Interview scorecard template

Artificial General Intelligence (AGI) Researcher interview scorecard

Evaluate Artificial General Intelligence (AGI) Researcher candidates across 4 weighted areas: theoretical command, from theory to hardware or code, research judgement, and explaining it to non-specialists. Theoretical command leads at 35%, so probe depth on transformer internals, scaling laws, RLHF and RL objectives, mechanistic interpretability; ask them to defend a position on. Use the rubric to compare role-specific evidence consistently.

See AI scoring
frontier research deep techagialignmentdeep learningscaling laws
TL;DR
For theoretical command, look for evidence the candidate argues precisely about optimisation dynamics, loss curves and inductive bias, citing specific papers and naming where the theory breaks down. For from theory to hardware or code, look for evidence the candidate names runs they owned end to end, from data pipeline to eval harness, with parameter counts, compute budgets and released artefacts. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework

What to assess and how to score it

Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.

01
Evaluation factor

Theoretical command

35% weight

Probe depth on transformer internals, scaling laws, RLHF and RL objectives, mechanistic interpretability; ask them to defend a position on emergence or sample efficiency with citations.

Evidence to listen for

  • Explains the underlying theory at the level the role demands, and can go a layer deeper when pushed
  • Knows which results are established and which are contested
  • Distinguishes their own contribution from the field's
  • Comfortable saying where the theory runs out

Five-point scoring guide

1
Poor

Recites terminology without understanding; cannot go one layer deeper.

2
Needs Improvement

Surface familiarity; conflates established results with speculation.

3
Satisfactory

Solid grasp of the core theory; thin at the frontier.

4
Very Good

Strong command; separates settled results from open questions.

5
Excellent

Argues precisely about optimisation dynamics, loss curves and inductive bias, citing specific papers and naming where the theory breaks down.

02
Evaluation factor

From theory to hardware or code

30% weight

Check what they actually trained: model sizes, cluster and framework (JAX, PyTorch FSDP, Megatron), datasets curated, evals built, and open-sourced code or checkpoints.

Evidence to listen for

  • Has built, simulated, or run something real, not only published about it
  • Knows the gap between the idealised model and the actual apparatus or system
  • Names the practical constraint that dominates in real conditions
  • Can describe a result that did not match prediction

Five-point scoring guide

1
Poor

Purely theoretical; no contact with implementation.

2
Needs Improvement

Some exposure but unaware of practical constraints.

3
Satisfactory

Has implemented work; understands the main real-world limits.

4
Very Good

Strong practical record; articulate about theory-versus-reality gaps.

5
Excellent

Names runs they owned end to end, from data pipeline to eval harness, with parameter counts, compute budgets and released artefacts.

03
Evaluation factor

Research judgement

20% weight

Test how they pick problems: killed experiments, negative results, choosing between a scaling ablation and an architectural bet under fixed GPU-hours.

Evidence to listen for

  • Chooses problems by tractability and value, not novelty alone
  • Knows when to abandon a line of work
  • Reads and evaluates others' results critically
  • Can say what would falsify their own approach

Five-point scoring guide

1
Poor

Chases novelty; no sense of tractability or when to stop.

2
Needs Improvement

Weak problem selection; persists past the point of value.

3
Satisfactory

Reasonable judgement within a defined programme.

4
Very Good

Selects problems well and knows when to abandon a line.

5
Excellent

Describes a research direction they abandoned early with the evidence that triggered it, and how they reallocated compute.

04
Evaluation factor

Explaining it to non-specialists

15% weight

Assess how they brief policy staff, safety reviewers or funders on capability jumps and risk without hype or jargon; ask for a blog post or talk.

Evidence to listen for

  • Explains the work to an engineer, an executive, or a funder without either mystifying or dumbing it down
  • Writes clearly
  • Collaborates across disciplines
  • Makes the case for resources in terms the audience cares about

Five-point scoring guide

1
Poor

Cannot communicate outside their specialism.

2
Needs Improvement

Explanation is either impenetrable or hollow.

3
Satisfactory

Adequate with technical peers; less effective with lay audiences.

4
Very Good

Explains clearly to specialists and non-specialists alike.

5
Excellent

Explains a result like grokking or deceptive alignment plainly, separating measured findings from speculation, and states uncertainty ranges.

Evidence-led prompts

Interview questions for a Artificial General Intelligence (AGI) Researcher

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Do you have hands-on experience with research projects in this area?

  2. 02

    What types of systems have you worked with in your research?

  3. 03

    Do you have published research or contributions in this field?

  4. 04

    Can you explain the methodology of a study you have conducted?

  5. 05

    Can you describe your experience with machine learning methods?

See the complete Artificial General Intelligence (AGI) Researcher question set
Put this rubric to work

Score every candidate against the same standard

Add these weighted factors to Hirevire and let AI evaluate recorded answers against your rubric.

Explore AI Scorecards