How to Become a Tech Innovation Researcher or an AI Technologist

Zoë BalroopMay 4, 2022

So, you have an interest in ethics and society and you want to break into the tech innovation and AI research industries. But where do you start? How do you go about building a strong foundation upon which you can jumpstart your career?

Generally, the most competitive companies like IBM, Meta, and Google/ DeepMind will require at least a Masters (sometimes even PhD), but what else should you be doing?

Acing your Job Application and the five relevant skills you need to have

The first step to breaking into your dream industry is to brush up your CV and make sure it fully reflects your ability to thrive in your desired job. For tech and innovation research, and AI ethics, you’ll want to showcase relevant skills such as:

Research and written communication skills

Get involved with projects, either something that interests you alone, or in a group if you can find a relevant opportunity, and show that you can research and (co-) author a paper. This could be volunteer or paid work with a Think Tank, a charity, or any local research initiatives that take your interest.

Good problem-solving skills

Tech/ AI research and ethics is a cutting-edge field of research. There is a lot we don’t know, making it a crucial industry, but equally a challenging one. Therefore, resilience and problem-solving skills are important to showcase in any applications and interviews.

Analytical skills

This goes hand in hand with good researching skills. Research, especially where tech and AI are concerned, needs to be forward-looking, analytical and critical of all possible outcomes. You’ll need to be able to think holistically about what impact new technologies could have on equality, society, politics, democracy, well-being – etc, without any rose tinted glasses.

Quantitative, statistical skills

Technological research requires being able to quantify results and theories with statistical models and evidence. If you’re not fully comfortable with manipulating mathematical functions and models, there are loads of free resources and courses online to help you get a grip on this.

Once the contents of you CV are up to date, consider exploring fun ways to show some personality and passion too. For example, previously successful applicants to Google, Meta, and Spotify to name a few, have formatted their CVs as if it were a Google search results page, or Facebook page, etc. Doing something relevant but creative shows that you’ve put time into your application, and that you are a genuine candidate. For tech and Ai industries, even the research side thereof, recruitment is so competitive that you’ll want to take any extra edge you can get.

A little help never hurts

Speaking of an extra edge, there are a couple quick things to consider to really round out your application and person.

  1. Get as much experience as you can; no matter how small, once it’s relevant, it can’t hurt.
  2. Do something to stand out. Whether it’s a funky CV (aside from formatting, it should always be tailored to company and sector), or sending your interviewer an email before and after you talk – the little things stand out. For example, did you know that only 20% of candidates send a thank you email after an interview, but that it boosts your chances of being successful by 80%?
  3. Cover letters aren’t optional. Once there’s an option to submit one, do it. Do your research on the company, on the sector, and share your motivations behind breaking into tech ethics/ research.
  4. Use LinkedIn. Build a network, connect with and reach out to recruiters. When you see posts from people who have landed a job somewhere you’re interested in, check who they thank and reach out to them. You never know which professional relationship might lead to a referral.
  5. Get a mentor. This is easier said than done, and you need to be commited, considerate, and cooperative. For example, you'll need to be open-minded and able to take feedback well and show improvement. According to the leaders in research-based user experience, it is important for both mentees and mentors to put effort into the relationship, and they go on to list the traits in this short 3-minute video.

Level up your skills

Depending on which part of tech and AI research you want to go into, coding might be beneficial, or even necessary. If you’re not sure, generally the ethics and social side of research won’t require coding, whereas the development and technical research side most likely will.

If you’re reading this wanting to work for (e.g.) Meta in their Machine Learning Research role, but realise you don’t have the coding skills yet – don’t worry. There is a plethora or free resources available online that you can and should be making use of to up-skill while you’re job hunting. Some of our top picks are listed below to help you get started in mastering all the skills you need to stand out. These come from edX- a great platform to find free online courses, where you can earn certificates upon completion. We also recommend Coursera and Udemy as other platforms to check for similar free courses (also known as MOOCs – Massive Open Online Courses) and certifications.

To learn and develop your coding skills:

Ranging from beginner to intermediate levels, in multiple coding languages and different applications.

IBM SkillsBuild: Artificial Intellegence

Learn Artificial intelligence with this free online course and resources

Harvard’s Introduction to Computer Science

Duration: 12 weeks

Structure: Self-paced

Availability: Until 31st December

Other variations: Computer Science for Business, Computer Science for Lawyers

Harvard’s Introduction to Artificial Intelligence with Python

Duration: 7 weeks

Structure: Self-paced

Availability: Until 31st December

Harvard’s Introduction to Programming with Python

Duration: 9 weeks

Structure: Self-paced

Availability: Until 31st December

Other variations: Introduction to Programming with JavaScript, Introduction to Programming with Scratch

Harvard’s Using Python for Research  (requires knowledge of python)

Duration: 12 weeks

Structure: Self-paced

Availability: Until 9th July

Harvard’s Data Science: R Basics

Duration: 8 weeks

Structure: Self-paced

Availability: Until 27th October

Other variations: Statistics with R, Data Science: Visualization, Data Science: Machine Learning

To learn the fundamentals of technology, AI, and ethical implications:

Understanding how technology, AI and innovation affect society, human welfare, business, and the future, as well as learning practical analytical tools to examine these impacts.

The University of Edinburgh’s Data Ethics, AI, and Responsible Innovation

Duration: 7 weeks

Structure: Instructor-paced

Availability: May 9th to July 4th

IBM’s AI for Everyone

Duration: 4 weeks

Structure: Self-paced

Availability: May 2nd to June 30th

The Linux Foundation’s Ethics in AI and Data Science

Duration: 6 weeks

Structure: Self-paced

Availability: Starts May 2nd

TokyoTechX’s Science, Engineering, AI & Data Ethics

Duration: 7 weeks

Structure: Self-paced

Availability: Starts May 2nd

These are not prescriptive, but rather suggestions to help give you direction and inspiration for what you might want to up-skill in to best prepare you for your career. Similarly, doing all will not help you – you only need one or two relevant ones; not all coding languages do the same thing and so depending on where your interest lies you’ll want to check which is best suited to you.

AI Ethicists – the technicalities

Becoming an AI ethicist is perhaps the most niche of the technology and innovation research routes. You’ll definitely need a strong understanding of AI tools and technology in multiple industries and wider society, but also the nitty gritty details of how they work, what the regulatory and legal policies surrounding them are, and existing ethical traps.

For AI in particular, Python, Scala, MATLAB, C/C++ and SQL (Structured Query Language, pronounced Sequel) are the main coding languages you want to focus on. Coursera and Udemy have some great courses covering these.

You can find them here: Scala | MATLAB | C/C++ | SQL

Last steps

Once you’ve taken all this into consideration, and built yourself the best possible application and foundational knowledge to demonstrate that you are right for technology/ AI research and ethics, it’s time to get job hunting!

As with any job, set alerts on and check LinkedIn, Glassdoor, Reed, Indeed, etc. On LinkedIn, also grow and leverage your network – consider posting about the job you’re looking for, or building a personal brand about what you’re learning (in terms of technology and ethics), so that recruiters who see your profile know you’re serious and knowledgeable. Set your profile to creator mode and use hashtags to attract a relevant audience. You never know who might notice you...

So, with all these tips now in your arsenal, we wish you the best of luck breaking into the tech/ innovation/ AI research sectors!

Start your job search today!

Be sure to utilize the CO.CAREERS Good Jobs Search Engine to filter down your desired role, even if you are not ready to apply. Be sure to also join over 13K people and follow us on Twitter for the freshest, most condensed career development tips and good job opportunities.

Zoe baldroop

This article was contributed by Zoë Balroop. Zoe is a Penultimate year student at the University of Warwick, studying Philosophy, Politics, and Economics (tripartite), and current Tech and Innovation Researcher at Warwick Think Tank. Topics written for them have included issues surrounding AI and algorithms + human gene editing with regards to inequality, as well as international power dynamics of the quantum supremacy race. Have always had a keen interest in technology, and am also interning for 3 tech startups alongside my degree: Algomo, The HR TECH Partnership, and Mayday. I hope to continue to expand my knowledge in these areas!

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One comment on “How to Become a Tech Innovation Researcher or an AI Technologist”

  1. Great, helpful read that has me considering a career switch to become an AI Ethicist. It is a social imperative to optimize for healthy, delightful user experiences that prioritizes human wellbeing, and algorithms that use AI extensively are quite optimized to generate revenue for companies that own them. I like how Elon Musk is planning to open source the Twitter algorithms after he recently bought the platform! Looking forward to the next piece indeed.

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