Life in DeepMind
Former intern turned intern director Richard Everett describes his DeepMind journey, sharing tips and advice for aspiring DeepMinders. 2023 internship applications will open on September 16, visit For more information.
What was your path to DeepMind?
Like many people, I loved playing multiplayer video games growing up. The interactions between human players and seemingly intelligent computer-controlled players fascinated me and I dreamed of a career in artificial intelligence. This dream led me to pursue an undergraduate degree in computer science. a common (but not exclusive!) path in the industry. However, after working on several research projects with my professors, I developed an appetite for research and decided to pursue a Ph.D.
Around the time I started my PhD, a small startup called DeepMind was acquired by Google. As I looked more closely at their research, I quickly found it inspired my own research, so in 2016 I decided to apply for an internship. After a few interviews with engineers, researchers and program managers, I didn’t get any offers. However, having met a bunch of great researchers, I decided to apply again the following year and got the internship. That experience led to a full-time offer, and I’ve been here ever since, working on AI and helping interns going through the same experience.
Can you describe the internship interview process?
The interview process was thorough, but it has evolved since I applied. Today’s interns can expect the whole process to take just a few months, which includes a technical and a group interview. In my application, I listed the researchers I was particularly interested in working with and was lucky enough to speak with after my technical interview. I was so excited. This was a unique opportunity to talk about my previous work and consider possible internship projects with world-class researchers I have followed for years and ask them questions about DeepMind.
My recruiters were incredibly helpful in guiding me through the process and providing resources to prepare for the interviews. For the technical interview, I prepared by reviewing my first-year undergraduate courses in math, statistics, and computer science. For example, review of linear algebra, calculus, probability, algorithms, and data structures. I also did some coding exercises where I tried to talk about what I was doing.
For the team interviews, I reviewed the team’s recent work (eg publications, blog posts, articles, speeches) and thought about how my work might relate to it. I also came up with a short list of questions I wanted to learn more about, such as the team’s collaborative style and how previous practices had worked.
What was it like when you came in full time?
It took me a long time to find my footing! With so many exciting projects going on and great people to talk to, working at DeepMind often feels like being a kid in the world’s biggest candy store. For interns, developing and focusing on just one project out of so many is challenging, especially in a limited amount of time. This was a challenge I found in my own practice, and today I enjoy supporting new beginners through this process who are experiencing the same excitement for the first time.
Why did you get involved with the internship program as a full-time employee?
Having gone through the internship experience myself, I can relate to what our aspiring and current interns go through. It can be scary, exciting, confusing and inspiring, all at the same time. After receiving so much support during my internship, I wanted to give the same support to future interns. As a result, I now coordinate my team’s internship program and am on various teams that continually seek to improve the program across DeepMind. I also interview, coach and manage interns as well as spend time reaching out and talking to potential candidates (eg. GraceHopper, NeuroIPSand research conversations).
What kind of work do interns do?
It’s always exciting to see what interns decide to pursue during their time with us. In my team (Game Theory and Multi-Agent), we work closely with practitioners to jointly develop projects that can do their own thing, and this has led to an incredible range of projects over the years.
To highlight just a few public examples, practitioners have designed new multi-agent environments (e.g. inspired by discount social game Among Us and assembly lines), developed infrastructure for the study of human-agent interactionused cooperative game theory to language models and formation of a negotiating teamthey worked for reverse multifactorial amplification, uncovered counterexamples to reinforcement learning, mastered the game of Strategoand implemented evolutionary game theory in online learning.
How would you describe the culture at DeepMind? And your team?
In short – polite and cooperative. Over the years, I’ve heard dozens of interns and new beginners make the same remark: “I can’t believe how friendly and supportive everyone is!” The time, energy, and support that DeepMinders give to each other is remarkable, and that extends all the way from company veterans to day-one newbies. Everyone is always willing to have a coffee to chat, discuss their work, share feedback and collaborate on projects together.
For example, one of my favorite projects at DeepMind (Learning robust cultural transmission in real-time without human input), came from the close collaboration between artists, designers, ethicists, program managers, QA testers, scientists, software engineers, engineering researchers, and others over a period of two years. This diverse and collaborative culture extends to our practices, with internship programs typically involving multiple partners and advisors from across the firm (spanning roles, teams, and even offices!). For example, several of our Game Theory and Multi-Agent team interns work closely with DeepMinders from our London and Paris offices.
From left to right, a subset of the project’s authors: Ashley Edwards (RS, London), Miruna Pislar (RE, Paris), Kory Mathewson (RS, Montreal), Alexander Zacherl (Designer, London), Richard Everett (RS, London ), Edward Hughes (RE, London), Avishkar Bhoopchand (RE, London).
Any advice for aspiring DeepMind interns?
For students interested in artificial intelligence, there are many readily available resources available to independently learn more about the industry and DeepMind: from papers, blog postsand conversations to open source code, demos and tutorials. It’s easier than ever to get hooked! You can also attend workshops and conferences, many of which offer student discounts and mentoring opportunities (eg Deep Learning Indaba, Cooperative AI). For me, I found my love for AI research by talking to professors about their research between classes, working on projects with them, and then reaching out to other researchers in the areas that excited me.
DeepMind is made up of kind, collaborative and driven people from all walks of life, and our internship program reflects that. Whether you’re an undergraduate or PhD student, studying a technical, natural or social science subject and have AI/ML experience or not, there’s probably an internship opportunity for you. We offer internships to various groups in Research, Engineering, Science, Ethics and Society and Business.
Having gone through the process myself (twice), I can completely understand and relate to how intimidating the application can be. I’ve spoken to so many incredibly talented students who mistakenly believe that DeepMind is out of reach or that their skills are insufficient and therefore not even applicable. If you’re thinking about applying for an internship, my genuine advice to you is to do it. You have nothing to lose and perhaps both you and DeepMind have a lot to gain.