Strong engineers leave clues before interviews.
Kristina Golovko
MindDesign
The signal system
Behaviour
How they engage with hard problems
Signal
Observable indicators of depth
Capability
What the signals actually predict
Fit
How signals align with the role
The signals that predict engineering quality are almost never on a CV. They're in how an engineer thinks, builds, and shares.
Why this matters
Strong engineers have recognisable patterns: how they approach ambiguous problems, how they communicate technical thinking, how they engage with communities, what they choose to build when no one is telling them what to build. These patterns are visible — if you know where to look.
Most hiring processes are designed to detect these signals inside the interview. But the signals exist long before: in code, writing, talks, community participation, and the questions they ask publicly. By the time an exceptional engineer is in an interview, their capability has already been demonstrated elsewhere.
Learning to read pre-interview signals transforms sourcing from a volume exercise into a precision one — and makes the interview process faster, more targeted, and more accurate.
Founder reality
Build a signal inventory for any engineering role:
What would a highly capable engineer have built, written, or contributed to that we could find publicly?
What community discussions would a strong candidate in this domain participate in?
What questions would they ask — and what does the quality of those questions reveal?
What technical trade-offs would they have encountered and how would they have documented their thinking?
What does their approach to a hard, open-ended problem look like before they've been asked to explain it?
A strong signal inventory lets you pre-qualify candidates before outreach — and ask better questions in the interview when you do meet them.
The signals
These signals are observable before a CV is reviewed or an interview is scheduled.
Output quality — what they've built, written, or contributed to
Public output is the highest-signal indicator of engineering capability. Code quality in open-source contributions, technical writing clarity, system design decisions in public projects, paper authorship — all reveal how an engineer thinks and executes, without the performance pressure of an interview.
Problem engagement — how they approach hard, ambiguous questions
Strong engineers engage with hard problems publicly: Stack Overflow answers that go beyond the literal question, issue threads where they surface non-obvious edge cases, forum posts that reframe the question before answering it. The quality of how they engage with hard problems is a capability signal.
Communication depth — how they explain technical complexity
The ability to explain complex systems clearly is a marker of genuine understanding. Technical blog posts, documentation quality, talk clarity, and even how they write GitHub issue descriptions all reveal whether an engineer can think through complexity as well as implement it.
Learning trajectory — how they've developed over time
Strong engineers grow in ways that are visible. Their early code differs meaningfully from their recent code. Their questions in forums shift from basic to structural. Their writing matures from implementation details to design reasoning. A visible growth trajectory is a strong predictor of continued development.
Common mistakes
01
Treating public output as a bonus, not a signal
GitHub activity and technical writing are primary sourcing signals — not supplementary ones. For engineering roles, output should be reviewed before the CV.
02
Discounting candidates who haven't published
Not all strong engineers have public output. Absence of public signal is not a negative signal — it means another evaluation approach is needed. Don't over-weight visible output at the expense of other evidence.
03
Reading signal without context
A large GitHub commit count is not inherently meaningful. A small number of high-quality, architecturally significant contributions signals more than thousands of minor fixes. Quality of signal matters more than volume.
04
Failing to use signal in the interview
Pre-interview signal should change the interview. If a candidate has published a specific technical piece, reference it. The quality of the conversation that follows reveals far more than a generic coding challenge.
Example scenario
A distributed systems company, Series B, sourcing for a senior infrastructure engineer. Standard pipeline producing candidates without relevant depth.
The signal-first sourcing switch
Sourcing moved from title-based LinkedIn search to output-based discovery.
GitHub: searched for contributors to relevant open-source infrastructure projects with substantive commit histories.
Writing: identified authors of technical blog posts on distributed systems topics in the past 18 months.
Community: reviewed contributors to active discussions in relevant Slack communities and technical forums.
How the signals changed the interview
Each candidate's specific output was reviewed before the call. Interviews opened with 'I read your post on clock synchronisation — I'd love to understand the trade-off you described between accuracy and fault tolerance'. Candidates were immediately more engaged.
The outcome
Signal-first sourcing produced 8 candidates with demonstrably relevant depth. 3 entered the full process. 2 received offers. Both accepted.
The interview is not where you discover who a great engineer is.
It's where you confirm what the signals already told you. Learning to read early signals makes hiring faster, more accurate, and more convincing — for both sides.
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