Why pre-screen speech engine developers before the technical panel
Published accuracy figures come from clean recordings by speakers the system was trained on. Real audio has a fan running, two people talking over each other, an accent underrepresented in the training data and a cheap microphone. Developers worth hiring quote their own measurements in those conditions. A short screen asks what accuracy they measured with background noise.
What actually matters when screening Speech Engine Developer candidates
- 01
Technical proficiency
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.
- 02
Systems and trade-offs
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.
- 03
Evidence and rigour
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.
- 04
Collaboration and communication
Assess how they work with linguists, data annotation teams and product owners on transcription guidelines, pronunciation lexicons, and interpreting user-reported recognition failures.
Pre-screening questions to ask Speech Engine Developer candidates
12 questions grouped by what they test. Ask the same set in every screen and score answers on a consistent scale, or send them as an async video screen and compare answers side by side.
Processed real audio
3 questions01Do you have experience developing automatic speech recognition systems?
Listen forSystems built and evaluated on their own audio, with error rates stated for real conditions.
Experience limited to calling a hosted service, or accuracy quoted from published benchmarks.
02Do you have experience developing text-to-speech systems?
Listen forSynthesis work with naturalness evaluated by real listeners, and pronunciation handling described clearly.
Synthesis quality judged by the developer alone, or pronunciation exceptions never handled.
03Have you built tools or products using speech recognition?
Listen forProducts used by real people, with the failure modes users encountered described honestly.
Demonstrations only, or no experience of users hitting recognition failures.
Signals and models
4 questions04Are you experienced with audio signal processing?
Listen forSampling, filtering and feature extraction understood, with microphone and channel effects considered.
Audio treated as generic input, or recording conditions not considered as a variable.
05What machine learning approaches have you used in this work?
Listen forModel choices explained with data requirements understood, and training data composition considered.
Models applied without regard to training data coverage, or accent bias never examined.
06How does phonetics inform your work on speech systems?
Listen forPhonetic knowledge applied to pronunciation, accent variation and the handling of confusable words.
Phonetics dismissed as unnecessary, or pronunciation problems handled only by retraining.
07What experience do you have with deep learning in speech applications?
Listen forArchitectures used with the compute and data cost understood, and fine-tuning applied where sensible.
Models trained from scratch without need, or transfer learning options not considered.
Meets real-time limits
3 questions08Do you have experience developing for real-time applications?
Listen forLatency budgets met and measured, with streaming recognition and buffering handled properly.
Real time claimed from batch processing, or latency measured only as an average.
09What do you consider the biggest challenges in building a speech system?
Listen forNoise, accent variation and overlapping speech named as the practical limits from experience.
Challenges described as compute cost alone, or accuracy assumed solved by larger models.
10Can you describe a challenging problem you faced and how you solved it?
Listen forA specific failure such as poor accuracy for a user group, diagnosed and addressed with data.
Problems attributed to user behaviour, or accuracy gaps between groups never investigated.
Audio data protected
2 questions11How have you handled the security and privacy of audio data?
Listen forConsent for recording, retention limits and access control all treated as requirements.
Recordings retained indefinitely for training, or consent scope not checked before reuse.
12Describe your experience collaborating with a team on this kind of project?
Listen forWork alongside linguists, product and engineering, with their contribution to each described.
Work done in isolation, or product requirements received without any discussion.
How to score responses
Score every candidate on the same four criteria immediately after the screen. At this stage you are shortlisting for panel interviews, not making the final call.
Technical proficiency
35%5Explains RNN-T loss, beam search pruning and lexicon or grapheme-to-phoneme choices from direct model training experience, not documentation summaries.
Systems and trade-offs
25%5Names a deployed engine with concrete numbers: WER reduced from X to Y, RTF under target, model size cut for edge hardware.
Evidence and rigour
25%5Reports per-slice error analysis on accents, noise and domain terms, and can cite a hypothesis they disproved with an ablation.
Collaboration and communication
15%5Describes converting vague reports of "it mishears names" into lexicon or biasing fixes, with clear handoffs to annotation and product teams.
Published accuracy comes from clean recordings by familiar speakers. A one-way video screen asks for their noisy numbers.
Try it on HirevireScreening FAQ
Process basics
How long should a pre-screening round for this role take?
Fifteen minutes across eight to ten questions, answered async. Enough to establish systems they built, test their audio and modelling depth, and check real-time and privacy handling.
What mix of skills should I expect?
Signal processing alongside machine learning. Someone with only the modelling side will treat audio as generic input and miss most of what determines accuracy in the field.
Evaluating answers
What is the strongest signal when screening this role?
Accuracy measured in noisy conditions on their own data. Developers with production experience have those numbers. Anyone quoting a published benchmark has not deployed to real users.
How do I judge their privacy practice?
Ask what happens to recorded audio. Real answers cover consent, retention and access control. Voice recordings are personal data and are frequently handled far too casually.
Go deeper on this role
Sanat has been hiring since 2012 and watching the recruitment industry change up close ever since, and turned that screening process into Hirevire's video screening platform. LinkedIn
