Founding Product & Engineering Lead (Technical) – PAM
Location: Remote-first (Europe preferred)
Compensation: Equity with vesting. No salary until commercialization.
About PAM
PAM is an AI-powered product that gives founders structured, actionable feedback on their pitch. It analyses pitch content, slideware, and delivery and returns personalised feedback, investor-style questions, and targeted resources to help founders improve with each iteration.
PAM is designed to scale high-quality pitch feedback beyond live programs and cohorts, while maintaining rigour, context, and fairness. The core product is already built and live in beta.
What already exists
PAM is not an idea-stage product. Today, the system includes:
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A live web application where founders upload a pitch deck and record a pitch.
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AI-based analysis of pitch content, structure, and delivery, with scored feedback across multiple criteria.
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Investor-style questions generated from pitch content, with example answers.
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A results experience with personalised scores, feedback, and a curated resource destination.
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A production-grade technical stack running on cloud infrastructure with AI-powered analysis and transcription.
The foundations are in place. The next phase is about scaling, hardening, and evolving the product into a commercial-grade offering.
The role
We are looking for a Founding Product & Engineering Lead to take full ownership of PAM from this point forward. This is a senior, hands-on role for someone who can move comfortably between product judgement and technical execution.
You will be accountable for what gets built next, why it gets built, and how it gets shipped. This is not a strategy-only role, and not a feature factory role. It is about making PAM reliable, scalable, and commercially viable.
What you will own
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Full ownership of PAM’s product direction, technical roadmap, and prioritisation.
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Translating an existing beta product into a scalable, reliable, and monetisable platform.
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Product and engineering decision-making across frontend, backend, AI workflows, and infrastructure.
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Preparing PAM to scale from early beta usage to 1,000+ users while managing cost, performance, and reliability.
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Owning the evolution of feedback logic, scoring systems, and the results experience.
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Making pragmatic tradeoffs across accuracy, cost, speed, and user experience in an AI-driven system.
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Establishing clear product operations, including backlog management, release cadence, monitoring, and feedback loops.
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Working closely with Founderland leadership on commercialization strategy and funding readiness.
Technical context
You should be comfortable reasoning end-to-end across:
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A React-based frontend and component-driven UI architecture.
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Backend services handling file ingestion, transcription, analysis, and scoring.
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AI-driven systems using large language models for structured evaluation and feedback.
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Cloud infrastructure, deployment pipelines, and cost management in a production environment.
You do not need to have built this exact stack before, but you must be able to take responsibility for it and make confident technical decisions.
What success looks like
In the first 3 months
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You have a deep, working understanding of the existing system, architecture, and feedback logic.
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You define a clear, opinionated product and engineering roadmap focused on scale and commercial readiness.
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The current beta experience is stabilised, with obvious friction and failure points addressed.
In the first 6 months
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PAM reliably supports a significantly larger user base with predictable performance and controlled AI and infrastructure costs.
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Core feedback, scoring, and results flows are hardened, documented, and easier to evolve.
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There is a clear, testable path to monetisation or paid deployment informed by real usage data.
By 9–12 months
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PAM is no longer perceived as a beta tool, but as a product that can be sold, licensed, or funded with confidence.
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Product and engineering decisions are no longer a bottleneck to Founderland’s strategy around PAM.
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The groundwork is laid for hiring additional engineers or further spinning out the product, if desired.
Who this is for
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A senior engineer, technical product leader, or early CTO-type who wants real ownership.
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Someone comfortable joining pre-revenue, with equity as the primary upside.
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Someone who values shipping, iteration, and discipline over constant reinvention.
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Someone who is excited by the responsibility of making an AI product actually work at scale.
