🚀 How to screen your first technical talent?

3 tactics to screen your first technical talent

🧢 Look for generalists

• Screen for two qualities:

  • Range: meaningful experience across frontend, backend, mobile, infra, or data

  • Learning appetite: eagerness to pick up unfamiliar stacks, tools, or workflows

• Run working sessions or small build tasks to observe adaptability, speed, and judgment under changing requirements

• Build a team that can test and iterate on ideas quickly; speed is a strong indicator of whether a startup will be successful (e.g. today’s web app can become tomorrow’s iOS/Android product)

• Deep technical expertise in areas that are essential for the business to exist should be owned by a technical cofounder instead of technical hires

💬 Thoughtful communicator

• Find someone who is thoughtful and considerate about key technical decisions (what language to use, how to design database, etc), and can communicate those well

  • Bad engineer: Make key decisions without considering the trade-offs, different stakeholders, etc

    • Beware of using an exciting but brand new language; hiring other talent who know it or want to work with it is extremely challenging and it might go out of fashion

  • Average engineer: Consider these things but do not communicate that they did and why they made the choice they did

  • Great engineer: Consider them, layout the options, trade-offs, their recommendation and have a discussion about it

    • They explain things from the perspective of the company, and in a way that you can understand

🛠 Useful > Interesting

• Find engineers who prioritize building something people want regardless of how interesting it was to build

• Stay away from engineers who optimizes for interesting work (latest technologies or problems that are technically interesting)

  •  Most software development isn’t technically interesting

• What's technically interesting may not be what's good for the product or company (e.g. building an activity hook that triggers an email campaign isn’t technically interesting or particularly fun to build but it boosts retention)

2 traps to avoid

🚨 Assuming engineers from a large company = win

• Be mindful of engineers from large companies, especially non-technology companies (banks, etc) but even places like Google, as they tend to be specialist

• Most large-scale operational expertise isn’t needed in an early-stage startup

• There’s also a culture shock to how quickly and chaotic startups are

🚨 Over-relying on outsourcing instead of having an in-house team

• Outsourced builds for initial MVP can work, but they often get rebuilt later as agencies treat products as temporary projects

• Most founders underestimate the amount of work needed to outsource (you’ll need to stay on top of them, communicate clearly and repeatedly what you want, etc)

• Your ability to iterate quickly is a core competency and you should not be outsourcing core competencies

1 tool to leverage

📖 Best practice on hiring technical talent

• Most seed stage startups have between 8 and 10 hires, with the first 3 hires often being software engineers

• Seed stage founders should spend 25- 40% of their time on hiring

• Leverage marketplaces like Karat to source for high-quality candidates

Bonus: 1 trend to spark startup ideas

📈 Managing health from home is the new norm

• More Americans will be over 65 than under 18 by 2034, but seniors are now living longer, more educated, wealthier, and far more technologically fluent

  • Within a decade, half of Americans over 60 are expected to use devices that can forecast life-threatening health risks)

• Massive structural shifts are underway:

  • Telehealth normalization: Seniors are now the highest-usage demographic, proving remote care reduces friction and expands clinical reach

  • Remote Patient Monitoring (RPM) explosion: 50M users today; market doubling by 2030—continuous vitals become the backbone of preventative care

  • Hospital-at-home: 350 hospitals already delivering acute care in living rooms, aligned with value-based care incentives

  • Caregiver collapse: 36B hours of unpaid labor worth $600B annually; shrinking labor pools create a massive coordination and automation gap

  • Home health workforce crisis: Demand up 34% by 2029; agencies need software, automation, and gig-based labor models to scale

• Opportunities across several frontiers: 

  • Remote Monitoring & Smart Health Devices: Devices that seamlessly integrate into seniors’ lives to capture data will dominate. 

  • Senior-Focused Telehealth: Purpose-built platforms that combine virtual care, remote diagnostics, and in-home follow-up, especially specialized telehealth for geriatrics, cardiology, and behavioral health.

  • Smart Homes & Assistive Tech: HVAC services, fall-proof flooring, voice-controlled appliances and AI-powered home companions 

  • Caregiver Infrastructure: Coordination apps, AI copilots for care management, and caregiver benefit platforms

  • Home Health Platforms & Marketplaces: Optimizing labor utilization, automating compliance, and offering flexible work models to stem turnover