From education to employment

Advice for aspiring #AI developers

Daniel Kroening, Co-founder & Chief Scientist at Diffblue

Artificial intelligence #AI has been hailed as the fourth industrial revolution and is steadily taking the tech world by storm.

AI start-ups are popping up all over, and some have gained sought-after ‘unicorn’ status (valued at over a billion dollars). It’s no surprise that aspiring developers might be interested in working in AI, but the question is how to break into the field.

The tech industry is more accessible than many other industries, in part because AI and tech jobs tend to be focused on demonstrable skills rather than a specific degree or extensive work experience. People enter technology from a variety of backgrounds, and many climb the career ladder much more quickly than they might be able to in other industries.

Because of the nature of the developing industry and the rapid creation of new areas of AI, companies often train new starters on the job, which is great news for those just starting out or switching careers.

But where is AI heading, and which areas are most likely to have long-term opportunities for a successful career in this field?

Invest in your skills in a way that works best for you

It’s very common for developers working in AI to have one or more degrees in computer science; many even have PhDs. A Bachelor’s degree in Computer Science will provide a solid foundation if your goal is to work as a developer in AI, but there isn’t a “one size fits all” approach into a career in the AI industry.

You can teach yourself how to code with books and online tutorials. Not all companies will be open to hiring someone without a BSc in Computer Science or a closely related subject, but there will be some who are more interested in your skills and cultural fit. A few universities offer degrees in AI or specific modules, and these will almost certainly become more common, but generally speaking there aren’t a lot. Cognitive science, mathematics and logic all feature in these courses, so pick up books on these subjects.

If you want to self-learn, get started with programming now. Choose a language (Python, R and Java are good places to start) and practice the foundations. After you have a solid grasp of the basics, start being creative with your code; experiment with side projects that will allow you to hone your skills, make mistakes, and learn. Jobs in AI require an open mind towards problem-solving, and familiarity with many different approaches will likely be rewarded—as will the ability to learn independently.

There is an abundance of ways to teach yourself to code, including getting involved with hackathons, coding challenges, local meet-ups and open source projects. YouTube is a great place to learn new skills and develop your knowledge of coding, machine learning, AI and deep learning; a few channels to check out are Siraj Raval, Programming with Mosh, and the Coding Train.

Advice for gaining experience

If you’re a student or want to switch fields, try to find an internship or part-time job within the technology sector or software engineering industry—there are plenty of summer positions that are vying for talent. LinkedIn is a great place to find these opportunities, as are graduate-specific websites like Milkround. At Diffblue, we offer a range of paid software engineering internships based in our office in Oxford.

Look out for these types of roles:

  • AI architect
  • AI researcher
  • AI engineer
  • Data Scientist
  • Machine Learning Engineer
  • Software developer
  • Software engineer
  • Scrum master
  • Team lead

Keep your eye on the future

One major thing about the ever-growing nature of AI—and more broadly, the technology, industry in general—is how many future jobs may not exist yet.

How do you prepare for a job that there’s not a listing for? It’s actually not that hard.

First, make sure you are able to learn, adapt, and transfer your skills, and most importantly be flexible about your specific job role. A growing trend in the AI and technology sectors is the preference for a person who is a generalist, rather than an expert in one particular field, who can adapt and learn as the company transforms—someone with initiative and the judgment to prioritise and de-prioritise tasks as necessary. This is doubly true for start-ups. The future seems to be heading towards this trend of lifelong learning.

Another thing to bear in mind is how AI and technology are becoming integral parts of a variety of companies: food-delivery companies like Deliveroo, ride-hailing services like Uber, media companies and newspapers like the Economist, financial services and banking companies like Monzo, and even healthcare and biotech startups are all making use of burgeoning technologies. In a way, you have a massive choice of industries to work within on AI projects, so you don’t have to limit yourself to traditional technology companies to get the opportunity to eventually work in AI.

The best motivator is your own enjoyment of the field, so if you genuinely want to work in AI, you’ll find the path that works best for you.

Daniel Kroening, Co-founder & Chief Scientist at Diffblue

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