Hiring nowSpeech, audio and music intelligence

Speech Recognition Engineer

Also called ASR Engineer

A Speech Recognition Engineer builds the system that turns spoken language into accurate text for real users in real conditions. It matters because a recogniser that fails on certain accents or noise fails those customers.

What they do

They train, adapt and evaluate speech recognition models for the people and settings the product serves. Day to day that means preparing audio and text data, tuning acoustic and language models, and running tests across accents, microphones and background noise. They read error output closely, find where the system breaks, and decide whether to fix it with data, modelling or decoding changes. They also work with product teams on vocabulary, latency and what a mistake costs the user.

What a strong hire delivers

In the first months a strong hire delivers a working recogniser with a clear evaluation suite. Results are reported by accent, noise condition and device, not as one average. They name the errors that matter most to users, show a measured improvement on them, and leave the team with a repeatable way to test every future model change.

How you can tell they are good

Ask them to walk through a recogniser they built and where it failed. A strong candidate talks about slices, not a single headline number. They separate harmless errors from consequential ones, such as a wrong name, number or medical term. Ask how they built a test set, and how they would know it matched real users.

Why it is hard to fill

The title is split between ASR Engineer, speech scientist and audio ML engineer, so searches miss people. Many candidates are strong in deep learning but weak in acoustics and linguistics, or the reverse. Few have shipped a recogniser to real users and handled accents and noise. Good people are usually settled in teams that value them.

Where to find them

Look in speech technology teams, voice assistant and transcription products, call analytics firms, and accessibility and captioning companies. Strong candidates also come from university speech and phonetics research groups, hearing and acoustics labs, and music technology. Some move across from general machine learning roles after working on audio models at a model provider.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Speech Recognition Engineer

  1. Tell me about a recogniser you shipped. Which users or conditions did it struggle with, and what did you do?

    A strong answer names specific slices such as accent, noise or device, and the fix tried, with measured results. Worry if they only quote one overall word error figure or describe work they only observed.

  2. How would you build an evaluation set for a product used across many accents and noisy environments?

    Look for sampling that mirrors real users, separate held-out speakers, recording condition coverage and care over transcript quality. Worry if they reuse public benchmarks alone or ignore leakage between training and test.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are building products that depend on accurate speech recognition for a wide range of real users. We are looking for a Speech Recognition Engineer to build and improve our recogniser, and to show clearly how well it works for different accents and noise conditions.

What you will do

  • Train, adapt and evaluate speech recognition models for our users and settings
  • Build and maintain evaluation sets covering accents, devices and noise
  • Report word error and consequential transcription errors by slice
  • Analyse errors and decide between data, modelling and decoding fixes
  • Improve language modelling for domain vocabulary and names
  • Work with product on latency, quality targets and release decisions

What you will bring

  • Shipped or substantially built a speech recogniser, with examples you can discuss
  • Strong deep learning skills applied to speech and audio
  • Experience designing evaluations and reporting results by slice
  • Working knowledge of acoustics and language modelling
  • Comfortable with audio toolchains and Transformer-based models
  • Clear written explanation of errors to non-specialists

Nice to have

  • Background in linguistics, phonetics or localisation
  • Experience with low-resource languages or accents
  • Experience running listening tests

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Speech and audio modellingAdvancedRecognise, synthesise or analyse audio
  • Deep learningAdvancedBuild and diagnose trainable neural models
  • Linguistics and localisationWorkingAdapt language behaviour across cultural and linguistic contexts
  • Model evaluationWorkingMeasure capability and failure modes credibly

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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