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About This Role
Overview
Rightsline is building the AI-native platform for IP commerce. We are moving from a modern system of record into an agentic product model. AI Assistants are in the market today, our first line of Agents coming next, and a longer arc that reshapes how our customers do the work of rights, royalties, and finance flows. Underneath the AI product line sits a modern analytics and reporting platform (Snowflake plus Sigma) that every product consumes.
We are hiring a Group Product Manager, AI & Data to lead the execution of that arc. You will personally own and build our AI product line as the working PM – PRDs, prototypes, discovery, and launches shipping on your own hands. Alongside that, you will directly manage Senior Product Manager(s) / Product Manager(s) and grow the AI & Data team as the Group's scope expands. You will set the craft bar for how we discover, build, evaluate, launch, and monetize AI products at Rightsline; make the build-vs-leverage-vs-partner calls across proprietary and third-party AI; and be the person the CPO and Executive Leadership Team look to for whether our AI bets are compounding into shipped customer value.
This is a player-coach product management role with a defined growth path as the Group scales and performs above expectations. The right candidate reads that path as motivating and the builder-first framing as freeing.
What You’ll Own
Build the AI Product Line – Hands-On
- Personally own our AI product line as the working PM. You are the one running discovery, writing the PRDs, building the prototypes, and shipping new AI products into production.
- Reimagine workflows in the world of AI: customer-back and future-first. We are not extrapolating today's product with AI sprinkled on. Fundamentally change how the work gets done, then design the surface.
- Solve pain first. Every AI capability starts from a real, named customer job not from what a model can do. If we cannot describe the pain in a customer's own words, we do not build it.
- Best-in-class, modern user experience. The AI surface is where our product feels different. Hold a top-tier UX bar with confidence indicators, human-in-the-loop patterns, exception handling, latency, and trust design as product properties.
- Own the MCP integration surface both how our AI products consume model and tool contexts, and how Rightsline exposes its own capabilities as MCP services for external agents over time.
- Own the AI product vision and multi-quarter roadmap for the Group. Translate the CPO's portfolio strategy into a sequenced, defensible plan and a board-approved agentic product vision.
- Establish evals and telemetry / LLMOps as foundational not optional. Own the accuracy / trust / usability / cost tradeoff and be accountable when we get it wrong.
- Guard the naming bar: functional names, no personas or marketing wrappers.
Cohesive AI Strategy – Proprietary + Third Party Partners
- Own a coherent AI strategy across proprietary Rightsline capabilities and best-in-class third-party AI. The customer sees one product, one experience, one trust model not a patchwork of vendors.
- Make explicit build-vs-leverage-vs-partner calls for every AI capability, and maintain an explicit "will not do" list.
- Publish and maintain the Group's thesis on where AI creates compounding moats for Rightsline (proprietary data, workflow context, domain models).
Customer Discovery, JTBD & Design Partners
- Run discovery on a Jobs to Be Done frame: map the user's full end-to-end role, not just the 30% that lives inside our product today. The 70% outside our product is the expansion opportunity.
- Structured customer validation before scaling.
- Bring customers along as design partners. Early Access Programs and co-design cadences are how AI capabilities graduate from prototype to GA not internal opinion.
Pricing, Packaging & Monetization
- Own pricing, packaging, and monetization strategy for AI at Rightsline. Reason clearly across the modern set of AI monetization models like bundled features, add-on copilots, standalone AI products, and platform-plus-usage and match the model to the product shape.
- Test pricing like a product launch. Beta cohorts, EAPs, preferential pricing during the pilot, then lock the model. Output-based, credit-based, and hybrid subscription-plus-usage are all on the table depending on the product shape.
- PLG motion where it makes sense. Some AI surfaces may open a product-led entry point into Rightsline. You own identifying and running those bets when the shape fits.
Success Metrics & Financial Attribution
- Own the AI product scorecard end-to-end including active adoption across the customer base, revenue attributed to AI capabilities, and customer value achieved.
- Instrument every AI product for telemetry and LLMOps from day one. Financial attribution including expansion attached to AI, retention impact, ARR uplift as first-class product responsibility.
Manage Product Manager(s)
- Personal build cadence is non-negotiable. PRDs, prototypes, and discovery cycles you personally run are part of the Group’s output – measured and reviewed alongside the ships your direct report(s) own. Personal ship is not optional; it is what makes the coach credible.
- Directly manage a Senior Product Manager on day one. Set the Group’s craft standard: PRD quality, prototype rigor, launch discipline, and eval discipline. Every PM ships PRDs plus user stories plus working prototypes.
- Build out the AI & Data PM team over time. As the AI product line matures and demands more PM coverage, you will hire additional PMs into the Group. Sequencing and shape are yours to propose to the CPO.
Cross-Pillar & GTM Partnership
- AI is horizontal. Partner with Rights & Platform and Royalties & Financials PMs to embed Assistants and Agents where they compound the most. Never build in isolation, never let AI become a silo.
- Work hand-in-glove with Engineering leadership on architecture, model selection, LLM-vs-deterministic tradeoffs, and integration surface area. Earn the room in technical discussions.
- Partner with the CRO, Marketing, and Client Expansion on positioning, packaging, and landing AI capabilities. Sign off on Group launch claims for accuracy and defensibility.
What You’ll Bring
- 7–10 years of Product Management experience in B2B enterprise SaaS. Direct management experience is a plus but not the primary bar; the primary bar is having personally built and shipped AI products. Prior direct management of ICs is required as this seat manages a Senior Product Manager on day one. The primary weight in evaluation, however, sits on having personally built and shipped AI-native products; direct management experience alone, without shipped AI product, does not clear the bar.
- Proven track record shipping AI-native products to production not AI features bolted onto existing workflows. You have personally taken at least one Agent-class product from zero to shipped, adopted, and measured.
- Fluency with modern LLM application patterns including MCP servers, evals, prompt design, RAG, human-in-the-loop workflows, cost curve management, model selection, and telemetry / LLMOps.
- Direct hands-on use of Claude, Codex, Cursor, or comparable AI development tooling in your own daily workflow.
- Real experience running Jobs to Be Done discovery and translating it into product bets including structured interviews with power users, churned customers, and non-customers, not just the friendly voices.
- Track record with pricing, packaging, and monetization for enterprise software, ideally including usage-based, credit-based, or hybrid subscription + usage models.
- Comfortable at the seam between AI product surfaces and the reporting/analytics platform underneath. Prior experience shipping in an analytics, reporting, BI, or embedded data product area, cloud data warehouse plus embedded BI (Snowflake, Sigma, or comparable), is a strong plus.
- Strong understanding of enterprise AI concerns like accuracy, explainability, permissions, data privacy, auditability, security, and customer trust treated as product properties, not compliance overhead.
- Excellent written and verbal communication. You can frame an AI product strategy for the executive team and a sprint tradeoff for an engineer in the same afternoon.
How We’ll Know You Are the One
- You are a builder first. Your happiest state is running discovery, writing PRDs, and shipping prototypes. Management is something you do to make more of that happen for the team around you.
- You are AI-native, not AI-curious. You already reach for agents in your own daily workflow and have opinions about where AI improves decisions without weakening accountability or context.
- You reimagine workflows customer-back. You do not sprinkle copilots onto existing UIs and call it AI.
- You treat evals, trust, cost, and financial attribution as product properties. You can talk hallucination handling, confidence design, cost curves, and AI bookings definitions without notes.
- You believe pricing and packaging are product work, not Finance work.
- You see the seam between AI product surfaces and the reporting platform under them as a feature of the role, the two are stronger run together.
What We Offer
- Significant impact: Direct influence over how the organization operates and executes
- Executive visibility: Close partnership with C-suite leadership
- Growth opportunity: Lead a team and shape operational strategy as we scale
- Collaborative culture: Work with smart, ambitious cross-functional teams
- Competitive compensation and benefits package
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 8,943 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At RightsLine, 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 $220,200 based on 862 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
RightsLine AI Hiring
RightsLine has 1 open AI role right now. They're hiring across AI Product Manager. Based in El Segundo, CA, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.
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