Hire Data Annotation Engineers | TeamStation AI

High-quality training data is the foundation of any successful machine learning model. Our Data Annotation Engineers are experts in the complex process of labeling, cleaning, and preparing datasets. We provide talent vetted for their expertise in building efficient labeling workflows, managing data quality, and using modern annotation platforms.

Is 'garbage in, garbage out' crippling your model performance?

The Problem

Inaccurate, inconsistent, or noisy labels in your training data are the number one cause of poor model performance. No amount of algorithmic tuning can fix a bad dataset.

The TeamStation AI Solution

Our engineers are experts in data quality. They are vetted on their ability to design clear labeling guidelines, implement quality control processes (e.g., consensus, review), and use programmatic techniques to clean and enhance datasets, ensuring your model is trained on high-quality, reliable data.

Proof: Measurable improvement in label accuracy and consistency.
Is your data labeling process slow, expensive, and unscalable?

The Problem

Manually labeling large datasets is a slow, costly, and often mind-numbing task. It doesn't scale as your data volumes grow, creating a major bottleneck for your entire AI development lifecycle.

The TeamStation AI Solution

Our engineers are proficient in modern annotation strategies. They can implement programmatic labeling techniques, weak supervision, and active learning to dramatically reduce the amount of manual labeling required, making the process faster, cheaper, and more scalable.

Proof: Reduced cost and time for data annotation projects.

How We Measure Seniority: From L1 to L4 Certified Expert

We don't just match keywords; we measure cognitive ability. Our Axiom Cortex™ engine evaluates every candidate against a 44-point psychometric and technical framework to precisely map their seniority and predict their success on your team. This data-driven approach allows for transparent, value-based pricing.

L1 Proficient

Guided Contributor

Contributes on component-level tasks within the Data Annotation Engineer domain. Foundational knowledge and learning agility are validated.

Evaluation Focus

Axiom Cortex™ validates core competencies via correctness, method clarity, and fluency scoring. We ensure they can reliably execute assigned tasks.

$20 /hour

$3,460/mo · $41,520/yr

± $5 USD

L2 Mid-Level

Independent Feature Owner

Independently ships features and services in the Data Annotation Engineer space, handling ambiguity with minimal supervision.

Evaluation Focus

We assess their mental model accuracy and problem-solving via composite scores and role-level normalization. They can own features end-to-end.

$30 / hour

$5,190/mo · $62,280/yr

± $5 USD

L3 Senior

Leads Complex Projects

Leads cross-component projects, raises standards, and provides mentorship within the Data Annotation Engineer discipline.

Evaluation Focus

Axiom Cortex™ measures their system design skills and architectural instinct specific to the Data Annotation Engineer domain via trait synthesis and semantic alignment scoring. They are force-multipliers.

$40 / hour

$6,920/mo · $83,040/yr

± $5 USD

L4 Expert

Org-Level Architect

Sets architecture and technical strategy for Data Annotation Engineer across teams, solving your most complex business problems.

Evaluation Focus

We validate their ability to make critical trade-offs related to the Data Annotation Engineer domain via utility-optimized decision gates and multi-objective analysis. They drive innovation at an organizational level.

$50 / hour

$8,650/mo · $103,800/yr

± $10 USD

Pricing estimates are calculated using the U.S. standard of 173 workable hours per month, which represents the realistic full-time workload after adjusting for federal holidays, paid time off (PTO), and sick leave.

Core Competencies We Validate for Data Annotation Engineer

Data Labeling and Annotation Workflows
Data Quality and Inter-Annotator Agreement
Annotation Tooling (e.g., Labelbox, Scale AI)
Programmatic Labeling and Weak Supervision
Data Preprocessing and Augmentation

Our Technical Analysis for Data Annotation Engineer

Candidates are given a raw dataset and a labeling task. They must design a complete annotation project, including creating clear guidelines for human labelers, defining a quality control process, and selecting the appropriate tooling. We assess their ability to manage the trade-offs between cost, quality, and speed in a data labeling project.

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