AI Product Manager Jobs in the United States

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/B 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.

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AI Product Manager Jobs in the United States (Remote, Full-Time) — Rex.zone

Title: AI Product Manager Jobs in the United States 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 product management, LLM product strategy, NLP, computer vision, ML lifecycle, model evaluation, A/B testing, data labeling strategy, RLHF, prompt evaluation, MLOps, analytics, stakeholder management Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

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.

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/B 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.

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.

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/eval splits, precision/recall, calibration, ranking metrics), experience collaborating with engineering and data science, and strong stakeholder management across business, legal, security, and operations.

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.

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.

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.

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.

Frequently Asked Questions

  • Q: What does an AI Product Manager do in AI/ML workflows?

    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.

  • Q: Are these AI Product Manager jobs in the United States remote?

    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.

  • Q: What AI domains are most common for this role?

    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.

  • Q: Do AI Product Managers work with RLHF?

    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.

  • Q: What skills should I highlight when applying on Rex.zone?

    Highlight AI product management, LLM product strategy, ML lifecycle knowledge, model evaluation, A/B testing, data labeling strategy, stakeholder management, analytics, and experience partnering with engineering, data science, and data operations.

  • Q: Is this role suitable for entry-level candidates?

    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.

  • Q: What employer types hire AI Product Managers through Rex.zone?

    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.

  • Q: How is success measured for an AI Product Manager?

    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.

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