Apply to Engineering Rigor

Look for clear evidence on the resume that someone is a strong engineer, not just employed at a startup. Key signals:
At least 1 is required, stronger candidates may have 2 or 3.

Education at a top engineering school (MIT, Stanford, etc.), ideally started coding before ChatGPT
Startup experience with slope -- joined early and grew with the company, or joined a well-known growth-stage company (e.g., Ramp at 200 to 600 people).
Rigorous engineering orgs -- certain FAANG teams (Amazon, Meta, select Google teams), not just any big tech role
Yellow flag: founding engineer (or founder) at a startup with no traction and no other signals of rigor -shows work ethic but not engineering quality
Jumpiness- people who moved around a ton with no good recommendations
2. Experience Filter (resume filter)
Ownership track record (required): shipped things, growing responsibility, clear progression
AI/agents experience (very nice to have): has worked on agentic systems, not just used Cursor or Claude Code
Healthcare adjacency (nice to have): experience at companies like Abridge, Ambient, Heidi, or similar
Role split: one hire leaning backend (architecture, systems), one leaning frontend (UI/design)
Experience range: 2 to 10 years; 1 year is fine with the right signals
Technical requirements: Experience in our stack
Stack: Python, FastAPI, Postgres, GCP, state-of-the-art LLMs and transcription APIs, React Native on the front end.
3. Motivation for Early-Stage (phone screen filter)
We want people who want the seed-stage experience specifically, not just "a startup." Key signals:
Desire for ownership -- felt stifled, segmented into narrow features, or separated from the customer
Hunger and grind mentality -- willing to work hard, something to prove
Life circumstances that support the commitment (lives in the city, fewer competing constraints)
Red flag: overly title-driven or negotiates excessively -- often a sign of misalignment
4. Values (phone screen + Matt interview)
Ownership & Autonomy The engineers we want have felt constrained -- segmented into a narrow feature, separated from the customer, or lost in a big machine. They're coming to Marlow because they want real ownership and a direct line to their work's impact. This is also a signal that they understand what early-stage means and are choosing it deliberately.
Hunger & Commitment Seed-stage is a grind, and we want people who are energized by that, not just tolerant of it. We're looking for candidates who have something to prove, are motivated by something bigger than themselves, and are at a point in their life where this kind of commitment is a genuine priority.
Mission Alignment Think about this in fours layers:
Layer 1: They want to see their work directly help people. This is the minimum bar.
Layer 2: They want to work on cutting edge AI that actually helps the world
Layer 3: They care about applying AI to healthcare specifically.
Layer 4: They're drawn to helping senior care or nurses specifically. Usually from personal experience.
Layer 1 is required. Layers 2 is a strong add on. Layers 3 and 4 can be meaningful differentiators but not filters.
Low Ego We're a small, close-knit founding team. We want people who are confident but don't lead with titles or self-promotion. Red flags: excessive negotiation, title obsession, more focused on the offer than the opportunity.
Growth Mindset We want people who have consistently taken on more responsibility over time -- not just held a role, but grown within it. Trajectory matters more than prestige.

1. Passion for the Role (Required) *

What specifically draws you to this Founding Engineer role at Marlow? What aspect of building AI agents to support nurses and transform nursing home operations are you most passionate about?

2. Ownership & Early-Stage Motivation *

Describe a time when you owned a meaningful product, system, or feature end-to-end — from idea through production — in an ambiguous or resource-constrained environment. What did you ship, and what did that experience teach you about ownership?

3. Agentic AI / Technical Depth *

Tell us about the most complex agentic system, LLM workflow, or production AI feature you have built. What were the hardest technical or reliability challenges you faced, and how did you solve them?

4. Mission & Healthcare Alignment *

Do you have any personal or professional connection to healthcare, nursing, senior care, or caregiving? How does that connection (or lack of one) influence your interest in working on this problem?