Apple ($AAPL) is facing scrutiny today for the way it's been collecting keeping audio recordings of customers' voice interactions with Siri, its digital assistant found in virtually all of its devices. The company was criticized for using contractors to listen to conversations people were having with Siri, ostensibly to add features and fix bugs based on real-world data.

But is it only contractors who are accessing customers' voice files? Hiring data suggests that's not the case.

Apple apologized and promised a software update to close the loop and allow customers to control how their voices are being used. Meanwhile, hiring data at Apple reveals that not only is the company hiring for Siri at its highest levels, but it's laser-focused on bringing on engineers and scientists who will use information collected from customer voice files to improve Siri's intelligence.

As of this week, Apple has 216 job openings with "Siri" in their titles — the highest level of hiring we've seen since we began tracking in 2016.

This rate of hiring — up 73% since April 2019 — far outpaces that of hiring overall at Apple, which is up 20% overall in the same timeframe.

While the privacy concerns stem from Apple reportedly using contractors to listen to conversations, given the amount of hiring Apple has been doing for Siri, one has to ask what the company is doing with the findings of said contractors. Some answers to that can be found in who Apple is hiring for Siri.

For instance, Apple is on the hunt for an "NLP ML Engineer, Siri Intelligence". NLP stands for "Natural Language Processing" while ML stands for "Machine Learning". This role will charged with using natural language data — ostensibly collected from real-world interactions with Siri — to develop Siri's intelligence and neural network. 

The company is also hiring for what it calls its "Siri Speech" team. For that team, it's looking for a SIri "Speed Scientist / Engineer" who will apply machine learning against speech processing. Again, one has to ask where the data for these engineers will come from.

It's also looking for "Data Scientists" who will work with "human-labeled data collection" in the "domain of linguistic annotation". We have to assume here that the human-labeled data is coming from some of these real-world conversations that are being archived by the aforementioned contractors who have been charged with listening in on customers.

Of the 216 job openings listed on August 27 on Apple's careers site, 17 include the term speech in their descriptions. They include: 


Location Text

Siri - Data Engineer

Santa Clara Valley (Cupertino)

Siri - Senior Engineering Program Manager, Siri Speech Accuracy

Santa Clara Valley (Cupertino)

Siri - Machine Translation R&D Scientist


Siri - International Linguist -Hindi/Indian English


Siri - Senior Machine Learning Engineer, Text-to-Speech


Siri - iOS Engineer

Santa Clara Valley (Cupertino)

Siri - International Linguist (Cantonese)

Hong Kong

Siri - International Computational Linguist (Turkish)


Siri - International Linguist (Japanese)


Siri - Machine Learning Engineer


Siri - Machine Learning Engineer / Scientist

Santa Clara Valley (Cupertino)

Siri - Software Engineer, Speech

Santa Clara Valley (Cupertino)

Siri - Engineering Program Manager, Siri Speech Program

Santa Clara Valley (Cupertino)

Siri - Software Engineer, Dev Ops (Text-to-Speech)

Santa Clara Valley (Cupertino)

Siri  - iOS Engineer

Santa Clara Valley (Cupertino)

Siri - Senior Machine Translation R&D Scientist

Santa Clara Valley (Cupertino)

Siri - Speech Scientist / Engineer (ICASSP 2019)

Santa Clara Valley (Cupertino)

Apple isn't alone when it comes to listening in on customers as a way to improve its digital assistant. Google came under fire for the same thing last year when it was discovered that its Nest security system included undisclosed microphones. Meanwhile, Amazon continues to audit voice interactions with Alexa even though it allows users to opt out, assuming they know how to do so. 

About the Data: 

Thinknum tracks companies using information they post online - jobs, social and web traffic, product sales and app ratings - and creates data sets that measure factors like hiring, revenue and foot traffic. Data sets may not be fully comprehensive (they only account for what is available on the web), but they can be used to gauge performance factors like staffing and sales. 

Further Reading: 

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