AI Engineer Jobs in the United States

AI Engineer jobs in the United States on Rex.zone focus on building and improving production AI systems across LLM training pipelines, NLP, computer vision, and applied ML. You will develop model training and evaluation workflows, integrate RLHF signals, improve training data quality with data labeling and QA evaluation, and ship reliable inference services. These Remote, FULL_TIME roles support tech startups, AI labs, and platform teams that need strong engineering execution with measurable model performance improvement, safety, and compliance. Explore and apply on Rex.zone to find AI Engineer openings aligned to your stack, domain, and impact area.

AI Engineer — LinkedIn Job Metadata

About the Role

As an AI Engineer in the United States (Remote) via Rex.zone, you will design, build, and operate ML systems that move from experimentation to production. The work includes training and fine-tuning models (including LLMs), establishing evaluation harnesses, and using RLHF-style feedback loops where appropriate. You will collaborate with data operations on data labeling, prompt evaluation, QA evaluation, and content safety labeling to improve training data quality, reduce model regressions, and increase reliability. You will also build scalable inference services, monitoring, and CI/CD automation to ensure fast iteration and safe deployments.

What You Will Do

Develop and maintain end-to-end ML pipelines for training, evaluation, and deployment; implement model evaluation and offline/online metrics to track model performance improvement; integrate human feedback and RLHF signals into training workflows when applicable; partner with data teams on annotation guidelines compliance, QA sampling, and error analysis; build prompt evaluation and regression test suites for LLM features; support NLP and computer vision annotation use cases such as named entity recognition, text classification, image/video labeling, and content safety labeling; optimize inference latency, throughput, and cost for production services; implement monitoring, drift detection, and incident response runbooks for model behavior.

Required Qualifications

Mid-Senior experience shipping ML systems into production; strong Python engineering skills and familiarity with ML libraries; practical experience with model training, fine-tuning, and evaluation workflows; experience designing data schemas, feature pipelines, or training datasets; understanding of training data quality, labeling noise, and QA evaluation methods; familiarity with LLM training pipelines and prompt evaluation practices; ability to collaborate across engineering, product, and data operations teams in a remote environment.

Preferred Qualifications

Experience with RLHF, preference optimization, or human-in-the-loop evaluation; experience with NLP and named entity recognition or computer vision annotation workflows; experience with content safety labeling, policy enforcement, or safety evals; experience with MLOps tooling for experiment tracking, model registry, CI/CD, and monitoring; experience deploying models with scalable inference patterns and cost optimization; familiarity with A/B testing, canary releases, and rollout strategies for model changes.

Work Domains and Modifiers Covered

This page covers remote and full-time AI Engineer jobs in the United States, plus common modifiers and adjacent formats: contract, freelance, entry-level, and senior. Typical domains include NLP, computer vision, content safety, and LLM training. Common employer types include AI labs, tech startups, enterprise product teams, BPOs, and annotation vendors supporting model development and evaluation.

How to Apply on Rex.zone

Search Rex.zone for AI Engineer jobs in the United States and apply to roles that match your domain (NLP, CV, LLM), stack, and desired scope. Prepare a resume highlighting production ML impact, evaluation methodology, training data quality work, and measurable improvements to model reliability or performance.

Frequently Asked Questions

Q: What does an AI Engineer do in these roles?
AI Engineers build and run ML systems end to end: data pipelines, model training and fine-tuning, evaluation, deployment, and monitoring. Many roles also include LLM training pipelines, prompt evaluation, QA evaluation, and collaboration with data labeling teams to improve training data quality.
Q: Are these AI Engineer jobs remote in the United States?
Yes. The roles on this page are marked Remote and located in the US, aligned to the keyword intent of AI engineer jobs in the United States.
Q: Do AI Engineers work with RLHF and human feedback?
Often, yes. Depending on the team, you may integrate RLHF signals, preference data, and human-in-the-loop evaluation workflows to improve model behavior and reduce regressions.
Q: What skills are most important for AI Engineer jobs in the United States?
Common requirements include Python, machine learning, deep learning, MLOps, model evaluation, and experience with LLM training pipelines. Domain skills in NLP, computer vision, named entity recognition, prompt evaluation, and content safety labeling can be strong differentiators.
Q: What industries and employer types hire for these roles?
These roles are common in Technology organizations, including AI labs, tech startups, enterprise product teams, and vendors that provide data labeling and evaluation services for model training.
Q: How should I tailor my application for Rex.zone listings?
Emphasize shipped production ML systems, evaluation rigor, monitoring and reliability practices, and examples of model performance improvement driven by better datasets, QA evaluation, or human feedback loops.

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