Interested in this AI Product Manager role at within?
Apply Now →Skills & Technologies
About This Role
About Within
Tldr on us: Series B, $91M raised, 7x growth last year.
Digital Transformation gave rise to a $600B consulting Industry. AI Transformation will be 10x bigger and will be delivered through agents. We built that.
Our AI discovers how work actually happens across every team and application, Structures it into a living Context Graph, and Improves it continuously.
ServiceNow mapped 900+ processes in 9 days. DoorDash captured 3,800+ finance operations in 14 weeks. That's not a project. That's compounding intelligence.
OpenAI, Google, DoorDash, and Stripe use Within to transform how they transform. We shipped GPT-4 document chat within 12 hours of OpenAI's API launch.
We move fast, reward agency, and care deeply about our customers, our team, and our mission.
The Opportunity
The hardest and most interesting problem in enterprise right now is making AI actually useful inside real, messy workflows. Most enterprises are failing at exactly that step, and it is the single biggest bottleneck standing between them and an AI transformation that works. At Within you’ll own the product surfaces where that gets decided: turning complex AI capabilities into experiences customers love, and doing it fast.
This is a full-stack builder role. You’ll own meaningful product areas end to end, from discovery through delivery, working directly with our CTO, engineering and design leads, and GTM, with the autonomy to define a problem, prototype the answer yourself, and ship it. You understand what’s under the hood, you write specs that don’t need translation, and you stay in the room through launch. To be clear, we are not hiring an engineer: the bar is expressing an idea in shippable form, not production-grade code.
What You’ll Own
- Core AI product areas with real scope. Define the problem, prioritize ruthlessly, write a crisp PRD in a day or two, and drive features from concept to production.
- The prototype that settles the argument. Build working prototypes with AI tooling to validate a direction before engineering commits.
- Discovery. Run customer interviews yourself and turn field feedback from Sales and Value Delivery into structured insight rather than anecdotes.
- The engineering and design partnership. Be the PM engineers trust for clear requirements and fast, principled decisions, and reason through real tradeoffs alongside them: should ABAC be applied at the process-index level, and what are the consequences?
- Concrete surfaces to own. Companion, AI Interviewer, Advisor, the agent repository: real, shipping product areas, not hypothetical roadmap items, that are yours to define and drive.
- AI-native behavior in the product. Set user expectations for probabilistic systems, design the feedback loops, and account for model failure modes.
- The outcome. Define success metrics up front, instrument, and close the loop on everything you ship, then present it to the whole team and to customers with clarity and confidence.
- Room to grow with the team. We move fast and stay agile, so what you own today is a floor, not a ceiling: ownership expands and shifts as you prove it out.
What We’re Looking For
- 5-8 years in product management. B2B SaaS or AI-platform background preferred, with a track record of shipping technically complex products to measurable outcomes.
- A builder with real product instincts. You ship working prototypes with AI tooling (Cursor, Claude Code, v0) to validate a direction rather than waiting on engineering capacity. Links to things you’ve built are a strong plus.
- Technically credible. You collaborate on architecture diagrams, reason about system tradeoffs, are comfortable with SQL and API documentation, and can raise a simple PR. Engineers trust your requirements.
- Shipped Gen AI, not traditional ML. You’ve built generative AI features that found product-market fit, and you know the tradeoffs of probabilistic systems: failure modes, non-determinism, evaluation, feedback loops. You’ve also pushed AI tooling well past chat, and you get models to do things most PMs don’t know are possible.
- Taste and UX intuition. You notice what’s wrong with an experience without being told, and your fixes feel obvious in hindsight. World-class visual design is not required. They're really good at distilling the underlying need beneath messy feedback and translating that into a simple, elegantUX.
- Customer-obsessed. You get in front of customers unsupervised and come back with structured insight, and you’re comfortable building experiences that are unfamiliar to users. You're able to ask high-quality questions that make customers feel heard and give you the signal that you need.
- Fast, optimistic, and high-slope. You synthesize a lot of context quickly, decide, and iterate. Ambiguity and speed energize you rather than stall you. You've developed rituals to continuously learn and stay at the bleeding edge of AI-native velocity.
Benefits:
- Equity in addition to competitive cash compensation
- 100% Employer-Paid Medical, Dental & Vision options!
- Paid Parental Leave
- $500 Annual Learning Fund
- $150 Monthly Wellness Fund
- 401k via Betterment
- Relocation support to San Francisco Bay Area (where applicable)
- Office-related Perks:
+ BART or Caltrain to the office?: Contribute pre-tax funds to a Parking & Transit account, and you will never be taxed for it! + Lunchtime, Leveled Up: Enjoy curated local eats delivered to the office daily to enjoy with the team over an engaging conversation + Snack Central: Drinks and snacks for every craving - from healthy bites to Within team favorites. + Safe & Secure Bike Room: Commute in and safely store your bike.
Within is an equal opportunity employer. Within provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, gender, national origin, sexual orientation, gender identity or expression, age, disability, genetic information, marital status or veteran status.
Compensation Range: $220K - $280K
Salary Context
This $220K-$280K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $193K across 99 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 9,653 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At within, 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($250K) sits 14% above the category median. Disclosed range: $220K to $280K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
within AI Hiring
within has 1 open AI role right now. They're hiring across AI Product Manager. Based in San Francisco, CA, US. Compensation range: $280K - $280K.
Location Context
AI roles in San Francisco pay a median of $264,500 across 2,413 tracked positions. That's 24% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.