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Strong AI Candidates Don’t Buy Vision. They Buy Clarity.
Something I’m seeing more clearly in AI/ML/robotics hiring right now:
The strongest candidates are not just asking:
“Is this company doing AI?”
They’re asking:
“Is this a real technical problem?”
“Will I own something meaningful?”
“Is the infrastructure serious?”
“Are the founders clear on what needs to work in the next 90 days?”
“Will I be surrounded by people who can actually build?”
That last one matters more than most teams realize.
A lot of early-stage companies still try to attract AI talent with broad vision language:
“We’re transforming the future of X.”
“We’re building agentic workflows.”
“We’re applying AI to a massive market.”
That might get attention.
It rarely closes the candidate.
The best AI/ML/robotics engineers are looking for evidence.
They want to know whether the company understands the real work after the demo: messy data, latency, deployment constraints, model behavior, hardware limitations, infrastructure tradeoffs, customer weirdness, and all the unsexy problems that separate a prototype from a product.
This is where founders accidentally lose strong candidates.
Not because the company is weak.
Because the role is too vague.
The must-haves are blurred with the nice-to-haves. The candidate can’t tell whether they’re joining a serious technical mission or becoming the person responsible for making the AI slide deck true.
Before opening another AI/ML/robotics role, I’d pressure-test five things:
What must this person make true in the first 90 days?
What technical problem will make a great engineer lean forward?
What proof can you show that the problem is real?
Which requirements are actually essential on day one?
Which ones are just founder anxiety in bullet-point form?
This applies beyond technical hiring too.
Consultants, creatives, operators, and founders all deal with the same thing:
Strong people don’t just buy the opportunity.
They buy the clarity.
If your role, project, or client brief feels fuzzy, strong people hesitate.
If it feels clear, specific, and grounded in real outcomes, they engage.
If you’re hiring for a hard technical role right now and your pipeline is noisy, I’m happy to help you pressure-test the role before you spend another month chasing the wrong people.
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It can't design without your direction.
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Congrats on the launch. Curious how you handle candidates who use AI in the assessment itself. Do you score the process or just the output? That feels like the hardest product call in this space.
Building MLOpsLab has taught me something interesting:
Most workflow problems aren't caused by missing automation.
They're caused by missing visibility.
A form gets submitted.
A webhook fires.
An API responds.
Everyone assumes the process worked.
Then days later someone discovers a missing lead, duplicate record, failed sync, or unexplained gap.
Lately I've been spending time building and testing workflow reliability scenarios around:
• Intake validation
• Duplicate event handling
• Failure detection
• API and database integrations
I'm documenting what works, what fails, and where assumptions break.
If you've ever spent hours tracking down a workflow issue that "should have worked", I'd be interested in hearing what the failure looked like.
#automation #n8n #api #workflow #supabase #buildinpublic
The hardest one to catch is the green run that did nothing. An API returns 200 with an empty list, every later step runs on zero items, and nothing errors. A cheap guard: count items in and items written at the end of the run, and alert when they differ. Are you testing that case in MLOpsLab, or mostly hard failures?