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
Technical proficiency
35% weight
Probe depth in acoustic and language modelling: CTC versus RNN-T versus attention encoder-decoder, WFST decoding graphs, feature pipelines (fbank, MFCC), and toolkits like Kaldi, ESPnet, k2 or WeNet.
Evidence to listen for
Command of the languages, frameworks, and data tools the role actually uses
Understands correctness, performance, and failure modes, not just syntax
Has opinions on testing and can justify them
Reads and reasons about code they did not write
Five-point scoring guide
1
Poor
Cannot work independently; fundamentals are missing.
2
Needs Improvement
Weak fundamentals; output needs heavy review.
3
Satisfactory
Competent for the role; needs guidance on complex or unfamiliar work.
4
Very Good
Strong practitioner; handles hard problems with little guidance.
5
Excellent
Explains RNN-T loss, beam search pruning and lexicon or grapheme-to-phoneme choices from direct model training experience, not documentation summaries.
02
Evaluation factor
Systems and trade-offs
25% weight
Check what shipped into production: streaming latency budgets, real-time factor, quantized on-device models, endpointing, and how WER or MOS moved on a named benchmark or product.
Evidence to listen for
Reasons about scale, latency, cost, and failure before writing code
Names the trade-off they chose and what they gave up
Understands the data lifecycle end to end
Anticipates what breaks at ten times the volume
Five-point scoring guide
1
Poor
No thinking beyond the immediate task; no awareness of scale or failure.
2
Needs Improvement
Limited architectural awareness; struggles with design decisions.
3
Satisfactory
Works within an existing design; makes sound local decisions.
4
Very Good
Designs for scale and maintainability; articulates trade-offs clearly.
5
Excellent
Names a deployed engine with concrete numbers: WER reduced from X to Y, RTF under target, model size cut for edge hardware.
03
Evaluation factor
Evidence and rigour
25% weight
Test evaluation discipline: held-out test set curation, noisy and accented speech slices, alignment quality, WER versus CER choices, and how they avoid training data leakage.
Evidence to listen for
Validates results rather than trusting output
Knows how their work is measured and what a bad result looks like
Can describe a time their own analysis or model was wrong and how they caught it
Careful about data quality, leakage, and silent failure
Five-point scoring guide
1
Poor
Ships unvalidated work; no notion of how correctness is checked.
2
Needs Improvement
Validates superficially; misses obvious quality or leakage issues.
3
Satisfactory
Reasonable checks in place; rigour drops under time pressure.
4
Very Good
Validates thoroughly; can name a real error they caught in their own work.
5
Excellent
Reports per-slice error analysis on accents, noise and domain terms, and can cite a hypothesis they disproved with an ablation.
04
Evaluation factor
Collaboration and communication
15% weight
Assess how they work with linguists, data annotation teams and product owners on transcription guidelines, pronunciation lexicons, and interpreting user-reported recognition failures.
Evidence to listen for
Explains technical work to non-technical stakeholders
Gives and takes code or peer review constructively
Documents enough that the work survives their absence
Aligns with team process rather than working around it
Five-point scoring guide
1
Poor
Cannot work in a team; resistant to feedback.
2
Needs Improvement
Communication issues create rework; lone-wolf tendencies.
3
Satisfactory
Adequate team member; documentation and review participation are light.
4
Very Good
Communicates well; reliable reviewer and collaborator.
5
Excellent
Describes converting vague reports of "it mishears names" into lexicon or biasing fixes, with clear handoffs to annotation and product teams.
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