Entry Level STEM Jobs United States

Entry Level STEM Jobs United States at Rex.zone connect early-career STEM talent in the US to remote, full-time engineering work supporting AI/ML systems and LLM training pipelines. In this role, you help improve model performance through data labeling, RLHF evaluation, QA evaluation, and prompt evaluation workflows used by AI labs, tech startups, annotation vendors, and BPO teams. You will apply annotation guidelines compliance, content safety labeling practices, and structured feedback to strengthen training data quality for NLP and computer vision use cases while collaborating asynchronously with distributed teams at Rexzone.

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Job Heading: Entry Level STEM Jobs United States

Title: Entry Level STEM Jobs United States | Date: 25-02-2026 | Company: Rexzone | Country: US | Remote Type: Remote | Employment Type: FULL_TIME | Experience Level: Mid-Senior | Industry: Technology | Job Function: Engineering | Skills: STEM fundamentals, Python, SQL, data labeling, RLHF, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, annotation guidelines compliance, training data quality, LLM evaluation, model performance improvement | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR

About the Role

You will support remote engineering workflows that connect STEM problem-solving with AI/ML data operations. Your work will contribute to LLM training pipelines by producing high-quality labeled datasets, running QA evaluation checks, and completing RLHF-style preference and prompt evaluation tasks. Projects may span NLP (named entity recognition, text classification, summarization evaluation), computer vision annotation (bounding boxes, segmentation), and content safety labeling. You will follow annotation guidelines compliance standards, document edge cases, and provide structured rationales that help improve training data quality and downstream model performance improvement.

What You Will Do

Execute data labeling tasks across text, image, and multimodal datasets; perform QA evaluation using sampling plans, disagreement analysis, and error taxonomy; complete RLHF preference ranking and prompt evaluation aligned to rubrics; annotate NLP tasks like named entity recognition and intent classification with consistent schema; support computer vision annotation including bounding boxes and segmentation masks when needed; apply content safety labeling policies for harmful, sensitive, or restricted content; write clear notes on ambiguous cases to improve annotation guidelines compliance; collaborate with engineering and ops partners to track training data quality metrics and model performance improvement outcomes.

Required Qualifications

US-based candidate available for remote, full-time work; STEM background (degree, bootcamp, or equivalent practical experience) with comfort in quantitative reasoning; ability to learn and follow detailed annotation guidelines compliance requirements; strong written communication to justify labels and RLHF choices; familiarity with spreadsheets, basic SQL, or Python for lightweight data checks; attention to detail for QA evaluation and consistency across large batches; reliability in meeting throughput and quality targets.

Preferred Qualifications

Experience with LLM evaluation, prompt evaluation, or RLHF workflows; exposure to NLP tasks such as named entity recognition and text classification; familiarity with computer vision annotation tools and concepts; understanding of content safety labeling and policy interpretation; experience measuring training data quality and performing inter-annotator agreement checks; comfort working with distributed teams across AI labs, tech startups, annotation vendors, or BPO environments.

How Success Is Measured

Training data quality scores (accuracy, consistency, completeness); QA evaluation pass rates and rework reduction; agreement and calibration performance on shared gold sets; turnaround time while maintaining annotation guidelines compliance; clear documentation of edge cases that improves rubrics and model performance improvement; reliable execution across NLP, computer vision annotation, and content safety labeling project requirements.

Why Rex.zone

Rex.zone is a platform where candidates explore remote, full-time roles supporting real AI/ML training workflows. You will gain hands-on exposure to LLM training pipelines, RLHF evaluation, QA evaluation, and data labeling systems used by modern technology teams, while building a foundation for long-term STEM and engineering growth.

How to Apply

Explore and apply via Rex.zone using a resume that highlights STEM coursework or projects, any Python/SQL experience, and examples of structured analysis. Include any exposure to data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, or content safety labeling.

Frequently Asked Questions

  • Q: Is this role remote and based in the United States?

    Yes. This role is Remote and Country is US.

  • Q: Is this a full-time position?

    Yes. Employment Type is FULL_TIME.

  • Q: What kind of work does an Entry Level STEM job involve here?

    The work centers on engineering-adjacent AI/ML support tasks such as data labeling, RLHF evaluation, QA evaluation, prompt evaluation, and documentation that improves training data quality in LLM training pipelines.

  • Q: Which domains might I work on?

    Common domains include NLP (e.g., named entity recognition), computer vision annotation, and content safety labeling, depending on project needs.

  • Q: What skills should I highlight to match the keyword intent?

    Highlight STEM fundamentals plus practical skills like Python, SQL, data labeling, RLHF, prompt evaluation, QA evaluation, annotation guidelines compliance, training data quality, and LLM evaluation.

  • Q: What is the salary range for this posting?

    Salary Range is USD 63360 to 126720 per YEAR.

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