What are the Top 5 Jobs in AI?

1. AI Researchers

The role of an AI Researcher is to identify new methods of using artificial intelligence to overcome problems and limitations that organisations are facing. Typically, they will specialise in understanding large data sets and converting its learnings into ideas and plans to develop new AI technologies that can be bought-to-life by Data Scientists.

To become an effective AI Researcher, soft skills such as critical thinking are crucial as the role will involve frequent brainstorming to find new methods and approaches. Alongside this, some of the more specialist skills required include mathematics to utilise statistics and predict how AI programs will run, and the ability to analyse data with experience in tools such as RapidMiner or SketchEngine.

2. Data Scientists

Whilst an AI Researcher is responsible for finding new methods for AI to problem-solve, their findings will typically be passed onto a team of Data Scientists whose role it is to apply these methods in real-life situations. As such, their roles are crucial to interpreting data effectively and executing tactics to develop AI models and practices.

The key skills required to succeed as a Data Scientist includes fluency in programming language such as Python and R, and a knowledge of algorithms and their frameworks to build AI models. Good communication skills are also essential as Data Scientists typically work together in teams and require a good understanding of the analysis conducted by AI Researchers.

One notable example of how AI Researchers and Data Scientists work together is overcoming how facial recognition technology is more likely to misidentify a person with darker skin. Whilst a team of AI researchers identified that three commonly used facial analysis algorithms worked better on light-skinned people, a team of Data Scientists are working to use this information and fix the bug to make the technology effective regardless of skin colour.

3. Machine Learning Engineers

Whilst data scientists work on information based on ‘data’ and human learning collated by AI Researchers, Machine Learning generates powerful predictive models from a technology-based interpretation without being particularly programmed but understanding the surrounding regularities in data. Read our blog to find out more about the differences between Data Science and Machine Learning.

This method generates important alternative results which can speed up the process in identifying new solutions, or spot gaps that can’t be easily identified by the human eye. As a result, Machine Learning Engineers have the job of designing self-running AI systems to automate these predictive models and enhance their effectiveness.

They will typically work alongside AI researchers to understand the data that Machine Learning will be programmed to solve, and communicate with data scientists, IT, and software teams. The key skills required to succeed in this role include strong analytical skills and experience in the use of machine learning packages such as TensorFlow and SciPy.

4. Deep Learning Engineers

Deep Learning is a subdivision of machine learning where artificial intelligence is programmed with ‘brain like’ structures called neuro networks, designed to mimic the thought process of humans. Although a more time-consuming process compared to machine learning, the results can be more effective, leading to an increasing demand from businesses for Deep Learning Engineers.

Deep Learning Engineers are responsible for programming AI systems to ensure they create transferable solutions. They will typically train their systems to better understand unstructured data in a variety of forms such as text and PDF’s, meaning some of the typical pre-processing of data can be removed to configure more detailed results.

Similarly, in most jobs within artificial intelligence, the ability to code is a must for Deep Learning Engineers with expertise in software such as Python. Successful employees in this area are typically focused individuals who are patient, curious and have a good data intuition.

5. Robotics Scientists

Whereas Data Scientists typically programme technologies to find solutions, Robotics Scientists design and build mechanical devices to perform tasks that can work in conjunction with humans and support their activities.

Robotics scientists are required to understand how ‘robots’ can tackle an issue in the way that humans can’t do on their own, and the expertise can be applied across a range of industries. In healthcare for example, robotic technology has been created to deliver colonoscopy and surgery, with AI robots programmed to detect possible cancer polyps.

Robotics scientists are responsible for designing this software and training artificial intelligence on how to perform. As a result, they must be able to think innovatively, possess a good knowledge of mathematics and computer science, and own high-level programming skills.

What are the top ‘less technical’ jobs in AI?

Expertise in artificial intelligence also opens up a host of ‘less technical’ career opportunities such as roles in cyber security, data analytics and user experience design. These jobs typically require the support of AI such as machine learning to detect suspicious activity in cyber threats. However, these roles are better suited to those who would like to specialise in broader technology roles but can still apply their knowledge of artificial intelligence.

If you want to learn skills that employers are actively seeking within technology and the AI development sector, you should consider studying our specialised online Masters in Artificial Intelligence. At the University of Leeds, our course modules range from Data Science and Algorithms to optional modules in Machine Learning and Robotics to equip you with the expertise required for a successful career in AI.
Disclaimer: This article is not based on data but on the author’s personal perceptions of the AI industry.

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