Interested in this AI Product Manager role at Eliza?
Apply Now →Skills & Technologies
About This Role
About Us
------------
We are a technology services company dedicated to helping organizations build and deploy cutting\-edge AI solutions. From generative AI and custom LLM integrations to predictive analytics and intelligent automation, we work across industries to bring real\-world AI applications to life. Our projects combine deep technical expertise with hands\-on client collaboration to solve high\-impact problems.
Job Summary
---------------
The Associate AI Product Manager is the execution engine of the AI PM function. While the AI PM owns scoping, strategic direction, and executive relationships on an account, the Associate owns the tactical delivery that makes those engagements run: trainings delivered, skills built, workshops executed, backlogs maintained, and progress made visible. This is a hands\-on role for someone with strong AI fluency who wants to learn the full AI PM craft by delivering inside real engagements from week one.
Eliza AI PMs are expected to deliver both of our core engagement types, and Associates support delivery in both:
- Forge (production AI system delivery): The Associate supports the AI PM and forward\-deployed engineers—maintaining the use case backlog, documenting requirements, running eval execution, and keeping delivery unblocked.
- Fusion (workforce enablement): The Associate is hands\-on\-keyboard in delivery—running trainings, building and refining skills with client champions, executing persona workshops, and tracking adoption.
Associates are staffed across both lanes based on account needs and development goals, with coaching from the AI PMs they support.
Key Responsibilities
------------------------
1\. Engagement Delivery (Fusion)
------------------------------------
- Deliver one\-to\-many trainings and hands\-on, persona\-specific workshops for client teams, from analysts to executives.
- Build, test, and refine custom skills, prompts, and workflows with client champions; turn what works into repeatable curriculum.
- Conduct persona and use\-case interviews with client stakeholders to surface high\-value workflows for workshops and skills.
- Track adoption signals throughout the engagement: usage depth, use\-case realization, and time savings—and flag what the data says.
2\. Delivery Support (Forge)
--------------------------------
- Maintain the scored use case backlog: keep feasibility, data readiness, and priority current as discovery evolves.
- Document requirements from client working sessions into structured inputs the AI PM can shape into specs.
- Execute evals: run test sets, log failure modes, and summarize results so quality calls can be made quickly.
- Run down blockers—access, data, calendars, approvals—so FDEs and the AI PM stay focused on the build.
3\. Account Operations
--------------------------
- Prepare executive readout materials: progress, metrics, risks, and next steps—short, honest, and outcome\-oriented.
- Keep engagement artifacts current: roadmaps, status trackers, meeting notes, and action items across concurrent workstreams.
- Coordinate scheduling and logistics across client stakeholders, FDEs, and Eliza leadership.
4\. AI Center of Excellence
-------------------------------
- Contribute to Eliza's reusable delivery assets: training curricula, skill libraries, workshop templates, and playbooks.
- Track new model and product releases relevant to active engagements and surface what matters to the team.
How We'll Know It's Working
-------------------------------
- Trainings and workshops run on schedule and clients ask for more of them.
- Skills built with client champions get used after the workshop ends—adoption data proves it.
- Backlogs, trackers, and readouts are current without being chased.
- The AI PMs supported by this role spend measurably more time on scoping and strategy—and say so.
- Within 6–12 months, this person is credibly stepping into AI PM scope on smaller engagements.
Qualifications
------------------
Required
------------
- 1–3 years in consulting, customer success, enablement/L\&D, or product—client\-facing delivery experience required.
- Strong AI fluency: daily working use of frontier tools (ChatGPT, Claude, or similar), a working mental model of what current models can and cannot do, and the habit of updating it as the field moves.
- Facilitation and presentation skills—comfortable teaching a room of skeptical analysts or walking an executive through a demo.
- Highly organized under load: able to run parallel workstreams across multiple accounts without dropping threads.
- Strong written communication—clear meeting notes, clean readouts, no jargon, no overselling.
- Comfortable in ambiguity; takes ownership of loosely defined work and asks sharp questions early.
Preferred
-------------
- Hands\-on experience building prompts, custom GPTs/skills, or lightweight automations—not just using AI, but shaping it.
- Experience designing training content or adult learning programs.
- Exposure to enterprise software delivery: sprints, backlogs, requirements documentation, or QA/eval work.
- Familiarity with a functional domain Eliza serves (finance/office of the CFO, private equity operations, healthcare).
What We Offer
-----------------
- Competitive compensation (base salary \+ performance incentives tied to client outcomes).
- Equity options in a growing AI services company.
- Exposure to a wide range of industries and high\-impact AI problems.
- Travel opportunities for on\-site client engagements (if desired).
- A collaborative, mission\-driven team passionate about the real\-world impact of AI.
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Eliza, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $120,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Eliza AI Hiring
Eliza has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.