STEM Degree Jobs in the United States

STEM degree jobs in the United States span software engineering, AI/ML engineering, data engineering, and applied research that power real production systems. At Rex.zone (Rexzone), you will work remotely on building and improving AI training pipelines using RLHF, data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling. These roles translate STEM fundamentals into measurable model performance improvement through training data quality, annotation guidelines compliance, and large language model evaluation. Explore full-time remote roles designed for mid-senior STEM professionals supporting AI labs, tech startups, and annotation vendors across NLP, CV, and LLM workflows.

Remote AI/ML Engineering Role (STEM Degree Jobs in the United States)

What You Will Do

Design and ship ML components for LLM training pipelines; partner with data operations to define annotation guidelines; build and iterate RLHF workflows (ranking, preference data, critique signals); run prompt evaluation and model evaluation to diagnose failure modes; implement QA evaluation checks for annotation guidelines compliance; coordinate NLP tasks (named entity recognition, classification) and CV annotation tasks (bounding boxes, segmentation); contribute to content safety labeling policies and sampling strategies; track model performance improvement using offline metrics and error analysis.

Who This Is For

Mid-senior STEM degree holders in the United States who want remote, full-time engineering work on applied AI/ML systems. Ideal backgrounds include computer science, engineering, mathematics, statistics, physics, or related fields, with experience moving from experimentation to production and collaborating across research, product, and data labeling teams.

Required Qualifications

STEM degree (BS/MS/PhD or equivalent experience); strong Python and software engineering fundamentals; hands-on machine learning experience across NLP and/or computer vision; familiarity with LLM evaluation, prompt evaluation, or RLHF concepts; understanding of training data quality, sampling, and dataset versioning; ability to write clear specs for labeling instructions and QA evaluation; experience analyzing model errors and communicating findings.

Preferred Qualifications

Experience with named entity recognition and information extraction; experience building evaluation harnesses and automated QA; knowledge of content safety labeling, policy taxonomies, and edge-case handling; familiarity with annotation vendor operations, inter-annotator agreement, and dispute resolution; experience deploying ML services and monitoring performance regressions.

Work Setup

Remote Type: Remote (US). Employment Type: FULL_TIME. You will collaborate asynchronously with distributed teams and may interface with AI labs, tech startups, BPO partners, and specialized annotation vendors depending on project needs.

Compensation

Salary Currency: USD. Salary Min: 63360. Salary Max: 126720. Pay Period: YEAR.

How to Apply on Rex.zone

Apply through Rex.zone by submitting your resume and a short summary of relevant projects (LLM evaluation, RLHF, data labeling programs, QA evaluation frameworks, NLP/CV pipelines). Highlight measurable outcomes tied to training data quality, annotation guidelines compliance, and model performance improvement.

Frequently Asked Questions

Q: What are STEM degree jobs in the United States on Rex.zone?
They are remote, full-time engineering roles where STEM graduates apply math, computing, and engineering skills to real AI/ML systems, including LLM training pipelines, RLHF, data labeling programs, and model evaluation/QA evaluation.
Q: Is this role fully remote?
Yes. Remote Type is Remote, and the job is based in the US.
Q: What does RLHF mean in day-to-day work?
RLHF work typically includes defining preference data collection, building ranking or critique workflows, running prompt evaluation, validating annotation guidelines compliance, and measuring model performance improvement from curated training data.
Q: Do I need prior data labeling experience?
Not always, but you should understand training data quality and be able to design labeling instructions, QA evaluation processes, and feedback loops that improve dataset reliability for NLP, CV, and content safety labeling.
Q: What types of domains are supported (NLP, CV, content safety)?
Projects may include NLP tasks like named entity recognition, CV annotation such as bounding boxes/segmentation, and content safety labeling for policy compliance, depending on the pipeline needs.
Q: What employment type and seniority is this posting?
Employment Type is FULL_TIME and Experience Level is Mid-Senior.
Q: What salary range is offered?
The annual salary range is 63360 to 126720 USD, paid YEAR.
Q: How does Rex.zone help with applying?
Rex.zone centralizes role details and application flow so candidates can match their STEM background to engineering work in AI/ML training pipelines, evaluation, and data operations.

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