AI Product Manager

Mulliganfunding; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Mulligan Funding is hiring an AI Product Manager to embed in core AI initiatives. You will lead discovery with functional SMEs, map workflows, and redesign processes with AI.

You will work closely with the AI Product Director, coordinate with specialist engineering partners, and maintain a strong design-first approach across the build cycle. The role emphasizes ambiguity navigation, rigorous discovery, and delivering production-grade AI solutions to improve speed, consistency, and decision

Qualifications

• Bachelor's Degree in Business, Computer Science, Engineering, Economics, or related field.
• Master's degree is a plus but secondary to delivery track record.
• Experience authoring clear product artefacts (process maps, design briefs, requirements documents).
• AI product fluency and familiarity with LLM-enabled products and AI agents.

Responsibilities

• Lead structured discovery sessions with SMEs to map current processes.
• Translate findings into a detailed AI capability design brief.
• Collaborate with AI Product Director and engineering partners throughout the build cycle.
• Coordinate with external engineering vendors and enforce design-first approach.

Skills

AI product management

SME discovery

Design brief authoring

Vendor management

Cross-functional collaboration

Ambiguity navigation

Structured problem solving

Education

Bachelor's Degree

Master's degree a plus

Job description

Headquartered in San Diego, Mulligan Funding serves as a leading provider of working capital (Up to $5M) to the small and medium-sized businesses that fuel our country. Since 2008, we have prided ourselves on our collaborative, innovative, and customer-focused approach. Enjoying a period of unprecedented growth, driven by the combination of cutting-edge technology, human touch, and unwavering integrity, we are looking to add to our people first culture, with highly motivated and results-oriented professionals, to push the limits of what’s possible while creating value for all of our partners.

At Mulligan, we are replacing legacy small business lending with fast, intelligent, AI-driven decisioning. Backed by 18 years of proprietary credit data and deep risk expertise, we have the institutional knowledge and technical foundation that competitors simply can't match. We are looking for builders who want to be right at the center of this work.

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 that AI-first approach across Sales, Customer Lifecycle, Finance, and Capital Markets.

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 for active AI projects, work shoulder-to-shoulder with business teams, and manage execution alongside specialist engineering partners.

We need someone who thrives in ambiguous environments and can take a messy problem and turn it into a clear design. Your main focus on day one will be sitting with functional experts, mapping out how their daily workflows operate, and redesigning them from scratch using modern AI tools.

Reporting to the AI Product Director, 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 and grow alongside our expanding team.

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.