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About Ande
We're a Series A startup building an AI-native platform that connects Enterprises and Hospitality across the entire booking experience, backed by top-tier investors including Lightspeed Venture Partners, Redpoint Ventures, Sierra Ventures, and Bain Capital, and led by a serial entrepreneur with offices in NYC and SF. We help companies turn every dinner, event, and offsite into something effortless to plan and a breeze to manage, plugging right into the tools they already use.
About the Role
Corporate teams spend billions every year on business entertainment, dinners, events, client experiences, and almost none of it runs through purpose-built software. At the center of our platform are AI agents that do real work for our customers: agents that carry multi-step, high-stakes workflows through to completion, with real money and real relationships on the line. We're looking for a PM to own these agentic experiences. There's no established playbook for this work; the patterns for running agents in production are being figured out in real time, by a small number of teams, and we're one of the teams doing the inventing. You'll own how our agents behave, how their quality is measured and improved, and how the system earns the trust to take on more. AI isn't a feature we bolted on, it's the foundation of our product and how we run the company: we automate what can be automated so people spend their time on judgment, not busywork. As a PM here, you'll prototype ideas yourself, get answers from data without waiting on anyone, and ship faster than you're probably used to.
What You'll Do
- Own the end-to-end experience our customers have with our AI agents, from first interaction to completed outcome
- Own agent quality as a product: evaluation frameworks, review loops, and the metrics that define "good" for a production agent
- Own the division of labor between agents and people, where automation earns more autonomy, and where human judgment stays in the workflow
- Own the improvement engine: the feedback loops and mechanisms that make agents measurably better every week, at growing volume
- Define, prioritize, and execute the roadmap for our agentic product experiences
- Go deep in the mechanics: prompt and context engineering, evals, failure analysis; you'll read transcripts, find patterns, and turn them into product decisions
- Partner with engineering to ship improvements fast, and build the tooling that makes quality visible and measurable
- Work closely with the operations teams whose workflows interlock with the agents; they're part of your product surface
- Coordinate with PMs across the product group, since the agents touch nearly every part of the customer journey
- Track the frontier: new patterns, research, and techniques emerge weekly in this space, and part of your job is knowing what's real, what's noise, and what we should try next
What We're Looking For
- 5+ years of Product Management experience, including hands-on work shipping agentic workflows
- Fluency in the patterns of production agentic systems, things like escalation and handoff design, guardrails, human-in-the-loop, feedback loops, and eval-driven development, and the judgment to know which pattern fits which problem
- A habit of staying at the edge: this field reinvents itself every few months, and you're the kind of person who's already tried the new technique before most people have heard of it
- Systems thinking; you see the agent, the people around it, the feedback loops, and the failure modes as one system, and you design for the whole
- An empirical mindset; you'd rather read fifty transcripts than debate a hypothesis, and you know quality is measured, not assumed
- Strong technical fluency, comfortable with prompt and context engineering, and able to work shoulder-to-shoulder with engineers on agent architecture
- High agency; you spot what matters, take ownership, and run without waiting for permission
Bonus Points
- You've operated an agentic product in production, with real users and real consequences; you know what breaks at volume that demos never show
- You've built evaluation systems or quality frameworks for non-deterministic products
- Experience in marketplaces or products mediating between two parties
- You write, speak, or build in public about agentic systems, or you're active in the communities where this craft is developing
Why This Role Matters
Own the most visible, customer-facing part of what we do, and problems most PMs won't get to touch for years. Work directly with founders and leadership on strategy and execution. Small team, high ownership: your decisions ship, fast. Help define how agentic products get built, at a company where that's the whole point, early enough that your fingerprints are on everything.
Equal Employment Opportunity
Ande is an equal opportunity employer. We are committed to building a diverse and inclusive team and do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other characteristic protected by applicable federal, state, or local law. We welcome applications from all qualified candidates and will consider all applicants for employment without regard to any legally protected status
Compensation Range: $190K - $240K
Salary Context
This $190K-$240K range is above the median for AI Product Manager roles in our dataset (median: $193K across 93 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 8,943 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At ANDE, 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 in Demand for This Role
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. Disclosed range: $190K to $240K.
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.
ANDE AI Hiring
ANDE has 1 open AI role right now. They're hiring across AI Product Manager. Based in New York, NY, US. Compensation range: $240K - $240K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national 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.
Frequently Asked Questions
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