[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-ai-prompt-engineer-jobs-in-united-states":3},{"Ques":4,"Slug":31,"Header":32,"job_category":60},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"They design prompts and guardrails, run prompt evaluation and QA evaluation, and iterate based on human feedback loops (often RLHF-adjacent) to improve model reliability, safety, and task success in production.","What does an AI prompt engineer do in a real LLM workflow?",{"A":11,"Q":12},"Yes. The roles on this page are marked Remote and located in the US, aligning with remote hiring and distributed LLM product teams.","Are these AI prompt engineer jobs in the United States remote?",{"A":14,"Q":15},"Prompt engineering, LLM evaluation, RLHF concepts, prompt evaluation, QA evaluation, NLP, content safety labeling, and LLM training pipelines are core, with tool use and RAG commonly required in production apps.","What skills should match the AI prompt engineer keyword intent?",{"A":17,"Q":18},"Prompt evaluation focuses on how prompt changes affect model behavior and outcomes, while QA evaluation checks output quality against defined standards, rubrics, and acceptance criteria to ensure consistency and compliance.","How is prompt evaluation different from QA evaluation?",{"A":20,"Q":21},"Often yes. Prompt engineers may define rubrics, labeling taxonomies, and annotation guidelines compliance so that human feedback data is consistent and useful for improving model behavior.","Do prompt engineers work with data labeling teams?",{"A":23,"Q":24},"NLP-heavy products, content safety programs, AI assistants with tool use, RAG-based knowledge systems, and multimodal applications that require careful evaluation and controlled output behavior.","What domains commonly hire prompt engineers?",{"A":26,"Q":27},"Yes. While this page focuses on full-time mid-senior hiring intent, the market commonly includes contract, freelance, entry-level, and senior roles depending on project scope and evaluation maturity.","Can entry-level or contract candidates find opportunities too?",{"A":29,"Q":30},"Include prompt libraries, before\u002Fafter examples, prompt test suites, evaluation rubrics, regression testing results, and any measurable model performance improvement from prompt iterations.","How should candidates present a portfolio for prompt engineering?","ai-prompt-engineer-jobs-in-united-states",{"desc":33,"title":34,"content":35},"AI prompt engineers design, test, and optimize prompts and evaluation workflows that improve large language model behavior in real production settings. On Rex.zone, these remote, full-time roles support LLM training pipelines through prompt engineering, RLHF-style human feedback, prompt evaluation, QA evaluation, and content safety labeling across NLP, tool use, and multimodal use cases. You will translate product requirements into prompt strategies, build prompt test suites, measure model performance improvement, and iterate with engineers and researchers to reduce hallucinations and increase instruction-following. Explore Rex.zone to find United States prompt engineer jobs spanning AI labs, tech startups, BPOs, and annotation vendors, including contract, freelance, entry-level, and senior pathways.","AI Prompt Engineer Jobs in the United States",[36,39,42,45,48,51,54,57],{"h2":37,"desc":38},"AI Prompt Engineer Jobs in the United States (Remote)","Title: AI Prompt Engineer Jobs in the United States\nDate: 25-02-2026\nCompany: Rexzone\nCountry: US\nRemote Type: Remote\nEmployment Type: FULL_TIME\nExperience Level: Mid-Senior\nIndustry: Technology\nJob Function: Engineering\nSkills: Prompt Engineering, LLM Evaluation, RLHF, Prompt Evaluation, QA Evaluation, LLM Training Pipelines, NLP, Content Safety Labeling, Tool Use, RAG\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":40,"desc":41},"About the Role","You will own prompt engineering and prompt evaluation workflows for LLM-powered features, focusing on reliability, safety, and measurable model performance improvement. This includes writing and versioning system prompts, building prompt libraries, crafting adversarial and edge-case test prompts, and running A\u002FB prompt experiments. You will partner with engineering to integrate prompts into applications (tool calling, function calling, RAG), and with data teams to define labeling taxonomies and QA evaluation rubrics aligned to annotation guidelines compliance. You will help operationalize human feedback loops similar to RLHF, including preference data collection, rubric-based scoring, and prompt regression testing to prevent quality drift.",{"h2":43,"desc":44},"Key Responsibilities","Design and iterate system prompts, developer prompts, and evaluation prompts for instruction-following, reasoning, and tool use.\nCreate prompt test suites with coverage for safety, policy, and content quality, including red-teaming style adversarial prompts.\nDefine evaluation criteria and scoring rubrics for prompt evaluation, QA evaluation, and human feedback collection.\nAnalyze model outputs for failure modes (hallucinations, refusal errors, policy gaps) and propose mitigations.\nCollaborate on RLHF-adjacent workflows: preference ranking, rubric scoring, and dataset iteration with labeling teams.\nWork with engineers to integrate prompts into production workflows (RAG grounding, retrieval prompts, tool schemas, guardrails).\nDocument prompt standards, prompt versioning practices, and prompt deployment checklists for repeatable operations.\nContribute to content safety labeling guidance and escalation procedures for sensitive content.",{"h2":46,"desc":47},"Required Qualifications","Experience designing prompts for LLM applications and evaluating output quality with structured rubrics.\nStrong understanding of NLP concepts, LLM behavior, and common failure modes.\nAbility to build practical evaluation frameworks (gold sets, acceptance tests, regression suites) for prompt-driven systems.\nComfort working with cross-functional partners across engineering, product, and AI\u002FML data operations.\nClear writing skills for prompt guidelines, annotation guidelines compliance, and QA documentation.",{"h2":49,"desc":50},"Preferred Qualifications","Hands-on experience with RLHF concepts, preference data, and evaluation datasets.\nExperience with RAG, embeddings, retrieval quality, and grounding strategies.\nFamiliarity with content safety labeling, policy taxonomies, and risk-based evaluation.\nExperience with multimodal prompting (text + image) or computer vision annotation workflows.\nAbility to use lightweight scripting or notebooks to analyze evaluation results and model outputs.",{"h2":52,"desc":53},"Workflows and Domains You May Support","NLP prompt engineering for summarization, extraction, classification, and dialogue.\nNamed entity recognition-style extraction via structured prompting and schema validation.\nPrompt evaluation and QA evaluation for customer support, sales enablement, and internal productivity copilots.\nLLM training pipelines with human feedback loops, rubric scoring, and prompt regression testing.\nContent safety labeling and policy-aligned refusals, including sensitive and regulated topics.\nMultimodal prompting and CV-adjacent tasks such as image captioning evaluation or visual QA.",{"h2":55,"desc":56},"Employment Types and Common Modifiers","This page covers remote, full-time roles, and also reflects common market modifiers: contract, freelance, entry-level, and senior prompt engineer opportunities. Employers may include AI labs, tech startups, BPOs, and annotation vendors, depending on the project and the LLM deployment stage.",{"h2":58,"desc":59},"How to Apply on Rex.zone","Create or update your Rex.zone profile, highlight prompt engineering portfolios, and include examples of prompt test suites, evaluation rubrics, and measurable improvements. Search for United States roles filtered by remote type, employment type, domain (NLP, content safety, tool use, RAG), and experience level, then apply directly through the Rex.zone job flow.","AI\u002FML Engineering"]