Entry Level AI Jobs in Brazil (Remote)

Entry level AI jobs in Brazil at Rex.zone focus on building real-world AI/ML training workflows through data labeling, RLHF, and large language model evaluation. In this remote, full-time role, you will follow annotation guidelines to create high-quality training data, run prompt evaluation, perform QA evaluation, and support model performance improvement across NLP, computer vision, and content safety labeling. You will collaborate with AI labs, tech startups, BPOs, and annotation vendors using structured rubrics and tooling to improve dataset quality, reduce annotation errors, and increase consistency for LLM training pipelines.

Job Heading: Entry Level AI Jobs in Brazil (Remote)

About Rex.zone

Rex.zone connects remote contributors with production AI/ML data operations work that supports LLM training pipelines. Projects commonly include data labeling, RLHF preference data collection, prompt evaluation, QA evaluation, and content safety labeling across multiple domains (NLP, CV, and multimodal).

About the Role

You will support entry level AI jobs in Brazil by producing and validating training data used to improve large language model evaluation and downstream model performance. Your day-to-day work includes reading tasks carefully, applying consistent rubrics, tagging entities for named entity recognition, rating model responses for RLHF, and performing QA checks to ensure annotation guidelines compliance. You will work with tooling and issue-tracking to document edge cases, reduce ambiguity, and drive training data quality improvements.

Core Workstreams You Will Touch

You may contribute to RLHF preference ranking, prompt evaluation, and QA evaluation for conversational AI; data labeling for NLP classification and named entity recognition; computer vision annotation such as bounding boxes, polygons, and segmentation masks; and content safety labeling to support policy compliance and safer model behavior. Assignments vary by employer type, including AI labs, tech startups, BPOs, and annotation vendors.

Responsibilities

Deliver accurate data labeling aligned to annotation guidelines and project rubrics. Perform QA evaluation using checklists to catch schema errors, guideline deviations, and low-confidence labels. Complete prompt evaluation and RLHF tasks by comparing model outputs, ranking responses, and writing short justifications when required. Maintain consistent training data quality across large batches and escalating edge cases with clear examples. Track work in project tools, meet throughput targets, and support model performance improvement through clean, well-documented feedback.

Required Skills

Comfort working with structured rubrics, examples, and annotation guidelines compliance. Ability to reason about language and intent for NLP tasks such as named entity recognition and classification. Attention to detail for QA evaluation and training data quality checks. Basic familiarity with AI/ML concepts such as large language models, RLHF, and evaluation datasets.

Nice to Have

Experience with computer vision annotation (bounding boxes, polygons, segmentation). Familiarity with content safety labeling and policy-driven classification. Exposure to prompt evaluation frameworks, rubric design, or inter-annotator agreement. Experience supporting annotation vendors or BPO-style production workflows.

Remote Work Details

This is a Remote, FULL_TIME position. You will collaborate asynchronously with distributed teams, follow standardized workflows, and use cloud-based labeling and evaluation tools. Remote roles must remain explicitly marked Remote.

How to Apply

Apply through Rex.zone and complete the required screening tasks. Be prepared to demonstrate annotation accuracy, rubric adherence, and practical judgment in prompt evaluation, RLHF, and QA evaluation scenarios relevant to LLM training pipelines.

Frequently Asked Questions

Q: Are these entry level AI jobs in Brazil truly remote?
Yes. The role is explicitly marked Remote and is designed for distributed execution using online labeling and evaluation tools.
Q: What type of AI work will I do in this role?
Typical work includes data labeling, RLHF preference ranking, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling to support LLM training pipelines.
Q: Is this a contract or full-time role?
This posting is for FULL_TIME employment. Rex.zone may also list contract, freelance, and part-time roles on other pages, but this page is full-time.
Q: What does QA evaluation mean in AI data operations?
QA evaluation is the process of checking labeled data and model-evaluation judgments for guideline compliance, consistency, and correctness to protect training data quality and improve model performance.
Q: Do I need prior experience with RLHF?
Prior RLHF experience is helpful but not required. You will use clear rubrics to rank or compare model outputs and provide consistent judgments that can be used for reinforcement learning from human feedback.
Q: Which domains are common for projects?
Common domains include NLP tasks (classification, named entity recognition), large language model evaluation, computer vision annotation, and content safety labeling.
Q: What employer types use this kind of work?
AI labs, tech startups, BPOs, and annotation vendors commonly run these workflows to build and validate datasets for model training and evaluation.

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