Senior Data Labeling Jobs in Birmingham

Senior Data Labeling is a Mid-Senior, full-time Remote role focused on producing high-quality training data for AI/ML systems across NLP and computer vision. On Rex.zone, you will label and review datasets used in LLM training pipelines, RLHF, prompt evaluation, and QA evaluation to drive model performance improvement. You will apply annotation guidelines compliance, run training data quality checks, and partner with engineering to resolve edge cases and reduce label noise. This posting covers remote, full-time senior data labeling work supporting tech teams, AI labs, startups, and annotation vendors seeking consistent, auditable outputs.

Job Heading: Senior Data Labeling Jobs in Birmingham

What You Will Do

You will execute and review complex data labeling tasks for NLP and computer vision datasets, calibrating decisions to match annotation guidelines and client specs. You will perform RLHF-oriented preference labeling, prompt evaluation, and QA evaluation to improve large language model evaluation signals. You will run training data quality audits, identify systematic errors, and recommend process changes that reduce rework and improve inter-annotator agreement. You will label content safety datasets (policy-based categories, severity, and rationales) and escalate ambiguous edge cases with clear documentation. You will collaborate with engineering and ops to refine taxonomies, sampling plans, gold sets, and acceptance criteria that support model performance improvement.

Core Workflows and Domains

LLM training pipelines: instruction-following data, conversational turns, tool-use traces, and safe completion checks. RLHF: pairwise ranking, preference modeling signals, rubric-based scoring, and bias/error spotting. NLP: named entity recognition, intent classification, sentiment, retrieval relevance, and prompt response evaluation. Computer vision annotation: bounding boxes, polygons, keypoints, segmentation masks, and attribute tagging for quality-critical datasets. Content safety labeling: policy-aligned categorization, jailbreak detection patterns, and risk severity scoring with consistent rationales.

Required Qualifications

Demonstrated experience in senior or lead-level data labeling, data annotation, or evaluation work in production settings. Strong proficiency with annotation guidelines compliance, including handling edge cases, documenting decisions, and maintaining consistent labeling standards. Practical knowledge of QA evaluation practices such as sampling, auditing, adjudication, and error taxonomy creation. Familiarity with at least one: RLHF workflows, prompt evaluation, named entity recognition, or computer vision annotation. Ability to communicate clearly with engineering stakeholders, translate model errors into labeling fixes, and support training data quality improvements.

Preferred Qualifications

Experience supporting large language model evaluation with rubrics, gold sets, and calibration sessions. Background in content safety labeling for policy-based datasets and safety-aligned evaluation. Exposure to active learning, dataset curation, and label-noise reduction strategies. Comfort working with multiple annotation tools and producing audit-ready documentation for client reviews. Experience mentoring annotators and improving inter-annotator agreement through training and feedback loops.

Quality Standards You Will Own

Training data quality: error detection, root-cause analysis, and corrective actions that measurably reduce defects. Annotation guidelines compliance: consistent interpretation, documented edge-case handling, and version control discipline. Large language model evaluation rigor: rubric alignment, calibration, and clear rationales for preference and scoring decisions. Model performance improvement feedback: actionable insights from QA evaluation and prompt evaluation tied to dataset updates. Operational reliability: predictable throughput, auditability, and dependable delivery in a Remote full-time environment.

Who This Role Is For

Professionals seeking senior data labeling jobs in Birmingham who want Remote, full-time work tied directly to AI/ML training outcomes. Candidates who enjoy structured decision-making, detailed documentation, and iterative QA evaluation cycles. Specialists interested in NLP, computer vision annotation, content safety labeling, and LLM training pipelines. People who can balance speed with high precision and can lead by example through calibration and guideline stewardship.

How to Apply on Rex.zone

Apply through Rex.zone using a resume that highlights data labeling, RLHF, QA evaluation, and domain expertise in NLP or computer vision annotation. Include examples of annotation guidelines compliance work such as audits, adjudication notes, gold-set creation, or rubric design. If you have content safety labeling or large language model evaluation experience, describe the policies, rubrics, and quality metrics you used. Ensure you are comfortable with a Remote, FULL_TIME schedule and can support consistent training data quality expectations.

Frequently Asked Questions

Q: Is this role Remote even though it targets Birmingham?
Yes. The posting targets senior data labeling jobs in Birmingham for search intent, but the Remote Type is Remote and the work is performed remotely within the US.
Q: What types of tasks are included in Senior Data Labeling?
Typical tasks include data labeling and review, RLHF preference labeling, prompt evaluation, QA evaluation audits, named entity recognition, computer vision annotation, and content safety labeling, all aligned to annotation guidelines compliance.
Q: What does success look like in this role?
Success is measured by training data quality, consistent guideline adherence, strong inter-annotator agreement, clear documentation of edge cases, and contributions that support model performance improvement in LLM training pipelines.
Q: Which domains are most relevant: NLP, computer vision, or content safety?
All may apply. The role commonly spans NLP (including named entity recognition), computer vision annotation, and content safety labeling, depending on project needs and evaluation priorities.
Q: Is this job contract or freelance?
No. This posting is for FULL_TIME employment; however, Rex.zone may list contract and freelance roles separately for similar data labeling and QA evaluation work.
Q: What experience level is expected?
The Experience Level is Mid-Senior. Candidates should have hands-on experience with complex labeling workflows, QA evaluation, and guideline-driven decision-making, ideally with exposure to RLHF or large language model evaluation.
Q: What salary range is listed for this role?
The salary range is USD 63360 to USD 126720 per year, with Pay Period set to YEAR.

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