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About The Bridgespan Group
The Bridgespan Group (“Bridgespan”) is a global nonprofit that aims to build a better world by strengthening the ability of mission\-driven organizations, philanthropists, impact investors, and corporations to achieve breakthrough results in addressing society’s most important challenges and opportunities.
Founded in 2000, Bridgespan has offices in Boston, Johannesburg, Mumbai, New York, San Francisco, and Singapore. Across these six locations, we have a diverse team of staff with varied backgrounds and experience across multiple sectors. We work with social change organizations such as TaRL Africa, Tiko, and CAMFED; philanthropies and foundations such as OSF, Women First Fund, and The Ford Foundation; impact investors such as TPG’s Rise Fund, Black Ambition, and AfricInvest; and bold funding collaboratives such as TED’s Audacious Project and Co\-Impact.
Bridgespan aims for a world where all people have the opportunity to flourish. Given how society works today—as manifest in systems and mindsets that have developed over time—that opportunity is not accessible to all. We are committed to equity, specifically to doing work to help ensure that race, gender, and other dimensions of identity are not determinants of life outcomes and to building an organization where the diverse set of individuals we need can thrive in pursuit of their full professional potential, and to proactively addressing the systems that help or hinder these goals. It is a commitment to both external equity work and internal inclusivity that gets Bridgespan closer to achieving its mission.
Position
We are seeking a Product Manager, AI to help shape how artificial intelligence is applied across Bridgespan’s workflows, tools, and systems to improve how we work and how we deliver impact.
This role focuses on identifying high\-value opportunities to apply AI, structuring and testing solutions, and driving the development of scalable, secure, and effective AI\-enabled workflows.
It complements existing product management efforts by focusing on AI\-driven opportunities across workflows, tools, and systems.
This is an opportunity to work at the intersection of business, technology, and emerging AI capabilities, helping Bridgespan thoughtfully incorporate AI into how we operate and deliver our work.
This role will operate across both experimentation and scaling of AI\-enabled solutions, with specific focus areas evolving based on organizational priorities.
Who you are
- You are an experienced Product Manager who enjoys working in ambiguity and bringing structure to new problem spaces.
- You are comfortable working across business and technology teams, and you have a strong ability to translate real\-world problems into practical, testable solutions.
- You are excited about the potential of AI, but you approach it with a pragmatic, outcome\-oriented mindset. You are equally comfortable exploring new ideas and driving toward tangible impact.
- You thrive in collaborative environments and are energized by working with stakeholders to identify opportunities, test ideas, and evolve solutions over time.
What you’ll do
As a Product Manager, AI, you will work closely with business stakeholders, Technology, and cross\-functional partners working alongside existing product management efforts, to structure, test, and scale AI\-enabled solutions across Bridgespan’s workflows, tools, and systems.
Key responsibilities include:
Opportunity Identification \& Discovery
- Partner with business stakeholders to understand workflows, pain points, and opportunities for applying AI
- Clearly frame problems and identify where AI can create meaningful value
Experimentation \& Prototyping
- Structure and drive experiments to test AI\-enabled approaches, including defining hypotheses, success criteria, and prototypes
- Leverage available tools to rapidly explore and validate solutions
- Evaluate results and synthesize learnings to inform next steps
Solution Development \& Workflow Design
- Drive the development of scalable AI\-enabled workflows and solutions for use cases ensuring solutions are grounded in real\-world needs and ways of working
- Translate business needs into clear solution approaches and requirements, applying appropriate structure and rigor based on context
- Partner with Technology and external vendors, as needed, to implement solutions
- Ensure solutions are designed for reuse and scalability across teams, avoiding one\-off implementations
Cross\-Functional Collaboration
- Work closely with Technology to ensure solutions align with data security, governance, and architectural standards
- Maintain strong communication with stakeholders, providing clarity on progress, tradeoffs, and outcomes
Continuous Improvement \& Scaling
- Monitor usage, adoption, and performance of AI\-enabled solutions
- Identify opportunities to improve, simplify, and expand workflows over time
- Promote reuse and consistency across solutions to avoid duplication and fragmentation
The ideal candidate should have
Required Qualifications
- 5\+ years of professional experience, including 3\+ years in product management or a similar role
- Hands\-on experience applying AI tools, automation, or data\-driven workflows to solve real\-world problems
- Experience working with internal systems, workflows, or enterprise applications
- Strong ability to translate business needs into product solutions and drive execution
- Excellent communication and stakeholder management skills
- Ability to work across technical and non\-technical teams
- Comfort working in ambiguous and evolving environments
- Strong problem\-solving and analytical skills
Preferred Qualifications
- Experience in consulting, professional services, or mission\-driven organizations
- Familiarity with enterprise systems such as CRM, ERP, or knowledge management platforms
- Experience working in agile environments and managing product backlogs
Why this role matters
AI is rapidly changing how organizations operate. At Bridgespan, we have an opportunity to apply AI thoughtfully in ways that improve how we work and increase our impact. This role will help ensure that our approach to AI is structured, practical, and aligned with real business needs—while maintaining the governance and rigor required for a mission\-driven organization.
*PLEASE NOTE: YOU MUST BE ELIGIBLE TO WORK IN THE US WITHOUT SPONSORSHIP, THE BRIDGESPAN GROUP IS UNABLE TO PROVIDE VISA SPONSORSHIP*
At Bridgespan, we are committed to diversity, equity, and inclusion because we are passionate about helping our clients achieve breakthrough results. An organization that reflects the diversity of our clients and their beneficiaries will produce deeper relationships, engagement, and insights. Bridgespan is committed to providing equal opportunities. We serve diverse organizations and are committed to non\-discrimination. Bridgespan will not discriminate against any employee with respect to any term or condition of employment, including but not limited to less favorable treatment, exclusion from employment or employment opportunities (including hiring, assignment, performance assessment and promotion) on the basis of race, color, religion, national origin, citizenship, ancestry, gender (including pregnancy), gender identity, age, disability, marital status, sexual orientation, expression, veteran’s status or other protected characteristics or status. We actively partner with organizations representing minority concerns and interests to build high\-performing teams that mirror the communities we serve.
All company sponsored programs including training, job, social and recreational activities are required to be nondiscriminatory and all human resource practices are monitored to ensure equal opportunity.
Salary Context
This $120K-$125K range is in the lower quartile for AI Product Manager roles in our dataset (median: $189K across 161 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 3,823 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At The Bridgespan Group, 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 $213,800 based on 583 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($122K) sits 43% below the category median. Disclosed range: $120K to $125K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
The Bridgespan Group AI Hiring
The Bridgespan Group has 1 open AI role right now. They're hiring across AI Product Manager. Based in US. Compensation range: $125K - $125K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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