AI Research Jobs in the United States (Remote, Full-Time)

AI research jobs in the United States focus on building and evaluating machine learning systems end-to-end, from dataset creation and labeling to RLHF, prompt evaluation, and large language model (LLM) training pipelines. On Rex.zone, you will collaborate with AI labs, tech startups, and annotation vendors to improve model performance through rigorous experimentation, training data quality, and evaluation methodology. This role connects applied research with production workflows, including QA evaluation, content safety labeling, and cross-domain tasks in NLP and computer vision.

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Job Heading: AI Research Scientist (United States, Remote)

Title: AI Research Scientist (United States, Remote) 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: AI research, machine learning, deep learning, LLM training pipelines, RLHF, prompt evaluation, model evaluation, experiment design, Python, PyTorch, NLP, computer vision Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

About the Role

You will run applied AI research projects for US-based customers via Rex.zone, translating open-ended research questions into measurable experiments. Your work will span LLM evaluation, RLHF data design, prompt evaluation, and model performance improvement. You will define training data requirements, write annotation guidelines compliance checks, and partner with data operations to deliver high-quality datasets. You will also design QA evaluation strategies, diagnose failure modes, and iterate on model training pipelines to improve robustness, safety, and helpfulness across NLP and computer vision tasks.

What You Will Do

Own end-to-end applied research cycles: problem framing, baselines, ablations, and reporting. Build and evaluate LLM systems using offline metrics and human-in-the-loop evaluation. Design RLHF workflows: preference data specs, rater instructions, prompt sets, and rubric-based grading. Create evaluation datasets and test suites: prompt evaluation, red-teaming prompts, and content safety labeling protocols. Collaborate with data labeling teams on data taxonomy, edge-case coverage, and training data quality. Perform error analysis and model debugging to drive model performance improvement. Document methodology and results for reproducibility and auditability.

Required Qualifications

Mid-Senior experience delivering applied ML research or productionized ML evaluation. Strong Python skills and experience with PyTorch or similar frameworks. Hands-on experience with LLM evaluation, prompt evaluation, or RLHF (preference modeling, reward modeling, or instruction tuning). Ability to design experiments, choose metrics, and interpret results with statistical rigor. Familiarity with dataset development: data labeling, QA evaluation, and annotation guidelines compliance. Strong written communication for research artifacts and cross-functional alignment.

Preferred Qualifications

Experience with named entity recognition, retrieval-augmented generation evaluation, or structured output evaluation. Exposure to computer vision annotation, multimodal evaluation, or OCR/diagram understanding. Experience building content safety labeling taxonomies and policy-aligned evaluation rubrics. Familiarity with MLOps practices for evaluation pipelines, dataset versioning, and reproducible runs. Publication record or demonstrable research impact in NLP, CV, or scalable evaluation.

Remote Work and Collaboration

This is a Remote, FULL_TIME role supporting United States-based projects. You will collaborate asynchronously with distributed research, engineering, and data operations teams. You may work with AI labs, tech startups, BPOs, and annotation vendors through Rex.zone while maintaining clear documentation and evaluation traceability.

How to Apply on Rex.zone

Apply through Rex.zone by submitting your resume and a short summary of your experience with AI research, LLM evaluation, RLHF, and dataset development. Include examples of experiment design, evaluation methodology, or training data quality work (papers, reports, repos, or dashboards).

Frequently Asked Questions

  • Q: What do AI research jobs in the United States typically involve?

    They typically involve framing research problems, running experiments, building evaluation datasets, and improving model performance for ML systems. For LLM-focused roles, this often includes RLHF, prompt evaluation, QA evaluation, and training data quality work.

  • Q: Is this role remote and full-time?

    Yes. The role is marked Remote and FULL_TIME and is aligned to United States-based hiring and projects.

  • Q: What domains does this job cover?

    The core focus is AI/ML research with common domains including NLP, large language model evaluation, content safety labeling, and optional computer vision evaluation depending on project needs.

  • Q: How does RLHF relate to this role?

    RLHF is used to improve LLM behavior using human feedback. You may design preference data, write rater rubrics, validate annotation guidelines compliance, and analyze reward model or policy model outcomes.

  • Q: Do I need prior data labeling experience?

    You do not need to be a full-time annotator, but you should understand data labeling workflows, QA evaluation, and how annotation quality impacts model training pipelines and evaluation validity.

  • Q: What types of employers hire through Rex.zone for AI research?

    Projects may come from AI labs, tech startups, and established technology teams, as well as annotation vendors and BPOs supporting ML training pipelines.

  • Q: What is the salary range and pay period?

    The salary range is 63360 to 126720 USD per YEAR.

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