[{"data":1,"prerenderedAt":60},["ShallowReactive",2],{"job-ai-developer-jobs-in-united-states":3},{"Ques":4,"Slug":28,"Header":29,"job_category":59},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25],{"A":8,"Q":9},"An AI Developer builds AI features and services end to end: data pipelines, model integration, evaluation, deployment, and monitoring. The work often includes large language model evaluation, prompt evaluation, and feedback loops such as RLHF to improve model quality and safety.","What does an AI Developer do in these US remote roles?",{"A":11,"Q":12},"Yes. The roles on this page are explicitly marked Remote and FULL_TIME, aligned to the United States job market and hiring needs.","Are these AI developer jobs remote and full-time?",{"A":14,"Q":15},"RLHF experience is preferred but not required. Candidates should be comfortable with evaluation methods, human-in-the-loop workflows, and iterative model performance improvement.","Do I need RLHF experience to apply?",{"A":17,"Q":18},"Common domains include NLP, computer vision, content safety labeling, and LLM training pipelines, including retrieval-augmented generation, structured prompting, and model evaluation.","What domains do these AI Developer roles cover?",{"A":20,"Q":21},"AI developers frequently define labeling specs, collaborate with data operations, and consume labeled datasets for supervised learning and evaluation. Understanding training data quality, annotation guidelines compliance, and QA evaluation improves model outcomes.","How is data labeling related to AI developer work?",{"A":23,"Q":24},"The salary range is 63360 to 126720 USD per YEAR, as listed in the job metadata for this posting.","What is the listed salary range and pay period?",{"A":26,"Q":27},"You can explore and apply through Rex.zone, which serves as the navigational hub for this job posting and related AI\u002FML engineering opportunities.","Where do I apply for these AI developer jobs in the United States?","ai developer jobs in united states",{"desc":30,"title":31,"content":32},"AI developer jobs in the United States at Rex.zone focus on building production AI systems and LLM training workflows end to end, from data ingestion and feature pipelines to model training, RLHF, evaluation, and deployment. You will collaborate with product and ML teams to improve model performance, training data quality, and safety while implementing NLP, computer vision, and prompt evaluation solutions. These remote, full-time roles support real-world AI\u002FML training pipelines used by AI labs, tech startups, and enterprise teams, with clear engineering ownership across tooling, QA evaluation, and scalable inference. Explore and apply through Rex.zone to join high-impact AI engineering work in the US market.","AI Developer Jobs in the United States (Remote, Full Time)",[33,35,38,41,44,47,50,53,56],{"h2":31,"desc":34},"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 development, Python, machine learning, deep learning, LLMs, NLP, computer vision, RLHF, prompt evaluation, model evaluation, MLOps, data pipelines, APIs, cloud deployment | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR",{"h2":36,"desc":37},"About the Role","As an AI Developer, you will design, build, and ship AI\u002FML features that connect training data to model behavior in production. You will implement LLM application patterns (RAG, tool use, structured outputs), build evaluation harnesses, and collaborate on RLHF-style feedback loops to improve model performance and reliability. You will also partner with data operations to define labeling specs, QA evaluation standards, and annotation guidelines compliance for datasets used across NLP, computer vision, and content safety labeling.",{"h2":39,"desc":40},"What You Will Do","You will build and maintain AI services (batch and real-time), integrate model inference APIs, and optimize latency and cost. You will develop data pipelines for training and evaluation, including dataset versioning, data validation, and training data quality checks. You will create automated evaluation for large language model evaluation, prompt evaluation, and safety regressions, using offline metrics and human-in-the-loop review. You will support workflows such as named entity recognition, classification, summarization, ranking, and content moderation, and you will document experiments and promote reproducible ML engineering practices.",{"h2":42,"desc":43},"Required Qualifications","Mid-Senior experience building AI or ML-powered products in production environments. Strong Python and software engineering fundamentals (APIs, testing, code reviews, CI\u002FCD). Hands-on experience with machine learning and deep learning concepts, and practical exposure to LLMs, NLP, or computer vision. Ability to design evaluation plans and interpret results to drive model performance improvement. Comfort working with data labeling processes, QA evaluation, and clear annotation guidelines compliance.",{"h2":45,"desc":46},"Preferred Qualifications","Experience with RLHF, preference modeling, prompt iteration, or human feedback loops. Familiarity with MLOps tools for deployment, monitoring, and experiment tracking. Experience implementing retrieval-augmented generation, vector search, and embedding pipelines. Background in content safety labeling, policy-aligned classification, or red-teaming evaluation. Experience collaborating with annotation vendors or BPO teams and defining scalable labeling and quality workflows.",{"h2":48,"desc":49},"Tools and Tech You May Use","Python, ML frameworks, model inference servers, vector databases, evaluation frameworks, and cloud services for training and deployment. Data tooling for dataset curation, sampling, labeling operations, and QA. Observability and monitoring for model drift, performance regressions, and safety metrics. Experiment tracking and reproducibility tooling to support reliable iteration across LLM training pipelines.",{"h2":51,"desc":52},"Work Model, Employment, and Modifiers","Remote Type: Remote. Employment Type: FULL_TIME (primary). This page also supports common search modifiers such as contract, freelance, entry-level, and senior variations depending on team needs. Domain coverage may include NLP, computer vision, content safety, and large language model evaluation. Employer contexts include AI labs, tech startups, enterprises, BPOs, and annotation vendors connected through Rex.zone.",{"h2":54,"desc":55},"Compensation","Salary Range: 63360 to 126720 USD per YEAR, depending on experience, scope, and interview performance. Compensation may include additional benefits per Rexzone policy and client engagement terms where applicable.",{"h2":57,"desc":58},"How to Apply","Apply through Rex.zone with a resume highlighting AI engineering projects, shipped systems, evaluation results, and any experience with RLHF, data labeling workflows, or QA evaluation. Include links to repositories, case studies, or technical write-ups demonstrating production ML delivery and measurable impact.","Engineering",1786024776808]