In every team of any serious ASR or NLP company, that has to be one person who is the "search guy". Not search as in search engine, but search as in searching in AI. The equivalent of a chess engine programmer in a chess program, or perhaps to engine specialist for race cars. Usually this person has three important roles:
- Program the engine,
- Add new features to the engine ,
- Maintain the engine through its life time.
This job is usually taken by someone who has title such as "Speech Scientist" or "Speech Engineer". They usually have blended skills of both programming and statistics. It's a tough job, but it's also highly satisfactory job. Because the success of a company usually depends on whether features can be integrated quickly. That gives the "search guy" a mythical status even among data scientist - a search engineer needs to effectively work with two teams: one with mostly research background on statistics and machine learning, the other with mostly programming background, whose job is to churn out pseudocode, implementation and architecture diagrams daily.
I tend to think the power of "search guy" is both understated and overstated.
It's understated because there are many companies which only use other people's engine. So they couldn't quite get the edge of customizing an engine. Those which use open source implementation is better, because they preserved the right to change the engine and give them leverage on intellectual property and trade secrets. Those who bought commercial engine from large company would enjoy good performance for few years, but then got squeezed by huge price of upgrading and constrained by overly restrictive license.
(Shameless prompotion here: Voci is an exception. We are very nice to our clients. Check us out at here. 🙂 )
It's overstated because the skill of programming a search is nothing but a series of logical exercises. The pity is programming a search algorithm, or generally a dynamic program (DP) in general, takes many kinds of expertise. The knowledge can only be sporadically found in different subjects. Some might learn the basic of DP in an algorithmic book such as CLRS, but mere knowledge of programming doesn't give you insights on how to debug an issue of the search. You do need to have solid understanding in the domain knowledge (such as POS tagging and speech recognition) and theory (such as machine learning) to get the job done correctly.