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About Wonder
Everything’s on the menu at Wonder. Except compromise.
Wonder is the mealtime platform built to feed every craving in one order. With Wonder, you can mix and match hundreds of dishes from 20\+ exclusive restaurants in a single order, so everyone gets exactly what they’re craving. Enjoy everything from tacos to Thai with $0 delivery fees, plus dine\-in or pick up at a Wonder location near you. Each dish is made to order on\-site by our culinary team and served hot.
Behind the scenes, Wonder owns and operates every step of the dining experience, from recipe development to the technology that powers our experience, allowing us to deliver quality, variety, and consistency at scale.
*Join us as we work to make great food more accessible.*
About The Opportunity
As the Wonder Create Principal Product Manager on Wonder's new AI \& Robotics team, you'll define and build the platform that lets anyone launch a digital restaurant in minutes. This is an entrepreneurial, high\-impact role for someone eager to push the boundaries of what’s possible by blending real\-world AI applications with strong technical execution. This is a true 0 to 1 product role with the opportunity to shape a new category at the intersection of robotics, AI, the creator economy, and food.
We collaborate openly, move fast, and believe that diverse perspectives and backgrounds make the product smarter, more human, and ultimately more useful. This role reports to the General Manager of Wonder Create.
The Impact You Will Make
- Own the Wonder Create product roadmap from concept to launch—defining the creator experience, prioritizing features against business impact, and shipping the MVP that proves anyone can launch a restaurant in under 15 minutes
- Define novel product solutions for creator\-specific needs like on\-demand branded packaging personalization, custom sauce formulations, and dynamic menu pricing, working with operations and engineering to make technically complex features feel seamless
- Work cross\-functionally with AI engineers, robotics teams, culinary operations, and design to balance creator freedom with production constraints, ensuring what the AI generates can actually be made reliably in our robotic kitchens
- Define and validate the marketplace mechanics: creator discovery flows, pricing optimization tools, earnings transparency, quality feedback loops, and the platform fee structures that make the economics work for both creators and Wonder
What You Bring to the Table
- 6\+ years of product management experience, ideally in consumer tech, marketplace platforms, or creator economy products; shipped 0\-to\-1 products that reached meaningful scale
- Strong technical fluency to partner effectively with AI/ML and robotics engineers
- Experience designing AI\-powered workflows or tools that hide complexity from users, making sophisticated capabilities feel simple and intuitive
- Startup\-ready mindset: rapid prototyping, crisp scoping, clear communication, and comfort owning ambiguous problems where you're defining the category as you build it
- Proven ability to define and validate business models, working with finance and operations teams to ensure unit economics work while building products users love
- Track record of working cross\-functionally with design, engineering, operations, and business teams to ship products under tight timelines with incomplete information
- Deep user empathy and research skills to understand diverse creator needs, from professional chefs to home cooks to content creators, translating insights into actionable product requirements
- Data\-driven decision making with experience establishing metrics frameworks, running experiments, and using analytics to iterate toward product\-market fit
*As a matter of company policy, Wonder does not sponsor applicants for employment visa status for this role.*
Our hybrid model requires 3 days a week in the office. That said, many team members choose to come in more often to take advantage of in\-person collaboration and connection. You're welcome—and encouraged—to be in the office up to 5 days a week if it works for you.
\#LI\-Hybrid
New York: $193,500 per year.
Wonder uses geographic\-specific salary structures, which means the salary offered may vary depending on where the job is located. The final salary offer will take into account various factors, such as the candidate's skills, education, training, credentials, and experience.
Benefits
The benefits applicable to this role include a competitive compensation package with equity and a 401(k). We also offer a choice of medical, dental, and vision plans, company paid short and long term disability coverage, paid time off including flexible time off for exempt employees, paid vacation for non\-exempt employees, and paid sick leave in compliance with applicable law in addition to paid parental leave, discounted meals and exclusive perks across the Wonder family of brands.
Eligibility, effective dates, and available plan options vary by employment classification and location. To learn more about benefits for this role, visit our Careers page here.
A Final Note
At Wonder, we build the best teams by hiring with an objective lens — evaluating people for their potential while championing diversity, equity, and inclusion. We do not discriminate based on race, color, religion, gender identity or expression, sexual orientation, national origin, age, military service eligibility, veteran status, marital status, disability, or any other protected class. As part of our commitment to fair and compliant hiring practices, Wonder participates in the federal government's E\-Verify program to confirm employment eligibility. If you need an accommodation during the interview process, please let your recruiter know.
We look forward to hearing from you! We'll contact you via email or text to schedule interviews and share information about your candidacy.
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 Wonder, 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 $217,100 based on 471 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
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.
Wonder AI Hiring
Wonder has 3 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in New York, NY, US. Compensation range: $249K - $249K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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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