STEM Research Jobs in the United States

STEM Research Jobs in the United States at Rex.zone focus on applied and experimental research workflows that turn hypotheses into measurable results across AI/ML, NLP, computer vision, and data-driven engineering. In this remote, full-time role, you will design experiments, build prototypes, evaluate model and system performance, and document findings for product and research stakeholders. You will work with training data quality, evaluation metrics, and reproducible pipelines that support large language model evaluation, prompt evaluation, and safe deployment. Explore Rex.zone to find and apply to STEM research openings aligned to your domain expertise and career goals.

Job Overview — STEM Research Jobs in the United States

About Rex.zone

Rex.zone is a platform for discovering and applying to remote and on-site roles, including full-time, contract, and freelance work across AI/ML, engineering, and data operations. Our postings reflect real-world workflows such as experiment tracking, model evaluation, training data quality reviews, and production readiness checks that support modern research and product teams.

What You Will Do

You will plan and execute STEM research projects, translate problem statements into testable hypotheses, and run experiments using reproducible methods. You will develop and evaluate models and systems using standard metrics and error analysis, including LLM evaluation and prompt evaluation when relevant. You will collaborate with engineering and data teams on data labeling strategy, annotation guidelines compliance, and QA evaluation to improve training data quality and model performance improvement. You will write clear technical documentation, research summaries, and recommendations for stakeholders. You will help define experimental baselines, implement ablation studies, and maintain experiment logs to ensure repeatability and auditability.

Key Responsibilities

You will design experimental protocols, sampling strategies, and evaluation plans. You will implement prototypes in Python and validate results with statistical analysis. You will conduct error analysis, bias checks, and content safety labeling reviews when working with user-facing models. You will coordinate with cross-functional partners on data labeling operations, including named entity recognition and computer vision annotation tasks where applicable. You will maintain versioned datasets, evaluation sets, and model artifacts to support reliable comparisons over time. You will communicate findings, risks, and next steps through concise reports and technical presentations.

Required Qualifications

Mid-Senior experience delivering research or applied R&D outcomes in a STEM domain. Strong experimental design and statistical reasoning, including confidence intervals, hypothesis testing, and power considerations. Proficiency in Python and common data science tooling. Practical experience with machine learning evaluation, dataset curation, and training data quality. Ability to write clear documentation and collaborate in distributed, remote teams.

Preferred Qualifications

Experience with LLM training pipelines, RLHF (Reinforcement Learning from Human Feedback), or large language model evaluation. Familiarity with annotation guidelines compliance, QA evaluation workflows, and vendor-managed labeling programs. Experience with NLP tasks such as named entity recognition and prompt evaluation, or CV tasks such as bounding boxes and segmentation. Exposure to MLOps practices for reproducible research, experiment tracking, and model deployment validation.

Tools and Workflows You May Use

Python notebooks and pipelines, experiment tracking, reproducible research workflows, dataset versioning, and evaluation harnesses. Where relevant, labeling tools and QA systems for data labeling, content safety labeling, and rubric-based evaluation. Collaboration tools for remote execution, peer review, and documentation.

How to Apply on Rex.zone

Search Rex.zone for STEM Research Jobs in the United States, filter by Remote and FULL_TIME, and submit your application with a resume highlighting research outcomes, experiment design, and evaluation impact. Include examples of publications, internal reports, shipped prototypes, or measurable improvements such as reduced error rates, improved evaluation scores, or faster research iteration cycles.

Frequently Asked Questions

Q: What does “STEM Research Jobs in the United States” mean on Rex.zone?
It refers to research-focused roles in science, technology, engineering, and math across US-aligned employers and teams, including remote positions. The work typically involves experimental design, data-driven evaluation, prototyping, and communicating findings to stakeholders.
Q: Is this role remote?
Yes. Remote Type is Remote, and the role is designed for distributed collaboration with asynchronous documentation and scheduled research reviews.
Q: Is this a full-time job?
Yes. Employment Type is FULL_TIME, with expectations for sustained ownership of research initiatives and end-to-end delivery of findings.
Q: What experience level is targeted?
Mid-Senior. You should be able to independently scope experiments, run evaluations, and deliver research outputs with minimal oversight.
Q: What kinds of research tasks are common in this role?
Common tasks include hypothesis-driven experimentation, statistical analysis, model and system evaluation, error analysis, dataset curation, and documentation. In AI/ML contexts, it may include LLM evaluation, prompt evaluation, RLHF-adjacent workflows, and training data quality reviews.
Q: Do I need AI/ML experience to qualify?
Not always, but it is often helpful for Technology and Engineering postings. Experience with machine learning evaluation, reproducible pipelines, and data-centric workflows strengthens alignment with many Rex.zone research listings.
Q: What is the salary range for this posting?
Salary Min is 63360 USD and Salary Max is 126720 USD, paid per YEAR. Final compensation may vary by scope, qualifications, and market factors.
Q: What skills should I highlight to match this job intent?
Highlight STEM research, experimental design, statistical analysis, Python, machine learning evaluation, NLP or computer vision exposure, training data quality, data labeling and QA evaluation familiarity, and clear technical writing.

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