Co-Founder / Founding Engineer for an agentic M&A service start-up based in SF & NYC

Our thesis is that most of the work is automatable . Sourcing, diligence coordination, buyer identification, materials drafting, and process management absorb the majority of a junior banker's hours and almost none of them require judgment a well-designed system cannot handle. If one banker can run five mandates instead of one, institutional quality advisory becomes viable at deal sizes that have never supported it.

Tasks

As Co-Founder / Founding Engineer , you will own the technical build together with the other founders — from architecture to production. You will design, build, and iterate the agent systems that run real mandates, the data layer underneath them, and the security posture required to hold up to institutional diligence.

You will:

• Own the technical direction and architecture of the first product together with the other founders — from prototype to production.
• Build the core agent systems and data layer, including company universes, buyer graphs, comps, document ingestion, and workflow context.
• Translate messy, real-world M&A workflows into powerful, intuitive product experiences, using AI-native tooling, automation, and agent infrastructure.
• Ship fast while building the right foundations for reliability, security, and institutional-grade diligence.
• Work closely with the other founders on product strategy , customer feedback, fundraising, technical diligence, and the early engineering team.

This is not a job. It is a founding seat with founding equity.

Requirements

• *You have…

Open to relocating to San Francisco. We are already in New York and are opening an office in San Francisco. We need you to come west with us!

Communication above all.** You will be in rooms with business owners, buyers, and investors, not just in a repo. You can explain tradeoffs to non-technical people, disagree directly without creating friction, and write clearly. This is the filter we care about most.

Early engineer at a startup. You have been one of the first few engineers somewhere and shipped through the messy part. You know what to build badly on purpose and what to get right the first time. Forward-deployed experience is a strong plus.

Strong technical foundation. You are strong across modern AI/LLM stacks, backend engineering, workflow automation, cloud infrastructure, and system design.

Bias to shipping. You have a track record of turning ambiguous problems into shipped product within weeks, not quarters.

High product intuition. You understand users, workflows, and experience design, not just code.

Experience with AI-enabled products. You have worked on automation, agents, LLM products, or adjacent AI-native systems.

Obsessive focus on quality, reliability, and safety. You care deeply about getting the important things right, while still optimizing for speed and iteration.

Long-term motivation. You are driven by value creation, product excellence, and equity upside — not titles or corporate engineering culture.

High standards. You thrive among A-players and in environments with intensity, ownership, and direct feedback — not in places built around consensus or politics.

We do not need banking experience.

Benefits

Our Commitment to Your Success

We are currently engaged in an active funding round with connections to prominent fintech and vertical AI investors. With existing clients and a steady flow of referrals, our product is designed to meet actual market demand rather than speculative hypotheses.

You will have the opportunity to collaborate closely with two highly experienced bankers who can provide insights into the rationale behind process structuring. Additionally, you will have complete autonomy in building and leading your engineering team.

Our Values

• We prioritize impact, efficiency, and trust — not short-term gains and funding rounds.
• We embrace continuous learning and personal growth.
• We build to enjoy the journey together — creating meaningful, high-impact work with exceptional people.

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Quelle: Statistisches Bundesamt (Destatis), Tabelle; dl-de/by-2-0; eigene Berechnung