[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-ai-jobs-united-states":3},{"Ques":4,"Slug":25,"Header":26,"job_category":85},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"Yes. This page targets remote AI jobs in the United States with Country set to US and Remote Type set to Remote.","Are these remote AI jobs based in the United States only?",{"A":11,"Q":12},"You will focus on LLM training pipelines, RLHF workflows, model evaluation, prompt evaluation, training data quality, and QA evaluation, often connected to data labeling programs and safety datasets.","What kind of AI work is most common in this role?",{"A":14,"Q":15},"This posting is for FULL_TIME employment. Separate contract or freelance roles may exist elsewhere on Rex.zone.","Is this a full-time or contract position?",{"A":17,"Q":18},"Direct experience helps, but strong evaluation engineering, ML pipeline development, and familiarity with annotation guidelines compliance and training data quality are also highly relevant.","Do I need experience with data labeling to qualify?",{"A":20,"Q":21},"The role supports NLP and computer vision use cases, including named entity recognition, computer vision annotation, content safety labeling, and large language model evaluation.","What domains does the job support?",{"A":23,"Q":24},"The salary range is 63360 to 126720 USD per year, with Pay Period set to YEAR.","What is the salary range for this role?","remote-ai-jobs-united-states",{"desc":27,"title":28,"content":29},"Remote AI jobs in the United States at Rex.zone cover production ML engineering work across LLM training pipelines, RLHF, evaluation, data labeling workflows, and quality assurance for model performance improvement. You will collaborate with cross-functional AI teams to build, test, and monitor NLP and computer vision systems, improve training data quality, and operationalize safety and compliance signals. These full-time remote roles support real-world AI\u002FML delivery for AI labs, tech startups, and enterprise teams, with clear expectations, measurable outcomes, and modern tooling for distributed execution.","Remote AI Jobs in the United States",[30,32,35,46,55,63,72,79],{"h2":28,"desc":31},"Title: Remote AI Engineer (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: LLM training pipelines, RLHF, prompt evaluation, model evaluation, data labeling, training data quality, QA evaluation, NLP, computer vision, content safety labeling, named entity recognition, MLOps, Python\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":33,"desc":34},"About the Role","You will design and ship components that improve model quality and reliability for LLM and multimodal systems. This includes building evaluation harnesses, defining annotation guidelines compliance checks, integrating human feedback signals (RLHF), and partnering with data operations to raise training data quality. You will work across NLP and computer vision use cases such as named entity recognition, content safety labeling, prompt evaluation, and LLM response grading. Success is measured by model performance improvement, stable deployments, and clear metrics across offline evaluation and online monitoring.",{"h2":36,"desc":37},"Key Responsibilities",[38,39,40,41,42,43,44,45],"Build and maintain evaluation pipelines for large language model evaluation, including rubric-based grading and pairwise preference tests.","Integrate RLHF signals and feedback loops to support model performance improvement and safer outputs.","Define, test, and iterate on annotation guidelines compliance and quality gates with data labeling teams.","Develop prompt evaluation and regression suites to detect quality drops across releases.","Partner with stakeholders on NLP tasks (e.g., named entity recognition) and CV tasks (e.g., computer vision annotation) that support product requirements.","Implement content safety labeling strategies, policy taxonomies, and audit-friendly reporting.","Create dashboards and monitoring for training data quality, evaluation metrics, and production health.","Improve tooling and automation for QA evaluation, labeling workflows, and review operations.",{"h2":47,"desc":48},"Required Qualifications",[49,50,51,52,53,54],"Mid-Senior experience shipping AI\u002FML systems in production environments.","Strong Python skills and ability to build reliable data and evaluation pipelines.","Hands-on experience with model evaluation, prompt evaluation, and experiment design.","Understanding of RLHF concepts, human feedback collection, and preference modeling workflows.","Ability to translate ambiguous quality problems into measurable metrics and actionable engineering tasks.","Comfort working with cross-functional partners across product, data operations, and QA.",{"h2":56,"desc":57},"Preferred Qualifications",[58,59,60,61,62],"Experience with LLM training pipelines, dataset curation, and training data quality measurement.","Exposure to annotation systems, data labeling programs, and reviewer calibration processes.","Experience with NLP and\u002For computer vision annotation, including named entity recognition schemas.","Knowledge of content safety labeling, policy enforcement, and risk mitigation for generative AI.","Familiarity with MLOps practices for deployment, monitoring, and reproducible experimentation.",{"h2":64,"desc":65},"What You Will Work On",[66,67,68,69,70,71],"Large language model evaluation and continuous quality measurement","RLHF feedback loops and preference-based training support","Prompt evaluation, rubric design, and QA evaluation workflows","Training data quality improvements and annotation guidelines compliance","NLP and computer vision data pipelines, including named entity recognition and computer vision annotation","Content safety labeling and safety-focused evaluation datasets",{"h2":73,"desc":74},"Remote Work and Location",[75,76,77,78],"Country: US","Remote Type: Remote","Employment Type: FULL_TIME","This is a fully remote role aligned to United States hiring and payroll requirements.",{"h2":80,"desc":81},"How to Apply",[82,83,84],"Apply through Rex.zone and select the Remote AI Jobs in the United States listing.","Include a resume highlighting evaluation pipelines, RLHF, data labeling or QA evaluation, and production ML engineering experience.","If applicable, provide links to relevant projects, publications, or shipped systems.","Engineering"]