[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-ai-product-manager-jobs-united-states":3},{"Ques":4,"Slug":31,"Header":32,"job_category":63},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"An AI Product Manager defines the AI product roadmap, translates user needs into ML requirements, aligns data strategy (data labeling and training data quality), and owns evaluation and iteration loops such as offline metrics, human evaluation, prompt evaluation, and responsible AI guardrails through production.","What does an AI Product Manager do in AI\u002FML workflows?",{"A":11,"Q":12},"Yes. This posting is explicitly marked Remote and based in the US, with full-time employment expectations and cross-functional collaboration across engineering, data science, MLOps, and QA.","Are these AI Product Manager jobs in the United States remote?",{"A":14,"Q":15},"Common domains include LLM applications, NLP, computer vision, content safety, and model evaluation programs that rely on human-in-the-loop review, data labeling operations, and continuous model performance improvement.","What AI domains are most common for this role?",{"A":17,"Q":18},"They often do. AI PMs may define RLHF-style feedback programs, set annotation guidelines, choose evaluation criteria, coordinate human review operations, and measure improvements in model quality and user outcomes.","Do AI Product Managers work with RLHF?",{"A":20,"Q":21},"Highlight AI product management, LLM product strategy, ML lifecycle knowledge, model evaluation, A\u002FB testing, data labeling strategy, stakeholder management, analytics, and experience partnering with engineering, data science, and data operations.","What skills should I highlight when applying on Rex.zone?",{"A":23,"Q":24},"This posting targets Mid-Senior experience. However, Rex.zone may also list entry-level, contract, freelance, and senior variants depending on employer needs and the scope of ownership.","Is this role suitable for entry-level candidates?",{"A":26,"Q":27},"Typical employers include technology companies, AI labs, tech startups, enterprises modernizing with ML, and platforms coordinating annotation vendors and BPOs for data labeling and evaluation.","What employer types hire AI Product Managers through Rex.zone?",{"A":29,"Q":30},"Success is measured through product and model outcomes such as adoption, retention, latency, reliability, quality metrics (accuracy, relevance, safety), reduction in evaluation failure rates, improved training data quality, and delivery against roadmap milestones.","How is success measured for an AI Product Manager?","ai-product-manager-jobs-united-states",{"desc":33,"title":34,"content":35},"AI Product Manager jobs in the United States focus on defining, building, and scaling AI-powered products across LLM applications, NLP, computer vision, and model-driven platforms. On Rex.zone, you will lead product discovery, translate business needs into ML requirements, and drive end-to-end delivery—from data strategy and annotation guidelines to offline evaluation, A\u002FB testing, and responsible AI practices. This role partners with engineering, data science, MLOps, design, and QA to improve model performance, training data quality, and user outcomes while managing roadmaps, stakeholder alignment, and measurable impact in production systems.","AI Product Manager Jobs in the United States",[36,39,42,45,48,51,54,57,60],{"h2":37,"desc":38},"AI Product Manager Jobs in the United States (Remote, Full-Time) — Rex.zone","Title: AI Product Manager 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: AI product management, LLM product strategy, NLP, computer vision, ML lifecycle, model evaluation, A\u002FB testing, data labeling strategy, RLHF, prompt evaluation, MLOps, analytics, stakeholder management\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":40,"desc":41},"About the Role","You will own AI product outcomes for US-based customers while working remotely. You will define product vision and roadmap for AI features, align cross-functional teams, and translate ambiguous problems into requirements that engineering and data science can execute. You will shape data strategy (training data quality, taxonomy design, labeling operations), define evaluation plans (offline metrics, human evaluation, prompt evaluation, RLHF workflows), and drive iteration from prototype to production with strong MLOps and monitoring practices.",{"h2":43,"desc":44},"What You Will Do","You will: (1) Define AI product requirements (PRDs), success metrics, and milestones for LLM- and ML-powered features, (2) Partner with ML engineers and data scientists on model selection, fine-tuning plans, and evaluation design, (3) Coordinate data collection and data labeling strategy, including annotation guidelines compliance and QA evaluation processes, (4) Lead experimentation with A\u002FB testing, guardrails, and model performance improvement loops, (5) Collaborate with content safety and responsible AI stakeholders on policy, risk assessment, and mitigations, (6) Drive go-to-market readiness with clear documentation, training, and stakeholder alignment.",{"h2":46,"desc":47},"Key Workflows You Will Own","Core workflows include: problem framing and user research; dataset planning and training data quality reviews; labeling operations with vendors or internal teams; evaluation design including human-in-the-loop review, prompt evaluation, and RLHF-style feedback loops; production readiness with MLOps, monitoring, and incident response; continuous improvement through analytics, error analysis, and roadmap iteration.",{"h2":49,"desc":50},"Required Qualifications","Mid-Senior experience shipping software products with measurable outcomes, strong product sense and execution, ability to write clear PRDs and acceptance criteria, comfort working with ML concepts (features, training\u002Feval splits, precision\u002Frecall, calibration, ranking metrics), experience collaborating with engineering and data science, and strong stakeholder management across business, legal, security, and operations.",{"h2":52,"desc":53},"Preferred Qualifications","Experience building LLM applications (RAG, agents, tool use), familiarity with RLHF and human evaluation programs, experience with NLP or computer vision product delivery, hands-on work with annotation vendors or BPOs, experience designing content safety labeling and policy workflows, and familiarity with MLOps and production monitoring for drift, latency, and quality regressions.",{"h2":55,"desc":56},"Who You Will Work With","You will partner with ML engineering, data science, platform engineering, QA, design, analytics, and data operations teams. You may also coordinate with AI labs, tech startups, and annotation vendors to scale data labeling, evaluation, and model iteration cycles.",{"h2":58,"desc":59},"Remote Work and Location (United States)","This is a Remote, Full-Time role based in the US. You will collaborate across time zones using documented processes, weekly planning, and clear metric reviews, while maintaining reliable handoffs to engineering and data operations.",{"h2":61,"desc":62},"How to Apply on Rex.zone","Apply through Rex.zone with your resume and a brief summary of AI products you have shipped, including metrics, evaluation approach, and how you partnered with engineering and data teams. Include examples of roadmap ownership, experimentation, and production launch responsibilities.","AI Product Management"]