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About This Role
About Ramp
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Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000\+ companies: authorizing payments, flagging risk, categorizing spend, and closing books.
The problems are high\-stakes, data\-dense, and unforgiving.
We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome.
The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same.
If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it.
About the Role
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Most companies have more GTM ideas than they can reliably execute. Launching a new motion still requires manual targeting, weeks of enablement, seller coordination, and messy measurement. Learnings live in people's heads. When something works — or stops working — the playbook doesn't change fast enough.
Ramp is building the AI platform that GTM runs on — a ground\-up application layer where agents handle execution, playbooks encode institutional knowledge, and every interaction feeds a learning loop that makes the system smarter each cycle. We're operating at the frontier of what agents can do in a real enterprise context, and the problems are unsolved: reliable background execution at scale, human\-agent collaboration that earns trust, and feedback loops that turn raw GTM signal into a compounding organizational advantage.
As a PM for Revenue, you'll own the data and execution layer that makes the GTM platform work: the scoring and routing systems that get the right accounts to the right people, the data infrastructure and acquisitions that power intelligence across the org, and the agents for SDRs, Solutions, and channel teams. This is the foundation that everything else depends on. Because your users are down the hall, feedback is immediate and iteration cycles are short. You'll build sharper product instincts faster here than in most roles — the kind that only come from shipping, seeing what lands, and doing it again. This role partners closely with Engineering, Data Science, Design, Finance, and Sales leadership.
What You’ll Do
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- Build for GTM Teams. Own the agentic tooling and workflows for the GTM teams that are less served today — SDR prospecting and outreach, Solutions discovery and POV workflows, and channel partner execution. These motions are high\-context and high\-value, and largely greenfield.
Own the GTM data platform. Define the data contracts, schemas, and pipelines that make GTM intelligence possible. Own account scoring, routing, and assignment — the systems that determine which accounts get attention, from whom, and when. Drive net new data acquisitions that expand what our agents can reason over. Ensure the data feeding our AI tools is accurate and trustworthy enough to act on.
Serve every GTM motion, not just one. Resist the pull toward optimizing for a single team. Understand the distinct workflows and incentives across the GTM org and build systems flexible enough to serve all of them — while still being opinionated about what good looks like.
Instrument, iterate, and close the loop. Define what good looks like. Build eval frameworks, feedback systems, and dashboards that tell you whether the tools are driving real adoption and impact — and use that signal to make reps active participants in improving the system over time.
What You’ll Need
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- 1–3 years of product experience, or 3 \- 5 years of total experience in a role that built real judgment — banking, consulting, deployment strategy, agent PM, GTM operator, or founder. The path matters less than what you built and what you learned.
- Deep hands\-on experience building with AI: you've prototyped, shipped, and iterated on AI tools or agents — not just managed roadmaps about them. Technical fluency with modern AI coding harnesses (Cursor, Claude Code, Codex).
- Working knowledge of core LLM concepts (prompting, embeddings, retrieval, evals) and the judgment to translate these into reliable products where hallucinations have real consequences.
- Comfortable in SQL and confident reading data. You can pull your own analysis, spot what the numbers aren't telling you, and make decisions without waiting for a data team.
- Curious systems thinker who learns fast and defaults to building. You connect dots across AI, data, and GTM quickly, pick up new domains without needing to be an expert first, and don't wait for perfect requirements to ship.
- Comfort with 0\-to\-1 ambiguity. There is no established playbook here. You define the problems, prioritize ruthlessly, and build the foundation others will build on.
- Scrappy, opinionated, and low\-ego. You take the work seriously and yourself less so. You fit in on a team that moves fast, debates hard, and genuinely enjoys building together.
Nice\-to\-Haves
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- Broad GTM fluency and operational empathy for the field — you understand how GTM functions operate, what they need from tooling and data, and how to build products that earn trust in a skeptical org.
- Strong working knowledge of GTM data and systems: CRM data models, sales engagement tooling, pipeline data, and intent signals.
- Prior work in fintech, enterprise SaaS, or other domains where data quality is load\-bearing.
- Proven ability to manage senior stakeholders — you can earn trust with VP\- and SVP\-level leaders, align them on tradeoffs, and keep work moving without escalating everything.
- Curiosity about externalizing internal AI work — turning what we build for our own GTM org into a product or market signal.
Benefits available to all full\-time Ramp employees (Global)
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- Flexible PTO
- Centralized home\-office equipment ordering
- Health and wellness stipend
- Budget for intra\-office travel
- Weekly coffee stipend
United States
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- 100% medical, dental \& vision insurance coverage for you, with partial coverage for dependents
- One Medical annual membership
- 401(k), including employer match on contributions made while employed by Ramp
- Fertility HRA (up to $10,000 per year)
- Parental leave: up to 16 weeks (birthing \+ bonding) or 8 weeks (bonding only) at 100% pay
- Pet insurance
- In\-office perks: lunch, snacks, drinks, and more
- Relocation support to NYC or SF (as needed)
Canada
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- Group medical, dental, and vision coverage through Sun Life
- Life, AD\&D, and disability coverage
- Fertility drug coverage (up to $4,000 lifetime)
- Group Retirement Plan with employer match (RRSP \+ DPSP)
- Parental leave: up to 16 weeks (birthing \+ bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay
- Employee Assistance Program and virtual care through Lumino Health
United Kingdom
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- Private medical insurance through Freedom Elite
- Virtual GP and at\-home care via eMed x Livi
- Workplace pension through Penfold, with salary sacrifice option
- Parental leave: up to 16 weeks (birthing \+ bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay
Referral Instructions
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If you are being referred for the role, please contact that person to apply on your behalf.
Other notices
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Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
*Beware of recruiting scams: Ramp will only contact you through official @**Ramp.com* *email addresses and will never ask for payment or sensitive personal information during the hiring process.*
Ramp Applicant Privacy Notice
Compensation Range: $235K \- $325K
Salary Context
This $235K-$325K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $185K across 167 roles with salary data).
View full AI Product Manager salary data →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 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Ramp, 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 $217,100 based on 471 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($280K) sits 29% above the category median. Disclosed range: $235K to $325K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Ramp AI Hiring
Ramp has 3 open AI roles right now. They're hiring across Data Scientist, AI Product Manager, AI Software Engineer. Positions span New York, NY, US, US. Compensation range: $297K - $330K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
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