I've spent a long time thinking about what actually predicts whether someone will thrive in a role — and built a framework around seven signals that live below the surface of a CV or a standard interview.
In practice: a discovery call transcript goes in. What comes out is a complete search brief — meta-layer analysis, a signal-weighted scorecard, a candidate-facing job brief, interview structure, sourcing strategy, and market intelligence. All calibrated to the specific role, company, and moment.
What follows is a real example, anonymised. The company is Vitalco. The role is senior engineer. This is what the framework produces.
Vitalco is an Australian healthtech scaleup that has spent the last three years rebuilding its foundations. What started as a scrappy direct-to-consumer wellness platform running on a founder-built monolith has been systematically modernised. The rebuild is done. Now the focus shifts: get close to the customer again, ship fast, and use AI to do something genuinely useful in personalised health.
The engineering team is sub-50, organised into self-sufficient squads. Each squad has a tech lead, product manager, and designer. The team has been deliberately kept lean. Which means every hire has to matter.
The squad model has a problem. It's bottom-heavy — strong mid-level engineers, overloaded tech leads, not enough senior ICs in between. The tech leads are doing two jobs: leading the squad and being the most senior engineer in the room. That's not sustainable as the roadmap gets more ambitious.
The goal with these two hires is to add genuine senior weight to two squads — people who can own features end to end, push back on product, and start lifting the engineers around them. Senior Engineers on a Staff-track trajectory.
AI is the other thread. The VP of Technology has already run a small pilot — a group of engineers moved to an agentic workflow, AI-first. Pull request volume roughly doubled within the first sprint. The ambition is to scale that across the team.
Frontend-leaning, AI-adjacent. Recommendation and health assessment engine. Stakes: moderate.
Backend-heavy, high reliability. Recurring wellness plans, automated reorder logic. Attention to detail is non-negotiable.
Before any signal is assessed, four questions are answered. The answers determine how the signals are weighted.
RISK 1 The Operator trap. Looks senior on paper but has spent their career executing clear requirements. They interview well. They disengage when nobody tells them what to do next.
RISK 2 The AI-sceptic. Technically strong but hasn't engaged with the new ways of working.
RISK 3 The enterprise refugee. Engineers from very large organisations where ownership was narrow and process was thick.
This isn't a job description. It's everything a candidate needs to decide whether this is right for them — including the parts that are genuinely hard.
Vitalco is a healthtech scaleup that has spent three years rebuilding its foundations. What's in front of us now is more interesting: get close to the customer again, ship things that matter, and use AI to do something genuinely useful in personalised health.
We're not a startup. There's a roadmap, there are squads, there are product managers. But we're not a big company either. The engineering team is sub-50. Your voice will be heard. If you see something broken, you can fix it.
The team is moving fast in this direction. A pilot group has already shifted to agentic workflows, AI-first. Pull request volume doubled within the first sprint. The ambition is to scale this across the whole team. Curiosity is a requirement.
You care about what you're building. You push back when something doesn't make sense. You can own a feature end to end. You're honest about what you don't know.
Your degree, or whether you have one. The specific language you've built in before. Whether you've worked in health before.
Base salary $165,000–$190,000 depending on level and background, plus superannuation. Sydney preferred. Hybrid — roughly two days in the office.
Signals are not a checklist. They are a lens. The question underneath all seven is always the same — can this specific person do this specific job, in this specific environment, with these specific constraints?
Something built outside of work because they couldn't help themselves. The content matters less than the impulse. Strong: almost embarrassed enthusiasm, explains the problem before the technology.
"Tell me about something you built outside of work in the last 12 months. Why that thing?"
Quality of decision-making when information is incomplete, time is short, or the right answer isn't obvious. The counterfactual is the tell.
"What was the option you most seriously considered and why did you reject it?"
Their name comes up before you ask about them. Gathered through sourcing conversations and candidate-consented peer references only.
Not assessed through specific questions — observed across the entire conversation. Strong: the pause before answering, clarifying questions before committing. The VP said it directly: candidates who rush into a solution without understanding the problem are failing before they've drawn anything.
Not the companies or titles — the choices. Strong: outcome language over responsibility language. Flag: large enterprise experience where ownership was narrow.
The difference between genuine depth and performed depth. Smooth narratives are a flag. Real depth has rough edges.
"Walk me through the specific decision you owned that had the most impact on that outcome."
An accurate model of reality in both directions. The gap between how they attribute credit in a success story and blame in a failure is the most reliable self-awareness signal in the conversation.
"What would be genuinely hard about this role for you? Not challenges you'd overcome — things that would require you to grow."
Three stages. Designed to be condensed — strong candidates have options and long processes lose them.
Mindset, motivation, AI baseline. One job: figure out whether this person is worth the team's time. Do not send to next stage: anyone dismissive of AI, anyone who can't give a specific answer about a constrained environment they've shipped in.
Mutual assessment. The hiring manager's job is not just to assess — it's to make the right person want to work here. Share the AI pilot story. Tell them about the engineer who moved the activation metric over a weekend without being asked.
Part A — Take-home review (20 min): candidate brings a simple working application. The point is not what they built but how they talk about it.
Part B — Live extension (35 min): deliberately complex. The point is not to complete it. Watch: do they use AI fluidly as part of how they think, or to generate code they can't explain?
Part C — System design (40 min): a real problem from Vitalco's infrastructure, simplified. Speed is a flag here, not a feature.
A senior engineer from a product-led startup or scaleup — somewhere between 30 and 200 engineering staff — who has owned features end to end and has started genuinely engaging with AI tooling in their day-to-day work.
The AI pilot story is the hook. Not "we're hiring senior engineers" — "we've already doubled pull request volume by shifting engineers to agentic workflows and we're looking for people who want to be part of scaling that."
AVOID Enterprise refugees from large organisations where ownership was narrow.
AVOID AI sceptics. There's a meaningful difference between an engineer who hasn't gone deep on AI tooling yet but is curious and open, and one who has decided it's overhyped.