What the real interview was like

1. Applied on Mercor for a remote role judging AI-generated consulting work.
2. One AI-led interview conversation.

• Where an AI transformation should start: existing systems, data and processes.
• Balancing business ambition against what is technically feasible.
• AI disruption in media and entertainment, and what should be released to the public.
• Governance, ethics and guardrails.
• Open-source versus proprietary foundation models.

• Moved between themes like a practitioner, not a recruiter ticking keywords.
• Focused and probing, though without the warmth of a good human interviewer.

• →Show how you judge messy business situations, not just AI vocabulary.
• →Link boardroom strategy to hands-on knowledge of the tools.
• →Frame answers around governance and human accountability.

The post does not give a result.

1. Submitted a profile and CV.
2. Asked to enter much of the same information again.
3. An AI video interview with a blank video screen.

• A key project that met a challenge, and how you overcame it.

• Cut in when the candidate paused mid-answer.
• Did not recover well when told he had not finished.
• The question had no clear link to the role.

• →Keep answers flowing; a long pause may be taken as the end.
• →Expect general behavioural questions as well as role ones.

He came away with a poor impression of AI video interviews.

1. Applied to a part-time doctor contract, up to 20 hours a week, helping build AI for medical research tasks.
2. A recruiter sent instructions by email and followed up with a phone call.
3. One AI interview, taken the day after applying.

• Your specialty, and why you count as an expert in it.
• A recent clinical case and how you reasoned through it.
• One way language models could help in medicine, and how you would measure whether it works.

• Asked sensible follow-up questions on each answer.
• Cut in a few times while the candidate was still explaining.

• →Bring one concrete use of AI in your specialty, and say how you would score it.
• →The interviewer may cut in mid-answer. Its follow-ups still made sense.

The post does not say whether the candidate went further.

1. The AI interviewer introduced itself and explained the format.
2. Questions drawn from the CV: running programmes, research, care with limited resources.
3. Five minutes to read a case tailored to the candidate's background.
4. About thirteen minutes to answer the case aloud, with follow-up probes.

• Running disease programmes while balancing clinical care and coordination.
• Research listed on the CV.
• Delivering care with limited resources and building local skills.
• A health delivery case: weak infrastructure, culture, and working with NGOs and government.

• A spoken voice with subtitles on screen.
• Adapted as it went and asked probing follow-ups.
• Knew the context of the region in the case.

• →Expect the case to be built from your own CV.
• →Use the reading time to plan a spoken answer.
• →Be ready to go deep on each programme you list.

Placed in the talent pool; no project yet when the post was written.

1. A recruiter got in touch about a remote student role supporting AI lab projects.
2. One 20-minute AI interview, purely technical, with a visible clock.
3. Waiting for a decision.

• Backend architecture and how it was used in real projects.
• How parts of a system talk to each other, such as choosing REST or GraphQL.
• What was actually built during internships.
• Designing a financial chatbot: latency, caching, WebSockets versus polling, several data APIs.
• Security: encryption, tokens, rate limits, compliance logging, SQL injection.

• No warm-up: technical questions from the first minute.
• Cared more about why a choice was made than about definitions.
• Intense but fair. Admitting a gap and reasoning it through seemed to land well.

• →Use real project examples rather than textbook answers.
• →If you have not done something, say so and explain how you would approach it.
• →Have concrete numbers ready, such as the traffic a system handled.

Still waiting to hear back when the post was written.