$200K/yr
You'll own labeling quality and methodology across Coalition - designing annotation tasks, defining quality frameworks, and ensuring every labeled dataset meets the standard required to ship ...
$200K/yr
You'll own labeling quality and methodology across Coalition - designing annotation tasks, defining quality frameworks, and ensuring every labeled dataset meets the standard required to ship ...
Annotation Labelling information
What is annotation labelling?
Annotation labelling is the process of tagging or marking data—such as images, text, or audio—with relevant information or labels. This is an essential step in preparing datasets for machine learning and artificial intelligence models, as it helps algorithms understand and learn from raw data. Annotation labelling can include tasks like identifying objects in photos, transcribing speech, or categorizing text. Skilled annotators ensure accuracy and consistency to improve model performance. People in this role often use specialized tools or software to streamline and standardize the annotation process.
What are the key skills and qualifications needed to thrive as an annotation labelling specialist?
To thrive as an Annotation Labelling Specialist, you need strong attention to detail, data analysis capabilities, and familiarity with data annotation standards, usually supported by a background in computer science or related fields. Proficiency with annotation tools such as Labelbox, CVAT, or Supervisely, and sometimes knowledge of basic programming or scripting, is typically required. Excellent communication, consistency, and the ability to follow complex instructions are crucial soft skills for producing high-quality labeled data. These skills ensure the accuracy and reliability of datasets, which are foundational for successful machine learning and AI model development.
What are some common challenges faced by annotation labelling professionals, and how can they be managed?
Annotation Labelling professionals often encounter challenges such as maintaining high accuracy while handling repetitive data, meeting tight deadlines, and adapting to evolving project guidelines. To manage these, it’s important to develop strong attention to detail, regularly communicate with team leads to clarify instructions, and leverage annotation tools efficiently. Collaborating closely with quality assurance teams can also help identify and correct errors early, ensuring consistently high-quality outputs.
What is the difference between Annotation Labelling vs Data Labeling Specialist?
| Aspect | Annotation Labelling | Data Labeling Specialist |
|---|---|---|
| Credentials | Basic technical skills, attention to detail | Similar skills, sometimes additional domain knowledge |
| Work Environment | Data annotation platforms, remote or office | Data annotation tasks, often remote or in-office |
| Industry Usage | AI, machine learning, autonomous vehicles | AI, machine learning, healthcare, retail |
| Search & Comparison | Commonly compared for entry-level data tasks | Related but broader role |
Annotation Labelling involves marking data such as images, text, or videos to train AI models. Data Labeling Specialists perform similar tasks but may have a broader scope, including verifying and managing labeled data. Both roles are essential in AI development, often overlapping in skills and work environment, but Annotation Labelling is more focused on the annotation process itself.
What are popular job titles related to Annotation Labelling jobs in West Virginia?
For Annotation Labelling jobs in West Virginia, the most frequently searched job titles are:
What job categories do people searching Annotation Labelling jobs in West Virginia look for?
The top searched job categories for Annotation Labelling jobs in West Virginia are:
Senior Manager, Machine Learning (Data Operations)
Coalition, Inc.
• Real Estate • 11 - 50 employees11 - 50 employees
$200K/yr
Full-time
Medical, Dental, Vision, PTO
Re-posted 25 days ago
Job description
About the role
You'll own labeling quality and methodology across Coalition - designing annotation tasks, defining quality frameworks, and ensuring every labeled dataset meets the standard required to ship production ML models. You'll manage our labeling platform (Label Studio), work directly with ML and product teams to structure labeling programs, and oversee outsourced labeling vendors to hit quality and throughput targets.
This role reports to the Chief Product Officer and sits at the intersection of product, ML, and operations. You won't manage internal labelers - all annotation work is outsourced - but you will be the single point of accountability for whether Coalition's labeled data is accurate, consistent, and fit for purpose.Responsibilities
Coalition's machine learning models are only as good as the data they're trained on. This role exists to make sure that data is right.
You'll own labeling quality and methodology across Coalition - designing annotation tasks, defining quality frameworks, and ensuring every labeled dataset meets the standard required to ship production ML models. You'll manage our labeling platform (Label Studio), work directly with ML and product teams to structure labeling programs, and oversee outsourced labeling vendors to hit quality and throughput targets.
This role reports to the Chief Product Officer and sits at the intersection of product, ML, and operations. You won't manage internal labelers - all annotation work is outsourced - but you will be the single point of accountability for whether Coalition's labeled data is accurate, consistent, and fit for purpose.
- Labeling quality & methodology: Define annotation guidelines, taxonomies, and edge-case protocols for each labeling program. Establish gold standard datasets, inter-annotator agreement (IAA) targets, and audit sampling processes. Identify and remediate mislabeled data in existing datasets.
- Platform & tooling: Serve as the primary user and requirements driver for Label Studio - defining project configuration needs, workflow designs, pre-labeling pipeline requirements, and integration points with ML infrastructure. Partner with the data engineering team that builds and maintains the platform.
- Cross-functional partnership: Work with ML engineers, data scientists, and product managers to translate model requirements into well-structured labeling tasks. Challenge teams on task design when labeling instructions are ambiguous or likely to produce unreliable labels.
- Vendor management: Source, onboard, and manage external labeling vendors and BPOs in coordination with Coalition's operations team. Set quality SLAs, run calibration sessions, and manage feedback loops to labelers. Hold vendors accountable to accuracy, not just throughput.
- Measurement & improvement: Define and track operational metrics - label accuracy, IAA scores, cost per label, turnaround time - and use them to drive continuous improvement. Identify opportunities for active learning, model-assisted labeling, and pre-annotation to reduce cost without sacrificing quality.
- 5+ years in ML data operations, data labeling, or a related field (ML engineering, data science, or data engineering with heavy labeling exposure)
- Deep understanding of annotation quality frameworks: IAA, consensus labeling, gold standard evaluation, error taxonomy, and calibration workflows
- Direct experience managing labeling platforms (Label Studio strongly preferred; Scale AI, Labelbox, Prodigy, or similar acceptable)
- Track record managing outsourced labeling vendors or BPOs for ML data production
- Familiarity with common ML labeling tasks: text classification, NER, document extraction, intent detection
- Comfortable working in Python and SQL; bonus if you've built tooling around labeling workflows or quality measurement
- Strong opinions on what makes labeled data good or bad, and the willingness to push back when it's bad
- Experience in insurance, cybersecurity, or fintech is a plus but not required
Our compensation reflects the cost of labor across several US geographic markets. The US base salary for this position ranges from $134,400/year in our lowest geographic market up to $200,000/year in our highest geographic market. Consistent with applicable laws, an employee's pay within this range is based on a number of factors, which include but are not limited to relevant education, skills, job-related knowledge, qualifications, work experience, credentials, and/or geographic location. Your recruiter can share more on target salary for your location during the interview process. Coalition, Inc. reserves the right to modify this range as needed.
Perks- 100% medical, dental and vision coverage
- Flexible PTO policy
- Annual home office stipend and WeWork access
- Mental & physical health wellness programs (One Medical, Headspace, Wellhub, and more)!
- Competitive compensation and opportunity for advancement
About Coalition Space
Sourced by ZipRecruiter
Industry
Real estate
Company size
11 - 50 Employees
Headquarters location
New York, NY, US
Year founded
2002