Deep Learning Engineer jobs

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A deep learning research engineer is an IT specialist who develops and trains artificial neural networks that mimic the structure and function of the human brain. The results of the Deep Learning Engineer's work: voice assistants, auto-translators, autopilots, computer vision detectors, speech recognition programs, etc. are used both in industry and business, as well as in everyday life. The deep learning job is one of the specializations of a Data scientist and is suitable for people with developed analytical skills.

Features of the profession

The specialists from deep learning engineer jobs work with huge amounts of information (for example, developing self-driving cars requires millions of images and thousands of hours of video) and with significant computing power, which allows reducing the training time of a neural network. In this way, his work is similar to the tasks of a machine learning (ML) specialist. But there is a difference between these two specialties:

·      A machine learning specialist develops algorithms and models for processing data using computer systems. It can work on various tasks such as classification, clustering, regression or prediction.

·      A deep machine learning engineer specializes in creating complex neural networks that can process large amounts of data and independently extract subtle patterns from it, guided by their own classification criteria. He can also optimize the operation of neural networks and improve their performance.

We can say that a Deep Learning Engineer is a very highly qualified ML engineer. The work of DL engineers is more complex and interesting, but they also pay more for it. They can work both in contract jobs, and in remote jobs.

Pros and cons of the profession

Everyone involved in deep learning technologies does not have to worry about the prospects for the profession: this IT segment is growing by 40% every year. In addition to its prospects, the profession has other advantages:

1.    An opportunity to quickly make a career while the competition is not too high.

2.    High salaries. Even a beginning Deep Learning Engineer can count on a decent salary.

3.    A variety of employment options: you can find application for your knowledge and skills in a variety of areas - from medicine and finance to geological exploration and state security, while remaining an IT specialist.

There are many vacancies for remote deep learning jobs.

The vacancy of a deep learning engineer is suitable for “hardcore” introverts: communication skills are not very important in the work, however, you still need to be able to work in a team: DL Engineer usually works in conjunction with Data Analysts and Data Engineers .

Minuses:

1.    Sedentary work with all the ensuing consequences. Over time, vision may deteriorate.

2.    Constant intellectual tension.

Without mathematical skills and logical thinking, you will not be able to find and become the best deep learning engineer.

Career

Because deep learning engineers must be experts in their field, jumping the career ladder is not an option; experience is required. They begin their career path as junior team members (juniors) in the positions of data analysts, data or ML engineers, acquiring the necessary skills and knowledge. In the IT field, it is possible to reach the position of team lead in 5–6 years, and in the manufacturing best companies or corporation, it is possible to head a department dealing with AI Jobs.

Interesting Facts

The first computer model of neural networks in the world was called “Mark-1”; it was presented to the public back in 1960. And it was conceived even earlier: Frank Rosenblatt, the creator of “Mark,” published the article “Perceptron,” in which he described a model for the perception of information by the brain, in 1958. Rosenblatt relied on the ideas of W. McCulloch and W. Pitts, put forward in 1943.

Considering the possibilities of training his model, Rosenblatt, among other things, proposed the concept of unsupervised learning. In the 1960s These ideas interested the scientific community, but then interest in them decreased for one simple reason: until the beginning of the 21st century. there was no computing power that would allow them to be realized. The period of declining interest in neural networks even received a special name – “AI Winter”. It finally ended around 1995–2000. After 20 years, a completely different period began - the rapid development of everything related to neural networks, and its completion is not expected in the foreseeable future!