Senior Data Annotator Jobs Geneva

Senior data annotator jobs in Geneva focus on producing high-quality training data for AI/ML systems—spanning data labeling, RLHF preference ranking, prompt evaluation, QA evaluation, and safety labeling that improve large language model evaluation and downstream model performance. On Rex.zone, this remote full-time role supports real-world LLM training pipelines and computer vision annotation workflows with strict annotation guidelines compliance, training data quality checks, and continuous model performance improvement. If you have strong judgment, attention to detail, and experience leading complex labeling projects (NLP, content safety, NER, CV), explore this Rex.zone opportunity and apply to help teams ship reliable AI.

Job Heading: Senior Data Annotator Jobs Geneva

About the Role

You will deliver and review high-accuracy annotations used to train and evaluate AI models across NLP, LLMs, and computer vision. You will work with project leads to interpret labeling specs, execute RLHF-style preference ranking and rubric-based scoring, and run QA evaluation to ensure training data quality. This role contributes directly to large language model evaluation, safety policy enforcement, and model performance improvement through consistent annotation guidelines compliance.

What You Will Do

Produce gold-standard labels for complex tasks (classification, span labeling, named entity recognition, relation extraction, sentiment, safety). Execute RLHF workflows such as pairwise ranking, preference labeling, and rationale capture. Perform prompt evaluation and response grading using calibrated rubrics for helpfulness, harmlessness, and factuality. Complete QA evaluation including inter-annotator agreement checks, error analysis, and corrective feedback. Document edge cases, update taxonomies, and propose guideline clarifications to stabilize label consistency. Support computer vision annotation as needed (bounding boxes, polygons, keypoints) with quality thresholds. Partner with operations and engineering to improve tooling, sampling, and audit strategies for training data quality.

Required Qualifications

Mid-Senior experience in data annotation, data labeling, or AI evaluation with measurable quality outcomes. Demonstrated ability to follow and refine annotation guidelines, manage ambiguity, and maintain consistency at scale. Hands-on experience with QA evaluation methods (spot checks, audits, adjudication, agreement metrics). Familiarity with LLM evaluation concepts, RLHF labeling patterns, and prompt evaluation workflows. Strong written communication for documenting decisions, edge cases, and guideline updates.

Preferred Qualifications

Experience with content safety labeling, policy-based moderation, and safety taxonomy creation. Background in named entity recognition or structured NLP annotation (spans, entities, relations). Exposure to computer vision annotation tools and quality gates. Comfort collaborating with cross-functional teams supporting LLM training pipelines, including data ops and engineering stakeholders.

Quality Standards You Will Own

Training data quality targets via calibrated rubrics, reproducible decisions, and complete audit trails. Annotation guidelines compliance through versioned documentation, examples, and adjudication patterns. Model performance improvement support through high-signal error analysis and structured feedback loops.

How to Apply on Rex.zone

Apply through Rex.zone with a concise summary of your data annotation experience, example task types (RLHF, QA evaluation, prompt evaluation, NER, content safety, computer vision), and the quality controls you have used to maintain training data quality.

Frequently Asked Questions

Q: What does a senior data annotator do in LLM training pipelines?
A senior data annotator creates and validates high-quality labels used for model training and evaluation, including RLHF preference ranking, prompt evaluation, QA evaluation, and content safety labeling. The work improves training data quality and helps drive model performance improvement.
Q: Is this role remote and full-time?
Yes. The role is explicitly marked Remote and the employment type is FULL_TIME.
Q: What annotation domains are covered in this job posting?
The role supports NLP and LLM evaluation (including named entity recognition, prompt evaluation, RLHF) and may include computer vision annotation and content safety labeling depending on project needs.
Q: What skills align most with senior data annotator jobs in Geneva?
Strong alignment includes senior data annotation, data labeling, training data quality, annotation guidelines compliance, QA evaluation, RLHF, prompt evaluation, LLM evaluation, named entity recognition, computer vision annotation, and content safety labeling.
Q: How is quality measured for data labeling and evaluation work?
Quality is measured through QA evaluation processes such as audits, adjudication, agreement checks, rubric calibration, and error analysis—ensuring consistent annotation guidelines compliance and reliable training data quality.

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