Across the five interviews

Five real applicants, anonymized, interviewed for about twenty minutes each and graded the same way: 14 questions at the median after the two openers (from 6 to 21), each written from the applicant’s last answer, over 20 minutes at the median. Click any question, name or quote to open that interview beside the page, at that point.

Each interview, question by question

Every question in order, by what it was for. MATS's list in amber.

Real · anonymized
Real interview, anonymized: an interpretability researcher
Real · anonymized
Real interview, anonymized: an evaluation-awareness researcher
Real · anonymized
Real interview, anonymized: an interpretability-by-design researcher
Real · anonymized
Real interview, anonymized: an AI control researcher
Real · anonymized
Real interview, anonymized: a power-seeking researcher

What was asked

The questions after the two openers, grouped by what they were about, in about the order a call takes them; each question is counted once. How many of the five interviews asked each, when it first came up, and the words used. MATS's list is marked in amber where the questions served it.

Their path, and which of their work mattered most
4 of 5 · 7 questions · first at minute 3
- “Which of your interpretability projects has mattered most to you?” Most significant
- “Is there anything you think we've missed about you which you think is important to highlight?” Most significant
- “What did you first work on after leaving Google?” Their path
All 7 questions, by applicant
- Kiran
  - “Which of your interpretability projects has mattered most to you?” Most significant
- Elias
  - “Is there anything you think we've missed about you which you think is important to highlight?” Most significant
- Felix
  - “What did you first work on after leaving Google?” Their path
  - “How did you get from the prompt-optimization work at MATS to the control protocol you're researching now?” Their path
- Theo
  - “What is the next thing you're excited to work on?” Their path
  - “You mentioned reading the Sequences in 2021. How did that turn into starting the Northwestern safety group?” Their path
  - “Is there anything you think we've missed about you which you think is important to highlight?” Most significant
Follow-ups on their work
5 of 5 · 38 questions · first at minute 2
- Research in 5 of 5
- In the weeds in 5 of 5
- Taste in 5 of 5
- Ownership in 4 of 5, not with Felix
- “Could you walk me through the approaches you tried on that transfer project, including the ones that failed, where most of your time went, and what finally worked? Feel free to go into detail.” ResearchIn the weeds
- “What did you personally work on at SPAR studying eval awareness?” Research
- “What have you been working on in your independent research?” Research
All 38 questions, by applicant
- Kiran
  - “Could you walk me through the approaches you tried on that transfer project, including the ones that failed, where most of your time went, and what finally worked? Feel free to go into detail.” ResearchIn the weeds
  - “How did you measure whether the vectors that seemed to work changed Whisper's outputs beyond what injecting noise did?” In the weedsTaste
  - “How did you pace the transfer project, and who set its milestones?” Ownership
  - “What research have you worked on since that sprint, and what came of it?” Research
  - “On the weight-based polysemanticity project, what did you actually run, and what did you find?” ResearchIn the weeds
  - “Which weight vectors were you comparing when you found they became more orthogonal?” In the weeds
- Noor
  - “What did you personally work on at SPAR studying eval awareness?” Research
  - “What did you personally run to test whether those two kinds of evaluation awareness changed compliance?” In the weeds
  - “Take me through the chain-of-thought injection work in detail. What did you try that didn't work, and where did most of your time go? Feel free to go long.” In the weedsTaste
  - “When the subtractive experiment kept regenerating eval awareness, how did you decide how much longer to work on it?” Ownership
  - “Why did you treat the additive injection result as evidence about evaluation awareness beyond the inserted text itself?” QuestionedTaste
  - “I mean the inserted sentence might change compliance even if the model would never have thought it on its own. What does that experiment tell you about evaluation awareness that arises naturally?” Questioned
  - “When those phrases arise on their own, they might reflect how the model was already going to respond, rather than cause its response. What do you make of that distinction?” Questioned
  - “Apart from the workshop deadline, who set the milestones for your part of the SPAR project?” Ownership
- Elias
  - “What have you been working on in your independent research?” Research
  - “What technical research have you actually done on interpretable architectures so far?” ResearchIn the weeds
  - “What have you implemented or tested so far in the interpretable-by-design project?” In the weeds
  - “What did you observe when you tested the transformer on TinyStories?” In the weeds
  - “What did you measure on TinyStories, and what result did you get after those changes?” In the weedsTaste
  - “Could you give me the TinyStories perplexity you got after the architecture change, even roughly?” In the weeds
  - “After reading those posts, what led you to choose interpretable architectures as your first research direction?” Taste
  - “What else was on the table when you chose to work on interpretable architectures?” Taste
  - “How did you set milestones for your independent architecture project?” Ownership
  - “How do you set deadlines for the project now?” Ownership
  - “How would you tell whether the larger model is easier to interpret, rather than simply sparser?” QuestionedTaste
  - “How would you test whether a hand-traced explanation really accounts for why the model chose that token?” QuestionedTaste
  - “Why pursue interpretable architectures at MATS if you think organizing people around AI may be more urgent?” Questioned
- Felix
  - “What made you choose untrusted advice as the next thing to work on after prompt optimization?” Taste
  - “Take me through the untrusted-advice research in detail: what did you personally try, what didn't work, and where did most of your time go? Feel free to go long.” In the weedsResearch
  - “You said you were fairly sure the protocol would be safe. How did you think about harmful advice spread across many short messages?” QuestionedTaste
- Theo
  - “What does your work on control through interrogations involve?” Research
  - “How did you arrive at the idea of measuring power by switching tasks in the middle of a trajectory?” Taste
  - “If doing the original task well also makes the AI better at the switched tasks, wouldn't your training penalize useful progress as power-seeking?” QuestionedTaste
  - “Could you walk me through your interrogation project at Pivotal, from the experiments you ran to what worked and what didn't? Feel free to go into detail about your part.” ResearchIn the weeds
  - “How much did the batch protocol change the attack success rate compared with the original interrogation protocol?” In the weedsResearch
  - “How did you pace your part of the Pivotal project, and who set the deadlines?” Ownership
  - “What experiment would you run first to test whether training against your power measure reduces power-seeking, rather than just teaching the model to get a low score?” QuestionedTaste
  - “What would you compare in that experiment to distinguish genuinely reduced power-seeking from gaming the score while still doing well on the primary task?” QuestionedTaste
What they know of the field
4 of 5 · 6 questions · first at minute 13
- Context in 4 of 5, not with Felix
- “From the papers you've been following, what do you think deception probes can tell us about today's models, and where do they fall short?”
- “What do you take the inoculation prompting results to show about emergent misalignment?”
- “Which societal or organizational problem do you think most needs research attention?”
All 6 questions, by applicant
- Kiran
  - “From the papers you've been following, what do you think deception probes can tell us about today's models, and where do they fall short?”
  - “Earlier you mentioned reading a lot of LessWrong. Which ideas there have most shaped how you think about AI safety beyond interpretability?”
  - “Where do you think interpretability currently falls short when people try to use it for safety?”
- Noor
  - “What do you take the inoculation prompting results to show about emergent misalignment?”
- Elias
  - “Which societal or organizational problem do you think most needs research attention?”
- Theo
  - “Across AI safety as a whole, what do you think is the most important research problem right now, and why?”
What they read
3 of 5 · 3 questions · first at minute 13
- “Beyond the Anthropic paper you mentioned, what do you read regularly to keep up with AI safety research?”
- “Outside the system cards you worked with, what do you read in AI safety, and how regularly?”
- “What have you been reading in AI safety over the past month that's shaped that view of the current paradigms?”
Mission and timelines
3 of 5 · 6 questions · first at minute 6
- Mission in 5 of 5
- “Before ARENA and SPAR, what first drew you into AI safety, and what did you do next?” Mission
- “What led you from your ML engineering job into independent research on AI safety?” Mission
- “What led from the DeepMind safety side project to your decision to leave Google?” Mission
All 6 questions, by applicant
- Noor
  - “Before ARENA and SPAR, what first drew you into AI safety, and what did you do next?” Mission
- Elias
  - “What led you from your ML engineering job into independent research on AI safety?” Mission
  - “What changed during your ML engineering year that led you to pursue safety research independently?” Mission
  - “What safety problem were you aiming to address by building an interpretable-by-design model?” Mission
  - “You said problem selection matters a lot to you. What do you think is the highest-leverage research direction in AI safety right now?” Mission
- Felix
  - “What led from the DeepMind safety side project to your decision to leave Google?” Mission
What they would work on during MATS
4 of 5 · 9 questions · first at minute 16
- At MATS in 4 of 5, not with Felix
- “What empirical project would you want to pursue during MATS?”
- “During MATS, what research question would you pursue first?”
- “What research question would you pursue during MATS?”
All 9 questions, by applicant
- Kiran
  - “What empirical project would you want to pursue during MATS?”
  - “What AI safety question would you want that world-model project to answer?”
  - “What would finding that distinction, or not finding it, tell us about an AI safety risk?”
- Noor
  - “During MATS, what research question would you pursue first?”
  - “What safety decision would you want that result to inform?”
  - “What would you measure to tell whether filtering that data makes safety evaluations more trustworthy?”
- Elias
  - “What research question would you pursue during MATS?”
  - “What specific question about your architecture would you try to answer during MATS?”
- Theo
  - “What specific research question would you want to tackle during MATS?”
How they work with others
0 of 5 · 0 questions
- Others in 0 of 5
Not asked in these interviews.
A problem to think through on the spot
0 of 5 · 0 questions
Not asked in these interviews.

How the grading separates them

Every applicant’s scores on the rubric first; any score opens their interview with why they got it at the top. Then each row: where every applicant landed, and the top two scores side by side with the reason and the applicant’s own words. Other scores on the row are one click away.

Scores on the rubric
Applicant	Research1 to 10	Context1 to 10	Mission1 to 10	Agency1 to 10	Ownership1 to 10	Others0 to 5	Fit for MATS1 to 10
Real · anonymizedReal interview, anonymized: an interpretability researcher
Real · anonymizedReal interview, anonymized: an evaluation-awareness researcher
Real · anonymizedReal interview, anonymized: an interpretability-by-design researcher
Real · anonymizedReal interview, anonymized: an AI control researcher
Real · anonymizedReal interview, anonymized: a power-seeking researcher

Technical ability and research taste

1 to 10

9 / 10

Felix devised the published untrusted-advice protocol, tested its usefulness against trusted-only and untrusted-only baselines, and found roughly two-thirds of the performance gap closed with 16 characters per step. Their diagnosis of a slow, noisy automated-search loop led to a cheaper manual first test. No live research scenario was asked.

8 / 10

Noor’s 2026 workshop study went from probe artifacts to randomized prefills, identified regeneration bias, and chose a cross-model check over tenfold more rollouts. A 7 would lack that cut; a 9 needs a test separating inserted from natural awareness.

Other scores on the row

7 / 10

Kiran has two years of interpretability research, a preprint and a one-neuron shape experiment. They timeboxed a 20-hour steering sprint and identified its missing noise control. A 6 would only list checks; an 8 would specify a decisive world-model test.

6 / 10

Elias's ML PhD and staged MNIST-to-TinyStories work anchor the grade. They recognized an implementation-versus-design confound, checked performance, and distinguished sparsity from interpretability. Their proposed scaling question lacks a verified before-and-after result, and they could not specify a test that a hand trace faithfully explains a token.

AI safety context

1 to 10

8 / 10

Noor’s 2026 workshop study of evaluation framing, plus a mechanism and robustness objection for inoculation prompting, clears seven. Nine lacks corroboration: the interview did not probe the field’s history, other safety agendas or institutional routes.

7 / 10

Kiran distinguishes monitoring lies from measuring beliefs and argues black-box methods currently outperform interpretability for safety monitoring. This supports 7 over 6; without a weighed recent finding or wider historical map, the call does not establish 8.

Other scores on the row

5 / 10

Elias's big-picture priority judgment is stronger than their technical state-of-play account: they question technical work's dominance and explain why post-hoc interpretability may struggle. They correctly distinguish sparsity from understanding, but incorrectly infer safety guarantees from interpretability and offer no faithfulness test for a traced explanation. Recent findings and field history were not probed.

Mission alignment

1 to 10

10 / 10

Felix describes an AI-safety goal dating to college, four years at Google Gemini spent building coding experience, and a deliberate move into direct safety research. Their coauthored 2026 Redwood AI-control post corroborates the move. They explicitly identify reducing AI existential risk as their motivation.

9 / 10

The strongest evidence is Theo's trajectory from a Northwestern safety group to 2026 fellowships, control research, and repeated MATS applications, coupled with choosing power-seeking to prevent incidents and avoid catastrophe. Their governance and alignment priorities broaden that concern. The unresolved test for gaming the power score modestly limits confidence in this particular route.

Other scores on the row

8 / 10

Noor moved from quant toward safety for social impact, completed ARENA and SPAR, and coauthored a 2026 evaluation-awareness paper. A 7 would need a thinner pattern of safety-directed choices; a 9 would need clearer reasoning about competing priorities.

7 / 10

Elias moved from ML engineering toward independent safety research, worked on interpretable architectures alongside full-time employment, and weighed that route against agent foundations and social organizing. Their concern about near-term social disruption is their own. The architecture project is now back-burner, and its proposed upfront safety guarantee is unsupported.

6 / 10

Kiran's two years of interpretability research, preprint, and 20-hour MATS sprint support 6 rather than 4. Their monitoring-versus-basic-science view is their own, but curiosity drives their work; an 8 would show safety motivating the research path.

Agency and drive

1 to 10

9 / 10

Felix left a four-year Google Gemini role for safety research and turned Redwood's broad control brief into a published untrusted-advice protocol. They persisted through an unsuccessful automated search and report an active SPAR follow-up. That follow-up remains self-reported, while the career change and published design anchor the grade.

8 / 10

Noor moved from quant trading into safety research, helped pivot a SPAR project, and reports preliminary OLMo tests. Seven would fit if OLMo were only proposed; nine would need more sustained independent iteration.

Other scores on the row

6 / 10

Kiran set method-level timeboxes in a 20-hour application sprint, then initiated a one-neuron shape-model experiment. A 5 would lack that independent experiment; a 7 would need clearer sustained follow-through or world-model work already started.

Self-ownership

1 to 10

8 / 10

Felix turned Redwood's broad control brief into a published, tested protocol and drew a practical lesson from an unproductive automated search. Their move from Google to safety work supports a sustained self-directed path. The call did not test their deadline-setting or reporting cadence.

7 / 10

Kiran timeboxed methods in a 20-hour MATS sprint and owned the report shortfall; over two research years they ran a toy shape-model experiment. A 6 would follow assigned tasks; an 8 would show sustained whole-project pacing.

Other scores on the row

6 / 10

Elias started an architecture project alongside full-time work and staged local experiments from MNIST classifiers to a TinyStories Transformer. They identified an initial training failure and described changing course. Their candid account of loose deadlines and current priorities shows self-awareness, while the call establishes little about sustained pace or completed results.

interpersonal

0 to 5

Fit for MATS

How much MATS should take each applicant, out of 10, highest first, and why.

Real · anonymized9/10
Noor has personally run safety-relevant experiments, diagnosed a bias in their own results, and chosen a useful replication over a costly rescue of a weak test. Twelve weeks with a mentor could turn that judgment and their preliminary OLMo work into a strong empirical project; what holds them back is limited evidence of independently kept milestones and an unresolved distinction between inserted and naturally arising evaluation awareness.
Real · anonymized9/10
Felix left Google for direct AI-safety work, originated a published AI-control protocol, tested its usefulness, and drew a practical lesson from an unsuccessful experiment. Twelve weeks with a mentor could turn their current red-teaming direction into a substantive safety evaluation; the main uncertainty is that the call did not establish their deadline-setting or reporting habits, and the protocol's safety has not yet been tested.
Real · anonymized9/10
Theo has done hands-on AI control research, founded a safety group, and shown sustained commitment to preventing AI harms. Twelve weeks with a mentor could turn their power-seeking proposal into a useful empirical study, though they have not yet specified an independent test that distinguishes reduced power-seeking from gaming the measure.
Real · anonymized7/10
Kiran has conducted hands-on interpretability research and shows the experimental judgment to turn a question into a useful study during 12 weeks with a mentor. Their longer-term independent follow-through is less clear, and their proposed world-model study still needs a sharper test.
Real · anonymized7/10
Elias brings substantial ML training, independent architecture experiments, and a clear motivation to work on AI safety. Twelve weeks with a mentor could turn their scaling question into a measured study, but they have not reported a post-change result or a test of whether the model's explanations are faithful, and the project currently lacks firm deadlines.