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About This Role
Thanks for your interest in Success Academy! Running a large, fast-growing, and high-performing network of public charter schools takes a village - families, children, teachers, staff and faculty, advocates, and supporters alike. We are growing fast in New York and expanding to Florida, and we would love to welcome you to our community! We work tirelessly every day to ensure children have access to a fun, rigorous, whole-child education regardless of zip code or economic status. When you join SA, you play a part in giving every student who walks through our doors a fair shot at reaching his or her potential.
About the Role
As an AI Product Manager at Success Academy, you will sit at the intersection of business strategy, school operations, and emerging technology to drive AI-powered innovation across a growing network of 57+ schools. Sitting alongside engineers, data scientists, UX designers, and organizational leadership, you will own the end-to-end lifecycle of generative AI tools that empower educators and streamline enterprise workflows. This role is central to Success Academy's mission to scale educational excellence, ensuring that cutting-edge AI features translate directly into measurable efficiency gains and improved student outcomes.
Key Responsibilities
- Own the end-to-end AI product lifecycle, guiding features from initial discovery and ROI-focused prioritization through to delivery and continuous optimization.
- Translate complex stakeholder needs and high-level business goals into precise AI/ML feature requirements for engineering and data science teams.
- Design intuitive and seamless AI experiences by collaborating with UX/UI and technical teams to integrate LLMs, RAG pipelines, and ML outputs into daily workflows.
- Establish evaluation metrics and design experimentation frameworks alongside data scientists to validate model performance and feature effectiveness.
- Drive continuous product refinement by building user feedback loops that evolve models and AI capabilities based on real-world adoption.
- Act as the connective tissue across the organization, balancing technical feasibility with business impact to deliver high-value AI solutions on schedule.
- Contribute to shaping the broader organizational AI strategy, aligning product roadmaps with long-term enterprise goals.
Qualifications
- 3+ years of product management experience (AI/ML or data-driven platforms preferred) with a track record of launching enterprise or customer-facing tools.
- Hands-on experience building custom GPTs or basic AI applications, with familiarity in LLM ecosystems (OpenAI, Anthropic, Gemini) and GenAI concepts (RAG, prompt engineering, vector databases).
- Demonstrated expertise in Agile environments using tools like Jira and Confluence to manage backlogs, gather requirements, and run sprints.
- Proven capability to translate ambiguous business challenges into concrete technical solutions and manage AI projects from conception to launch.
- Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, Economics, Applied Mathematics, Machine Learning, or a related field (or equivalent practical experience).
Competencies
- Strategic Prioritization: Consistently applies an ROI-first, data-driven approach to balance business impact, technical feasibility, and roadmap decisions.
- Cross-Functional Leadership: Serves as the central bridge between business partners, educators, and technical execution teams to rally stakeholders around common goals.
- Growth Mindset & Intellectual Curiosity: Demonstrates a deep drive to break down complex problems, continuously iterate based on user feedback, and stay at the forefront of AI innovation.
- Ownership & Execution: Holds a high standard of excellence and takes full accountability for managing products hands-on in a fast-paced, dynamic environment.
Compensation, Benefits & Perks
At Success Academy, we invest boldly in our Network team with a comprehensive package designed to support your health, financial future, and overall well-being:
- Day-One Health & Wellness: Medical, dental, and vision coverage starting your first day (SA covers 82% of our core medical plan), plus a free One Medical membership and confidential mental health support via EAP/Talkspace.
- Financial Security: 403(b) retirement plan with a 3% employer match, up to $2,400/year in employer HSA contributions, employer-paid life/disability insurance, and Public Service Loan Forgiveness (PSLF) eligibility.
- Time to Recharge: Generous paid time off, paid holidays, and paid parental leave.
- Perks & Stipends: A 50/month cell phone stipend (600/year), pre-tax commuter benefits, backup family care, and performance-based earning incentives.
This is a hybrid position based in our NYC Financial District office, requiring on-site work Tuesday through Thursday and remote work on Monday and Friday.
Success Academy Charter Schools is an equal opportunity employer and actively encourages applications from people of all backgrounds. Compensation is competitive and commensurate with experience. Success Academy offers a full benefits program and opportunities for professional growth.
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We are an equal opportunity employer and value diversity at our organization. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We actively seek applications from people of all backgrounds to strengthen our community and the perspectives needed to flourish in a multicultural world. Success Academy offers a full benefits program and opportunities for professional growth.
Success Academy Charter Schools does not offer employment-based immigration sponsorship.
Salary Context
This $150K-$175K range is below the median for AI Product Manager roles in our dataset (median: $194K across 123 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 9,079 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Success Academy Charter Schools, 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 947 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($162K) sits 26% below the category median. Disclosed range: $150K to $175K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Success Academy Charter Schools AI Hiring
Success Academy Charter Schools has 3 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span New York, NY, US, New York, NY. Compensation range: $175K - $175K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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