Research Scientist interview prep.

A research scientist at a frontier AI lab or platform firm produces ideas + experiments + papers + safety analysis, the work that shapes what models become and how they're deployed.

What interviewers look for

  • Can the candidate defend their own research end-to-end - design, methodology, results, limitations, what they'd do differently - under 30-60 min of probing?
  • Do they read + critique recent papers with technical specificity (what's the contribution, what's missing, what experiments would strengthen) - not just summarise the abstract?
  • Can they design a clean experiment to test a hypothesis - controls, ablations, evaluation choices, statistical rigor, honest about confounds?
  • Do they reason about alignment + safety honestly - know the current literature, identify eval gaps, surface failure modes - not platitudes or dismissal?
  • Are they intellectually humble + rigorous - state limitations clearly, engage with critique, don't overclaim, debate the substance?
  • Are they research-community-aware - the paper they're proudest of, who they cite, what conferences they engage with, recent papers they've found important?

Behavioural questions to expect

  1. Walk me through your CV.

    What it tests: Story coherence + genuine fit for the research scientist seat. Teams want evidence of research thinking (paper output, deep technical work) and intellectual curiosity / humility - not pure eng IC moving without research depth.

  2. Tell me about the paper or project you're proudest of.

    What it tests: Depth + ownership + scientific rigor. Tests whether the candidate frames the research as question → hypothesis → method → results → limitations → contribution to field, not just 'we got SOTA on X'.

  3. Tell me about a weakness, a failure, or feedback you've received and worked on.

    What it tests: Self-awareness + scientific honesty. Cross-role canonical. Fake weaknesses downgrade immediately. Research mistakes (over-claimed a result, missed a confound, missed a citation, missed a safety concern) shape your research reputation.

  4. Why research scientist - and why this lab vs academia or eng IC?

    What it tests: Authentic fit for the lab-research seat: idea-generation + experiment-design + paper-writing + community-engagement, with industry-scale compute + collaborators + alignment-with-deployment that academia + eng IC can't match.

  5. Which research area would you want to focus on, and why?

    What it tests: Genuine fit + grasp of how research areas differ (capabilities training / alignment / interpretability / RL / multimodal / evals / agents). Tests whether the candidate has a reasoned preference grounded in their thread.

  6. Why this firm?

    What it tests: Whether the candidate has done the homework. Bar: firm-specific evidence from the published papers, research culture, safety posture, recent work, and researchers - not generic 'great AI lab'.

  7. How would you describe this firm's research program + safety posture in your own words?

    What it tests: Whether the candidate has internalized the firm's research priorities + safety posture - not just that it 'does AI'. Tests whether they've read the recent papers + safety publications.

  8. How does research actually create value at a frontier AI firm?

    What it tests: Whether the candidate understands frontier-lab research economics: capability research drives competitive moat + product capability; safety + alignment research is brand + regulatory + deployment-enabling; community engagement + papers + open-source build talent pipeline + brand.

Technical concepts to master

ML fundamentals - transformer + scaling + generalization

Transformer + attention
Architecture from Vaswani et al. 2017: self-attention (each token attends to all others), positional encoding, multi-head, feed-forward; the foundation of modern LLMs.
Scaling laws + compute-optimal
Empirical: loss ~ (compute, data, parameters)^-α. Kaplan 2020 established initial scaling; Chinchilla 2022 corrected (more data for given compute).
Generalization + emergent capability
Model performance on held-out / OOD data; in LLMs, certain capabilities (in-context learning, chain-of-thought) emerge at scale rather than scaling smoothly.
Optimization + training stability
Adam / AdamW + learning-rate scheduling + gradient clipping + mixed precision; training-stability tricks (warmup, qk-norm) that make large-model training feasible.

Experiment + evaluation design

Hypothesis → measurable prediction
Operationalise a research hypothesis into a measurable prediction; specify what positive / null / negative result would mean before running.
Controls + ablations
Comparison condition that isolates the mechanism (control); systematic removal / variation of components (ablations) to attribute the effect.
Confounds + bias
Variables that could make the result look real but not be: selection bias in eval data, evaluation-set contamination, proxy-metric gaming, seed variance.
Eval design - capability + safety
Capability evals (MMLU, GPQA, HumanEval, MATH) measure what the model can do; safety evals measure what it should not (refusals, jailbreaks, sycophancy, alignment).

Alignment + safety + interpretability

Alignment training (RLHF + DPO + Constitutional AI)
RLHF: human preference data → reward model → RL fine-tune. DPO: direct preference optimisation, no reward model. Constitutional AI: self-critique vs constitution, reduces human-feedback bottleneck.
Alignment failure modes
Sycophancy (agreeing when wrong), specification gaming (optimising the proxy not the goal), reward hacking, capability without safety (gain capability faster than safety), deceptive alignment (hypothetical).
Mechanistic interpretability
Reverse-engineering what individual neurons / circuits do mechanically; aims for human-understandable explanations of model behavior.
Responsible scaling + frontier eval
Frontier-lab policies committing to evaluate capabilities + safety before scaling further; pre-deployment evals + escalating safety + security measures.

Research process + paper-writing + community

Paper structure + writing
Abstract / introduction / related work / method / experiments + results / discussion / limitations / conclusion. Strong writing makes contributions land; weak writing buries them.
Peer review + venues
Major ML venues: NeurIPS, ICML, ICLR, ACL, EMNLP, FAccT, AAAI. Review process: 3-4 reviewers + meta-reviewer; acceptance ~20-30% at top venues.
Reproducibility + open-source
Strong papers publish code + data + checkpoints; reproducibility statement; open-source releases that the community can build on.
Research collaboration + advisorship
Most research is collaborative; senior researchers advise junior researchers + maintain external academic relationships.

Practical drills

  • Walk me through your strongest research paper or project end-to-end - I'll probe deeply on method, results, ablations, limitations.
  • Pick a paper from the last 6 months that's relevant to this firm's research areas. Tell me about it + critique it.
  • Design an experiment to test whether 'Constitutional AI training reduces sycophancy in a way that's not captured by standard helpfulness evals'.

Smart-question anchors

  • Research areas + investments - the firm's research priorities, recent papers, future directions
  • Safety + responsible scaling - the firm's safety posture, frontier evals, release policy
  • Researcher autonomy - directed vs bottom-up research, publication freedom, conference attendance
  • Compute + infrastructure - resources, scale, partnership with eng / infra
  • Collaboration + community - academic collaborations, internal cross-team work, publication openness

Related roles

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