About you and your background
01Tell me about your research background and the problem you care most about.
What they are checking: The interviewer wants a clear research identity and a question you can articulate without notes.
Example answer
My doctorate was on representation learning for structured data, and my thesis work looked at how pretraining objectives shape what a model can transfer. Since then I have spent three years in an industrial research group working on evaluation and data curation for language models, which grew out of a frustration: we kept seeing methods reported as improvements that did not survive a careful evaluation. The problem I care most about is how to know whether a model is actually better. That sounds narrow, but it touches data contamination, benchmark design, statistical power in evaluation and the way models are compared across scale. I have published on evaluation methodology and on data selection, and I have built evaluation infrastructure that other teams adopted. I would describe myself as a careful empiricist who cares about the questions more than the architectures.
02Walk me through your most significant result and what you would do differently now.
What they are checking: They want depth on your best work and the self-criticism that shows you have kept thinking about it.
Example answer
The result I would point to is a data selection method that let a model trained on a filtered subset match the performance of a model trained on the full corpus, with a substantial reduction in compute. The idea was simple: use a small proxy model to score data for usefulness on a held-out target, and train the large model on the top-scoring fraction. What made it hold up was the evaluation: we tested across several model sizes, controlled for training tokens, and checked for contamination between the target set and the training data. What I would do differently is spend less time on the scoring model, which turned out to matter less than the choice of held-out target, and more on characterizing which data got removed. A reviewer asked that question and our answer was thinner than it should have been.
03Which of your papers or projects did not work out, and what did you learn?
What they are checking: This checks for honesty about failure and whether you extract method from it rather than just moving on.
Example answer
I spent most of a year on a project to make a model's internal representations more interpretable by adding a structured bottleneck during pretraining. The early results on small models looked promising, and I presented them internally. When we scaled to a moderately sized model, the bottleneck cost a meaningful amount of downstream performance and the interpretability gains mostly disappeared: the model learned to route information around the constraint. The paper was never written. What I learned was to test the scaling behavior earlier, since a method that only works at small scale is often a method that exploits small-scale weaknesses; and to define in advance what result would make me stop. I also learned the value of writing up negative results internally. That document saved a colleague from starting a similar project later.
04How do you choose which problems to work on?
What they are checking: They want a deliberate method for problem selection, since taste in problems is most of what separates researchers.
Example answer
I use three filters. First, does the question matter if the answer is yes: would a positive result change what people build or believe. Second, can I get an answer with the resources I actually have, and is there a cheap first experiment that would tell me whether to continue. Third, do I have an angle that others do not, whether from data, infrastructure or a perspective from a different field. I keep a list of questions and I revisit it monthly, and I try to have one main project and one small exploration running. I also pay attention to which questions practitioners are asking that researchers are not, because that gap is where useful work lives. And I avoid problems that are only interesting because they are fashionable; those are crowded and the results date quickly.
05How do you keep up with the literature without drowning in it?
What they are checking: This reveals whether you read critically and deeply rather than skimming for novelty.
Example answer
I do not try to read everything. I follow a small number of researchers whose judgment I trust and I read what they highlight. I keep a running document of questions I am working on, and I read papers that bear on those questions closely, including their appendices and code, while skimming the rest. I run a reading group at work where each person presents one paper a month, which distributes the coverage. When a result seems important I try to reproduce a small version of it, because that tells me more about whether it is real than the paper does. And I have stopped reading abstracts as a filter, since they are written to be exciting; I read the experimental setup and the tables first. The goal is depth on a few threads rather than breadth on all of them.
Research depth and method
06Explain a recent development in your area to me as if I were a strong researcher in a different field.
What they are checking: They are testing whether you understand a development well enough to explain its substance without jargon.
Example answer
I would pick the shift toward evaluating models on what they can do rather than on static benchmarks. A few years ago you would measure a language model by its accuracy on fixed question sets, and that worked until models started seeing those sets in training and until the interesting capabilities became multi-step tasks that a single answer cannot capture. The field has moved toward evaluations that are generated fresh, that involve interaction with tools or environments, and that grade process as well as outcome, sometimes using other models as graders with human calibration. The analogy to another field is the move from a written exam to a practical one: harder to administer and score, but much harder to game. The open problems are the reliability of model graders, the cost of running these evaluations, and how to compare results across labs when everyone builds their own.
07How do you design an experiment so that a positive result is actually convincing?
What they are checking: This checks for pre-registration habits, fair baselines, variance reporting and ablations.
Example answer
I decide what would falsify the hypothesis before I run anything, and I write it down. Then I make the comparison fair: same data, same compute budget, same tuning effort for the baseline as for the method, because an undertuned baseline is the commonest source of fake positives. I run multiple seeds and report variance, so a difference within noise does not become a claim. I check that the effect holds across at least two scales or settings, since a result that appears only in one configuration is probably an artifact. I look for the mechanism: if the method claims to help by doing X, I run an ablation that removes X and confirm the gain disappears. And I predict the result before I see it; if it surprises me, I look for a bug first. Convincing means I tried to break it and could not.
08Describe how you would evaluate a new training method against the baselines fairly.
What they are checking: They want the details of matched budgets, tuning, contamination checks, scale trends and simple baselines.
Example answer
Fair evaluation starts with matching what is matched in practice: total training compute, data, and the amount of hyperparameter search. I would sweep learning rate and other sensitive parameters for both the baseline and the new method with the same budget, and I would report the best of each, along with the sensitivity. I would use the same evaluation sets, held out from any tuning, and check for contamination against the training data. I would report results at multiple model sizes and fit the trend, because a method that helps at small scale and fades at large scale is a different finding from one that holds. I would run several seeds and show intervals. I would also include the simplest baseline that someone might argue is enough, since if the method only beats a weak baseline, that is what the reader needs to know.
09What is a common flaw you see in evaluation of large models, and how do you avoid it?
What they are checking: This tests critical awareness of contamination, unequal tuning, missing uncertainty and grader bias.
Example answer
The most common flaw is reporting a single number from a benchmark that the model may have seen during training, without checking for contamination or reporting uncertainty. A close second is comparing models that were tuned unequally, or comparing against published numbers that were obtained under different conditions. I avoid these by maintaining held-out evaluation sets that are created after the training data cutoff or generated procedurally, by running contamination checks with n-gram and embedding overlap, by re-running baselines myself under matched conditions, and by reporting confidence intervals from bootstrap over examples. I also break results down by category, because an aggregate can hide a regression on the cases that matter. And I treat model-graded evaluations with caution: I calibrate the grader against human judgments on a sample, report the agreement, and check whether the grader favors outputs from its own model family.
10How do you decide when to scale an experiment up, and when to stop?
What they are checking: They want disciplined use of compute: scaling ladders, pre-set decision points and the ability to stop.
Example answer
I scale up when the small-scale result is clean, when the mechanism makes sense, and when I have a specific prediction for what the larger run should show. Before spending a large budget, I run a scaling ladder of two or three sizes and check that the effect is stable or growing; if it is shrinking, I stop and understand why. I set a budget and a decision point in advance: at this point, if the metric is not above this line, we stop. I also ask whether the result would change anything if it held at scale, because some findings are interesting and not useful. Stopping is harder than starting, and I have learned to treat sunk time as sunk. A written record of why we started and what we expected makes the decision to stop less personal and easier to explain to the team.
Behavioral and teamwork
11Tell me about a collaboration where you were not the lead and what you contributed.
What they are checking: This checks whether you can contribute substantively without owning the project or the credit.
Example answer
A colleague led a project on long-context evaluation and I joined as one of four contributors. My part was the contamination analysis and the statistical treatment of the results, which was not the glamorous piece. I built a pipeline to check each evaluation example against the training corpora we had access to, found that a meaningful portion of one widely used benchmark overlapped with training data, and reworked the analysis to report results on the clean subset with proper intervals. That changed one of the paper's conclusions, and the lead was glad it changed before review rather than after. I also reviewed every figure for whether it supported the claim in the text. A good collaborator makes the result more trustworthy, not just bigger, and I was happy to take the part of the work that did that.
12Describe a time you disagreed with a co-author or a reviewer.
What they are checking: They want to see intellectual honesty: a willingness to test the other view rather than defend your own.
Example answer
A reviewer argued that our data selection method was just a form of deduplication and that the gains would vanish against a strong deduplication baseline. My co-author wanted to rebut it in the response; I thought the reviewer might be right. We disagreed for a day. I proposed running the experiment rather than arguing, since we had the infrastructure, and my co-author agreed. The result was that deduplication captured about half of our improvement and our method captured the rest on top of it. We added the baseline to the paper, revised the claims to be more precise, and thanked the reviewer. The paper was better for it, and my co-author and I now have a rule: if a disagreement can be settled by a run that takes less than a week, run it before anyone writes a paragraph defending a position.
13Tell me about a time you had to hand off research to an engineering team.
What they are checking: This tests whether your research is usable by others and whether you take responsibility for the transfer.
Example answer
We had a retrieval method that improved factual accuracy in our research setup, and the product team wanted it in a shipping assistant. The research code was a set of scripts with hard-coded paths. I spent two weeks turning it into a documented package with a clean interface and a test suite, and I wrote down what the method assumed about the data and where it would fail, such as on queries with no relevant documents. Then I sat with the engineering team through the integration, and I built an evaluation they could run in their pipeline so the gains would be measurable in production. The method shipped with a modest but real improvement, and the engineering team kept using the evaluation harness for later changes. The handoff taught me that a research result is only half done until someone else can run it.
14Describe a time you were wrong about a research direction.
What they are checking: They want to see whether evidence can change your mind even when it conflicts with what you enjoy.
Example answer
Early in my industrial research work I was convinced that the path to better reasoning was architectural, and I spent several months on modified attention mechanisms. Meanwhile, colleagues working on data and training procedure were getting larger gains with unmodified architectures. I resisted the evidence for a while, partly because the architecture work was more intellectually satisfying. What changed my mind was a controlled comparison I ran myself: the best of my architectural changes was worth less than a modest improvement in data quality at equal compute. I redirected my work toward data and evaluation, which is where I have been productive since. I try now to hold research directions more loosely, and to ask periodically whether I am working on something because the evidence supports it or because I enjoy it. Both matter, but the first has to win.
15Tell me about mentoring a student or a junior researcher.
What they are checking: This reveals whether you can develop others, protect scope and make negative results safe to report.
Example answer
I supervised a research intern on a project about evaluation contamination. She was technically strong and was trying to do too many things at once, so my first job was to help her narrow to one clear question with a result that could be finished in twelve weeks. We met twice a week, and I made a point of asking what she had ruled out, not just what she had found, so that negative results counted. Midway through, an experiment failed for a week because of a data pipeline bug; I let her find it rather than fixing it, and I told her that was deliberate. She finished with a workshop paper and a tool the team still uses. What I learned about mentoring is that the most useful thing I can do is protect scope and make it safe to report what is not working.
Your fit and the role
16Why this lab or company rather than staying in academia or joining a larger lab?
What they are checking: They want a specific reason grounded in what this team offers that the alternatives do not.
Example answer
Because of the proximity to real systems and real data. In academia I had freedom but I could not test at the scale where the questions I care about become sharp, and the evaluation problems I work on look different when the model is in front of users. A larger lab would give me scale, but from what I understand of this team, the research here is close enough to the product that results get tested in practice within months, and small enough that an individual's judgment about what to work on matters. I also read the group's recent work and it takes evaluation seriously in a way I recognize. I want to work where careful empirical work is valued, where there is compute to answer questions properly, and where the answers change what gets built. This looks like that place.
17How do you balance publishing with work that stays internal?
What they are checking: This checks that your expectations about publishing match what the organization can offer.
Example answer
I think of publishing as one way to check that work is real, and not the only one. Internal work that changes how a model is trained or evaluated is valuable even if it never appears in a paper, and I have done plenty of it. What I would want is clarity: which projects are expected to be published, which are internal, and a process for deciding, so that I am not surprised late. I also think the discipline of writing for external review makes internal work better, so I would write internal results up to the same standard. Where publishing matters most to me is in evaluation methodology, because the field benefits from shared standards and my credibility with other researchers depends on being visible. I would expect a mix, and I am comfortable with the balance tilting toward internal work when the results are commercially sensitive.
18What would your first research project here be?
What they are checking: They want a bounded, realistic first project that shows you have thought about the team's actual needs.
Example answer
From what I understand of the team's current work, I would start with the evaluation infrastructure, because it is the foundation for everything else and it is what I know best. The first project would be to audit the evaluation sets the team relies on for contamination and for statistical power, and to build a held-out suite that is regenerated on a schedule so it cannot go stale. That is a bounded project with a clear deliverable and it would teach me the team's data and models quickly. In parallel I would look for the question where the team disagrees most, since that is usually where an experiment would be most valuable. I would want to align this with the team in the first weeks rather than arrive with a fixed agenda; the right first project depends on what you already have.
19How do you handle a research agenda that gets redirected by product needs?
What they are checking: This tests whether you can be useful to the company without losing your research identity or resenting it.
Example answer
It depends on what redirected means. If a product need turns out to be a real research question, I am happy to follow it, and some of my most useful work came from a practical problem someone brought to me. If the redirection means dropping a promising line of work to do routine engineering for a deadline, I would want to understand why and negotiate: perhaps a bounded contribution rather than an open-ended one, or a handoff to someone better placed. What I would not do is quietly resent it or quietly ignore it. I think a research team inside a company earns its freedom by being useful, and being useful sometimes means answering the question the company has rather than the one I have. Over a year I would expect the balance to include both, and I would raise it if it did not.
20Where do you want to be in five years?
What they are checking: They are checking that your ambitions fit the trajectory this team can support.
Example answer
I want to be known as the person who made evaluation trustworthy for a lab or a company: the methodology, the infrastructure and the culture of testing claims before believing them. I would like to have led a small research group by then, with a couple of results that changed practice in the field and a track record of research that reached products. I care about the questions more than the title, but I do want to shape which questions a team asks, which means some leadership. In the nearer term I want to get better at experiments at the largest scales, since that is where my current work needs to be tested, and to publish work that sets standards rather than only reports results. This role is on the path because it puts me in a team with the scale and the seriousness to do that work.
Questions worth asking them
- How are research projects chosen here, and how much do individual researchers set their own direction?
- What compute can a researcher expect for an exploratory project, and how is larger-scale work approved?
- How do results move from research into the product, and how often has that happened in the past year?
- What is the policy on publishing, and what has the team published recently?
- How do you evaluate researchers, and what does a good year look like?
How to prepare
- Prepare a research talk of about thirty minutes on your best work, with the negative results and the open questions included, and rehearse the question period.
- Reread your own papers, including the appendices, because interviewers will ask about the details and the baselines.
- Read the team's last two or three papers closely and come with a specific question or critique about each.
- Practice explaining one recent result in your field to someone outside it in five minutes.