---
title: 'AI Product Manager at Mulligan Funding'
canonical: 'https://feeny.ai/job/ai-product-manager-mulligan-funding-san-francisco-kqb9q3164pdz'
type: 'job'
last_seen: '2026-10-14'
---

# AI Product Manager at Mulligan Funding

- **Company:** Mulligan Funding
- **Location:** San Francisco, CA
- **Compensation:** $170k–$250k
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-07-21
- **Last confirmed live:** 2026-10-14
- **Apply:** https://jobs.lever.co/mulliganfunding/ee861a37-f19d-4230-8473-a1a375fcdc44

## Job description

Mulligan Funding is a San Diego based fintech lender to the Small and Medium Sized Business market. We are building a deeply researched and first-of-it’s-kind, Agentic AI complete funnel automation strategy. Mulligan has decades of deep credit underwriting and analytics expertise, combined with rich data from almost a million applications and billions of dollars funded via a wide loan size of $10k to $10 million. We are a mission-driven company, focused on solving the SMB market’s access to the fast and affordable capital they need to thrive by re-inventing how credit risk is measured, priced, and negotiated. We combine a generational-technology with unwavering integrity and a human touch. We are replacing legacy small business lending with fast, intelligent, AI-driven decisioning. Backed by 18 years of proprietary credit data, analytics expertise, and insights from almost a million applications and billions in funded loans ($10k to $10M), we possess an institutional foundation competitors simply can't match.

We’ve already moved past the experimental phase: production-grade AI agents are actively running in our credit and underwriting workflows today, driving clear results in speed, consistency, and decision quality. Now, we are expanding this AI-first approach across Sales, Customer Lifecycle, Finance, and Capital Markets—and we need builders who want to own the outcomes at the center of this work.

We are hiring an AI Product Manager to embed directly inside one of our core AI initiatives. In this hands-on role, you will lead discovery and design, work shoulder-to-shoulder with business teams, and manage execution alongside specialist engineering partners. We need someone who thrives in ambiguous environments, owns their results end-to-end, and can take a messy problem and turn it into a clear design. On day one, you will sit with functional experts, map out their daily workflows, and redesign them from scratch using modern AI tools.

Reporting to the Chief Credit Officer, you will operate with high day-to-day autonomy inside your pod. This position is ideal for an early-career product manager looking to build deep AI expertise fast, take real ownership of production outcomes, and grow alongside our expanding team.

Current State Discovery & Process Intelligence

- Deep-Dive SME Discovery: Lead structured discovery sessions with functional Subject Matter Experts (SMEs) to map the current process in full — every step, decision point, pain point, exception case, volume estimate, and cycle time. The goal is to understand how the process actually works, not how it is documented.
- Metric Baselining: Quantify the current state — cycle times, error rates, manual touchpoints, cost of failure — to create the measurement foundation against which post-deployment impact will be assessed.
- Edge Case Extraction: Surface the process edge cases, informal workarounds, and undocumented exceptions that SMEs carry in their heads but rarely write down. These are often where AI creates the most value — and where designs fail if not addressed.
- Hypothesis Testing: Work with the AI Product Director to test design hypotheses during discovery — some will be confirmed by the process reality, some will need to be revised. Discovery should challenge assumptions, not confirm them.

AI Solution Design & Brief Authorship

- 0-to-1 Reimagining: Translate current state intelligence into an AI transformation design brief that reimagines the process from scratch — not an AI-assisted version of the status quo. The question to answer is: if we were designing this process from scratch with AI available, what would it look like? Vision and ability to think outside of the box is mandatory
- Design Brief Production: In close collaboration with the AI Product Director, author a detailed solution design brief that defines the AI capability, its inputs and outputs, the logic it applies, the QC framework it requires, and the acceptance criteria the engineering partner must meet. This brief is the specification the partner builds to — precision matters.
- Partner Identification: Conduct research to identify specialist engineering partners with a demonstrated track record in the specific use case domain. Apply Mulligan's IP sensitivity framework to determine the appropriate engagement model.
- Go/No-Go Recommendation: Produce a clear go/no-go recommendation to the AI Product Director with supporting rationale — including current state baseline, proposed AI solution design, recommended partner, and initial cost-benefit framing.

Engineering Partner Management

- Day-to-Day Partner Interface: Manage the working relationship with the specialist engineering partner through the build cycle — coordinating build reviews, tracking progress against the design brief, and keeping delivery on schedule.
- Deliverable Review: Review partner outputs against the design brief and Mulligan's acceptance criteria — identifying divergences early and escalating material gaps to the AI Product Director before they become rework cycles.
- Iterative Build Cycle: Facilitate the review-feedback-refine loop between the engineering partner and Mulligan's internal QC and functional stakeholders — ensuring the build converges on a production-grade output, not a minimum viable approximation.
- Vendor Discipline: Enforce Mulligan's core partnership principle: Mulligan authors the specification; the partner builds to it. Never allow the partner to define the scope or shape the design.

QC Framework & Evaluation Support

- Pre-Build QC Design: Design the QC framework for the pod's use case during Phase 1 — before the engineering build begins. Define the human review process, the deterministic checks the DS person will build, and the LLM-as-judge rubric that will evaluate output quality.
- Baseline Evaluation: Collaborate with the pod's DS person to establish evaluation rubrics, golden datasets, and accuracy thresholds that define what 'production ready' means for this specific use case.
- First Impression Discipline: Ensure the first outputs from any AI agent are seen only by the QC function before being shown to end users. The first impression determines the adoption trajectory — a rough tool shown prematurely is very hard to recover from.

Go-Live & End-User Adoption

- Shadow Run Coordination: Coordinate the shadow run period — the side-by-side comparison between AI output and the current process that converts end-user sceptics into advocates. This is non-negotiable and must be time-bounded.
- Adoption Planning: Work with the functional head to build the end-user adoption plan — ensuring the user community feels they helped shape the tool, not that it was imposed on them.
- Post-Launch Tracking: Track post-deployment metrics against the pre-implementation baseline to document the value created and surface the feedback the DS person needs to refine the model.

Required Experience & Education

- 3–5 years of experience in product management, product analysis, or a closely related role — with direct exposure to AI-enabled products, not just software delivery.
- Demonstrated ability to lead structured discovery with domain experts — extracting precise process intelligence, surfacing undocumented edge cases, and translating those findings into a clear design brief without being given the answer.
- Prior experience at an AI-native fintech, lending company, or high-velocity product environment is strongly preferred. This role requires the instinct to reimagine a process with AI rather than automate it. Candidates who have only worked in waterfall delivery environments or large enterprise software teams are unlikely to have the transformation energy this role demands.
- Experience authoring clear written product artefacts — process maps, design briefs, requirements documents, or specifications — that others have built to.
- Comfortable managing ambiguity and structuring undefined problems without waiting for direction. The ability to own a phase of work end-to-end and produce a clean output at the end of it.
- Experience coordinating with external engineering vendors or technology partners is a plus but not required. A willingness to learn partner management and hold partners to a specification is essential.
- Bachelor's Degree in Business, Computer Science, Engineering, Economics, or a related field is required.
- Master's degree is a plus but evaluated secondary to demonstrated product delivery experience and a track record of working on AI-enabled products.

Functional & Technical Skills

- AI Product Fluency: Working familiarity with how LLM-enabled products and AI agents are designed and deployed — sufficient to have a substantive conversation with an engineering partner about what is being built and why, and to identify when a proposed solution does not match the design intent. Credit or lending domain expertise is not required.
- Process Discovery & Intelligence Extraction: The ability to draw out precise process knowledge from functional SMEs who have never mapped their workflow before — asking the right questions, surfacing the exceptions, and documenting what you hear with enough precision that it can be turned into a design brief.
- Design Brief Authorship: The ability to translate discovery findings into a clear, buildable specification — defining what the AI should do, what data it receives, what output it must produce, and what 'done' means. A document someone else can build to without coming back for clarification.
- Structured Problem Solving: The ability to take an ambiguous problem — an undefined process, a blank-page use case — and structure it into phases, questions, and outputs without being told what the answer should be. The transformation instinct: asking 'what could this look like with AI?' rather than 'how do we make what exists faster?'
- Stakeholder Trust-Building: The ability to earn the confidence of functional SMEs who may be protective of their current process — creating an environment where they share the full picture honestly, including the parts that are messy or inefficient. Adoption is earned during discovery, not after launch.

Mulligan Funding is an Equal Opportunity Employer (EOE) and takes great pride in building a diverse work environment. Qualified applicants are considered for employment without regard to age, race, religion, gender, national origin, sexual orientation, disability or veteran status.

## About Mulligan Funding

## Company Overview
- **One-liner**: Mulligan Funding provides fast access to working capital loans and business term loans for small and medium-sized businesses across the United States.
- **Entity Type**: Private (Debt-financed; total funding of $135M, including $120M debt facility in 2024)
- **Headquarters**: San Diego, California, United States
- **Founded**: 2008
- **Founders**: Not publicly disclosed

## Core Business
- **Primary industry**: Financial Services – Alternative Lending / Working Capital
- **Target customers**: Small and medium-sized businesses (SMBs) in all 50 U.S. states and D.C. (B2B)
- **Mission / purpose**: Serve as a trusted, relationship-first source of working capital for the SMBs that fuel the economy. “Putting relationships before everything else.”

## Products & Services
- **Working Capital Loans**: The flagship product. Funds available as soon as the same business day after approval; repayment terms of 3–24 months; loan amounts up to $10M. Customized to each business’s situation.
- **Business Term Loans**: Alternative funding option for businesses needing larger, fixed-term capital.
- **Renewal Services**: Existing clients can access additional funding cycles.

## Market Standing
- **Valuation**: Not disclosed
- **Key Metric**: Total funding of $135M (debt and equity) – most recently a $120M debt facility in December 2024. Annual revenue estimated at $15.9M (LTM).
- **Notable Investors / Partners**: Debt financing from institutional lenders; prior venture rounds (2022: $10M, 2023: $5M) – specific investors not listed in provided sources.
- **Growth Signals**: 
  - Provided over $3 billion in cumulative funding to nearly 15,000 SMBs since 2008.
  - Investing in AI-driven underwriting tools to improve speed and consistency.
  - LinkedIn followers grew ~38% YoY (7,463 total), indicating growing brand awareness.
  - Headcount of ~90 employees (down 14.1% YoY, reflecting a potential restructuring or efficiency focus).

## Competitive Advantages
- **Speed and access**: Approval in as few as a few hours, funding as soon as the same business day.
- **Human touch**: Dedicated loan advisors review each application rather than a fully automated system, building long-term client relationships.
- **Customized terms**: Loan amounts up to $10M with flexible repayment periods (3–24 months), tailored to each business’s cash flow.
- **Track record**: 16+ years in business, trusted by thousands of SMBs across all 50 states.

## Strategic Focus
- **Technology modernization**: Building a “next version” of Mulligan Funding by combining credit expertise with modern AI to streamline underwriting and enhance customer experience while preserving human judgment.
- **Continued lending growth**: Focus on serving SMBs with working capital needs, leveraging the large 2024 debt facility to scale originations.
- **Talent investment**: Hiring data scientists for funnel analytics and optimization (roles in San Diego and San Francisco, up to $250k salary) to drive data-driven decision-making.

## Why Work Here
- **Culture**: Family-owned business with a “people first” culture that values relationships, integrity, and collaboration. Employees report above-average work wellbeing (score 74/100 on Indeed).
- **Work policy**: Hybrid – roles in San Diego and San Francisco with flexibility.
- **Compensation**: Competitive base salaries (e.g., Staff Data Scientist $180k–$250k; Recovery Analyst $73k–$101k) with recognition and rewards for talent and work ethic.
- **Engineering culture**: Opportunity to work on cutting-edge underwriting AI while maintaining a human-centric approach. Small, agile team where contributions are visible.
- **Career growth**: Emphasis on recognizing and rewarding talent; you won’t “get lost in the shuffle.”
- **Note**: AI tools are used to support parts of the hiring process (resume analysis, application review), but final decisions are made by humans.

## Sources
1. [Mulligan Funding Website](https://www.mulliganfunding.com/)
2. [Mulligan Funding Careers Page (Lever)](https://jobs.lever.co/mulliganfunding/)
3. [LinkedIn – Mulligan Funding, LLC](https://linkedin.com/company/mulligan-funding-llc)
4. [Indeed – Mulligan Funding, LLC Careers and Employment](https://www.indeed.com/cmp/Mulligan-Funding,-LLC)

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