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About This Role
Location
San Francisco
Address
San Francisco, California
Employment Type
Full time
Location Type
Hybrid
Department
Engineering, Product & Design
Compensation
- Tier 1Base Salary Range: $210K – $255K • Offers Equity • Offers Bonus
The base salary range represents the minimum and maximum of the salary range for this position based at our San Francisco headquarters. The actual base salary offered for this position will depend on numerous factors, including individual proficiency, anticipated performance, and the location of the selected candidate. Our base salary is just one component of Ironclad’s competitive total rewards package, which also includes equity awards (a new hire grant, along with opportunities for additional awards throughout your tenure), competitive health and wellness benefits, and a commitment to career growth and development.
Build AI contracting at enterprise scale. Ship it fast.
Ironclad is building AI on one of the most consequential datasets in enterprise software: over 2 billion contracts across thousands of the world's largest organizations. Contracts run every dollar, deal, and business relationship on the planet, and the AI transformation of this space is still largely uncharted. Engineers here are building agentic solutions to problems that haven't been solved yet, in a category that's still being defined. The feedback loop is short, the data is rich, and the people closest to the work are the ones deciding what gets built next. That ability to deliver impact quickly is why the the companies at the forefront of AI choose Ironclad to manage their contracts.
We’re consistently recognized as a leader in the industry: a Leader in the Forrester Wave and Gartner Magic Quadrant for Contract Lifecycle Management, a Fortune Great Place to Work, and one of Fast Company’s Most Innovative Workplaces. Ironclad has also been named to Forbes’ AI 50 and Business Insider’s list of Companies to Bet Your Career On. We’re backed by leading investors including Accel, Y Combinator, Sequoia, BOND, and Franklin Templeton. For more information, visit www.ironcladapp.com or follow us on LinkedIn.
About the role
Procurement teams need more than a repository for contracts. They need an intelligent system that helps them understand supplier relationships, prevent leakage, manage commitments, prepare for renewals and amendments, and turn contract data into negotiation leverage.
We are looking for a Senior Product Manager to lead the next generation of AI-powered procurement experiences at Ironclad. You will own product strategy and execution across capabilities such as supplier and contract intelligence, obligations, renewals, amendments, risk insights, and procurement workflows.
This role sits at the intersection of procurement operations, legal contracting, enterprise workflow design, and applied AI. You will work directly with procurement and legal users, partner closely with engineering, design, data, applied AI, and go-to-market teams, and turn ambiguous customer problems into products that are trusted, measurable, and adopted.
The right person is equally comfortable in a procurement workflow review, a customer conversation, an AI evaluation discussion, and a roadmap tradeoff. You do not need to be an AI researcher—but you should be curious about how models, data, retrieval, evaluation, and human-in-the-loop controls come together in a production product.
What you'll do
- Own the vision, strategy, roadmap, and success metrics for a meaningful part of Ironclad's procurement AI product area.
- Develop a deep understanding of procurement and legal workflows, including intake, supplier and contract management, obligations, renewals, amendments, risk review, and negotiation preparation.
- Identify high-value opportunities where contract intelligence and AI can help procurement teams save time, prevent leakage, reduce risk, and gain leverage.
- Shape experiences that connect structured contract data with actionable workflows—for example, supplier relationship views, contract-family intelligence, obligation tracking, renewal preparation, amendment drafting, risk insights, and consolidation opportunities.
- Partner with engineering, applied AI, data, and design to define the right product architecture, user experience, data requirements, and human approval points.
- Define how we measure AI quality in production, including accuracy, coverage, latency, task success, user acceptance, trust, and business value.
- Build feedback loops that let users review, accept, reject, and improve AI-generated insights and actions.
- Make complex enterprise workflows feel simple, explainable, and controllable while preserving the nuance that procurement and legal users need.
- Lead customer discovery, design-partner programs, usability testing, and product validation with procurement, legal, finance, and operations teams.
- Translate customer evidence and product data into clear prioritization decisions, product requirements, and execution plans.
- Partner with GTM, Solutions, Sales, and Customer Success on positioning, launches, enablement, adoption, and value realization.
- Drive products from discovery through launch and iteration, including clear go/no-go decisions and post-launch measurement.
What success looks like
- Procurement and legal customers use Ironclad as a source of truth for supplier relationships, contract commitments, and commercial risk.
- AI-powered experiences move beyond insights to help users complete high-value procurement work faster and with appropriate human control.
- The team can distinguish model quality from workflow, data, retrieval, usability, and adoption problems—and improve the right layer.
- Product investments are tied to measurable outcomes such as time saved, leakage prevented, discounts or rebates captured, faster renewal and amendment preparation, accepted insights, and delegated tasks.
- Ironclad builds durable shared capabilities that can serve multiple workflows and teams, rather than one-off AI features.
What we're looking for
- 5+ years of product management experience, ideally in enterprise SaaS, workflow products, procurement technology, legal technology, or adjacent business systems.
- A track record of owning products end to end, from customer discovery and strategy through launch, adoption, and iteration.
- Deep familiarity with procurement, sourcing, supplier management, contract lifecycle management, legal operations, or a closely related domain.
- Strong customer empathy and the ability to earn credibility with procurement leaders, lawyers, operators, and technical partners.
- Experience translating complex workflows, data, and business rules into clear product experiences and requirements.
- Practical fluency with AI product development, including model capabilities and limitations, evaluation, data quality, retrieval or grounding, feedback loops, and human-in-the-loop controls.
- Strong quantitative judgment and comfort defining success metrics, analyzing product behavior, and making tradeoffs using evidence.
- Excellent written and verbal communication, with the ability to create clarity across engineering, design, applied AI, data, GTM, executives, and customers.
- High ownership, sound product judgment, and the ability to operate effectively in ambiguity.
Nice to have
- Experience building procurement or spend-management products, including procure-to-pay, sourcing, supplier intelligence, contract intelligence, or procurement orchestration.
- Experience with legal technology, CLM, contract negotiation, obligations, renewals, amendments, or compliance workflows.
- Experience with enterprise integrations such as SAP, SAP Ariba, Coupa, Zip, Salesforce, or downstream finance and procurement systems.
- Experience shipping agentic workflows or AI products that can recommend, draft, or take action with user approval.
- Familiarity with enterprise data models, entity resolution, contract families, knowledge graphs, structured extraction, or document intelligence.
- Experience making build-versus-buy or vendor-partner decisions for AI, data, or workflow capabilities.
- Experience launching products in a highly regulated, high-trust, or document-heavy environment.
Base Salary Range: $210,000 - $255,000, offers company bonus
The base salary range represents the minimum and maximum of the salary range for this position based at our San Francisco headquarters. The actual base salary offered for this position will depend on numerous factors, including individual proficiency, anticipated performance, and the location of the selected candidate. Our base salary is just one component of Ironclad’s competitive total rewards package, which also includes equity awards (a new hire grant, along with opportunities for additional awards throughout your tenure), competitive health and wellness benefits, and a commitment to career growth and development.
US Full-Time Employee Benefits at Ironclad:
- 100% health coverage for employees (medical, dental, and vision), and 75% coverage for dependents with buy-up plan options available
- Market-leading leave policies, including gender-neutral parental leave and compassionate leave
- Family forming support through Maven for you and your partner
- Paid time off - take the time you need, when you need it
- Monthly stipends for wellbeing, hybrid work, and (if applicable) cell phone use
- Mental health support through Modern Health, including therapy, coaching, and digital tools
- Pre-tax commuter benefits (US Employees)
- 401(k) plan with Fidelity with employer match (US Employees)
- Regular team events to connect, recharge, and have fun
- And most importantly: the opportunity to help build the company you want to work at
**UK Employee-specific benefits are included on our UK job postings
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Compensation Range: $210K - $255K
Salary Context
This $210K-$255K 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 Ironclad, 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 ($232K) sits 6% above the category median. Disclosed range: $210K to $255K.
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.
Ironclad AI Hiring
Ironclad has 4 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, Data Scientist. Positions span San Francisco, CA, US, San Francisco, CA, New York, NY. Compensation range: $235K - $255K.
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.
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