Recent developments in artificial intelligence have ushered in an era of what’s been coined as “machine learning.” Machine learning is the ability for software to teach itself how to perform specific tasks, with little or no human intervention needed!
By incorporating algorithms into computer systems, they can automatically detect patterns in large amounts of data and then use that information to accomplish new goals.
This technology is making waves in almost every field, from healthcare to energy to automotive manufacturing. In fact, according to LinkedIn, one third of all senior level positions are projected to become obsolete within the next 10 years due to automation.
Deep neural networks (DNN) are a specific type of machine learning algorithm that have seen dramatic growth over the past few years. DNN’s were first introduced back in 2012 when researchers created convolutional neural networks (CNNs). Since then, CNNs have become the state-of-the-art architecture for most image classification applications.
Now, companies are using both types of networks together to achieve even better results! By combining the power of both deep networks and non-deep networks, it becomes possible to train complex AI systems.
Popular job titles for a deep learning engineer
Being a deep learning engineer is more than just developing neural networks and algorithms, it is also about educating people on how to use those networks. As such, there are many different positions that require someone with this skill set. Some of the most popular career paths include:
Software engineer who designs and develops AI-powered software or features
Who trains the algorithm using supervised or unsupervised methods
Engineers in these fields need to know general computer science concepts like coding, mathematics, and statistics
Applications of machine learning including natural language processing (NLP), image recognition, and speech technology
Education professionals who teach classes using DL techniques and materials
Business professionals who work directly with companies that apply ML technologies
The responsibilities of a deep learning engineer vary depending on what area of the field they specialize in. However, all must have at least a bachelor’s degree in computer science or engineering, as well as proficiency in both written and spoken languages.
Computer engineers typically start off by designing and creating new applications, but once enough experience has been gathered, they move onto other projects.
Being a deep learning engineer is not for the inexperienced. There are many different specializations within this field, so it is important to know what skills you have before jumping into one area or another.
A soft-skills professional can easily make $50K per year in the technology industry, but they would need to specialize in social media marketing or online advertising to pull off that salary.
Interpersonal relationships are an integral part of being a successful developer, so if you’re more of an introverted person, don’t expect to climb up the ladder very quickly!
By having these basic fundamentals under your belt, though, you’ll be able to start picking up tasks and positions easily enough. And with how rapidly the tech market is growing these days, there will always be something new to learn.
To be a deep learning engineer, you need at least an undergraduate degree in computer science or engineering. A bachelor’s degree is ideal since it can usually lead to employment as a senior software engineer with experience using machine learning tools.
Most companies also require at least two years of professional work experience so having one year under your belt is optimal. This could include working for a technology company or investing firm that uses AI/ML products.
In addition to these basics, there are several other factors that determine how well an applicant will do their job. These include things like being organized, able to manage time, have strong communication skills, and more.
The pay scale is very different across the globe
Recent developments in deep learning have opened up many new opportunities for professionals to work with this technology. Companies are now able to apply these technologies to various tasks, from image recognition to natural language processing.
The most popular field right now seems to be computer vision, which uses AI to find patterns in large amounts of data. This includes applications such as self-driving cars or robots that can “see” what they are being used for.
Given its rapidly growing popularity, it comes as no surprise that there is an ever-growing demand for people who know how to use it. And if you want to make money off of it, then you need to know more about it.
That’s where this article comes into play. We will talk about some important points related to the career path as a deep learning engineer, and what kind of income you can expect to earn.
Career prospects for a deep learning engineer
The career opportunities in this field are limitless, so there is no need to feel that only people with a professional degree can work in this area. With every major tech company investing heavily in AI technology, there is always a demand for engineers who know how to develop it.
Companies will often advertise job openings for either senior level positions or entry-level jobs. Even if you aren’t looking to make as much money as some of the other professionals out there, you will still receive great benefits that depend on your qualifications.
These include extended paid vacations, flexible hours, health insurance, and even monthly bonuses. It is important to do some research before accepting any position though, to see what kind of pay structures they have.
Deep learning projects come in many different shapes and sizes
As we mentioned earlier, not every project requires an AI specialist to succeed. Some of these specialists can find work as generalists that have specializations in other areas, such as computer vision or natural language processing.
If you’re more focused on one specific area of AI, then perhaps working for a startup that is just beginning to explore this field would be better suited. Or if you are very experienced with another specialty like NLP, then starting your own company might be the best way to launch into the career path that fits you best!
Either option will get you started on your journey towards becoming a deep-learning engineer.
Deep learning projects tend to be very data intensive
As mentioned earlier, deep neural networks require large amounts of training data in order to work. This means that you will need to make sure you have enough data before starting your career as a software engineer working with artificial intelligence (AI) or machine learning (ML).
In fact, there is an expression for this: the more data, the better! The more data AI has at any one time, the smarter it gets and the higher quality predictions it can give.
As such, there are several ways to gain experience in the field while also earning good money. You could start off by taking up some courses via universities or other education providers. These may offer scholarships or paid internships if you are already trained in something else and just want extra credit for your degree.
Deep learning projects require strong analytical skills
As mentioned earlier, deep learning engineers typically work for software companies that use this technology. So what kind of jobs are there for these professionals?
Well, they develop neural network architectures or find ways to improve current architectures. They also research new algorithms for training neural networks, which have become very popular in recent years.
Another important part of their job is keeping up-to-date with the latest developments in the field. This includes understanding mathematical concepts like backpropagation, theory that underlies most modern neural networks.
Last but not least, professional level deep learners must be able to communicate well both in person and via email and chat apps. That’s why it’s so valuable to attend academic events related to the area of AI you want to pursue.