence through evaluation projects, advisory roles, and research collaborations, while developing a working understanding of how frontier models are trained and assessed.
- Work with a top-tier network. Collaborate with researchers and engineers from leading institutions and labs on high-impact, flexible work.
- Set your own schedule. Flexible 10 to 40 hour weeks that fit around your existing work and your life.
- Competitive pay. Rates vary by project and are determined by a number of factors, including scope, skillset, and experience.