Computational Linguist – Annotation – Apply Before 30 April 2026

As AI systems move beyond basic text processing into deeper reasoning and multimodal understanding, the quality of linguistic annotation has become a limiting factor. Teams are hiring experienced computational linguists now because poorly designed annotation frameworks slow model performance and introduce long-term errors.

Many people assume annotation work is routine labeling. In reality, this role sits at the foundation of modern language models. Decisions made at the schema and guideline level directly affect how machines interpret meaning, context, and discourse at scale.

This is not passive income work; it requires consistent attention, communication, and follow-through.

In this role, your work centers on designing annotation systems that both linguists and machines can work with effectively. This is not a role for someone who prefers purely academic theory without operational responsibility. What matters most here is your ability to balance linguistic depth with practical scalability.

You’ll do well here if you enjoy translating abstract linguistic concepts into clear, testable annotation decisions. Many people assume tools and pipelines already exist, but in reality, you will help shape and refine them alongside engineering and data teams.

Key Responsibilities

• Design linguistic annotation schemas for advanced NLP tasks including semantics, discourse, and coreference.
• Develop detailed annotation guidelines that support accuracy and annotator consistency.
• Translate product and research requirements into practical annotation frameworks.
• Ensure linguistic coverage while keeping schemas computationally efficient.
• Build and maintain annotation interfaces, QA dashboards, and data workflows.
• Develop scripts for data preprocessing, validation, and analysis.

Required Qualifications

• Bachelor’s degree in Linguistics, English, or a related field.
• At least 5 years of experience in computational linguistics, NLP, or annotation operations.
• Strong grounding in syntax, semantics, pragmatics, and discourse analysis.
• Experience customizing annotation tools and workflows.
• Proven ability to manage annotation projects from pilot to production.
• Clear written and verbal communication skills.

Employer: Odixcity Consulting

Employment Type: Full-time

Location: Remote (Worldwide)

Salary: ₦500,000 per month

This is a direct remote opportunity. Shortlisted candidates will receive clear next steps and role expectations before any engagement begins.

How to Apply

Click Here to Apply

Sample ATS-Aligned CV

How This CV Helps You Stand Out

This CV emphasizes end-to-end annotation responsibility, not just labeling tasks. Recruiters quickly see evidence of schema design, cross-team collaboration, and production-level impact.

With minor tailoring, it can be adapted to reflect your specific linguistic specializations.

Interview Preparation

Role-Specific Questions

• How do you balance linguistic precision with annotation scalability?
• Describe a complex annotation schema you designed.
• How do you measure annotation quality?
• What challenges arise when annotators interpret abstract guidelines?
• How do you work with engineers on schema constraints?
• Describe your approach to multimodal annotation.
• How do you revise schemas after pilot feedback?
• What tooling decisions have you influenced?

General Questions

• Why are you interested in this role?
• How do you manage remote collaboration?
• How do you prioritize competing requests?
• How do you handle feedback on your designs?
• What keeps you current in NLP?

Do’s & Don’ts

• Do explain your reasoning clearly.
• Do reference real annotation projects.
• Do discuss trade-offs honestly.
• Don’t rely only on theory.
• Don’t ignore operational constraints.
• Don’t overcomplicate explanations.

Preparation Checklist

• Review past annotation frameworks you’ve built.
• Prepare examples of guideline trade-offs.
• Be ready to discuss tooling choices.
• Understand end-to-end data pipelines.

Strong candidates demonstrate how linguistic decisions scale beyond individual datasets into long-term system performance.