Life at DeepMind
Former intern and current internship manager Richard Everett describes his journey at DeepMind, sharing tips and advice for aspiring DeepMinders. Applications for the 2023 internships open on September 16, visit https://dpmd.ai/internshipsatdeepmind 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 wise computer-controlled players fascinated me, and I dreamed of a career in AI. This dream led me to pursue a bachelor’s degree in computer science; a common (but not exclusive!) path to the industry. However, after working on several research projects with my professors, I developed a passion for research and decided to continue my education toward a Ph.D.
While I was starting my PhD, a miniature startup called DeepMind was acquired by Google. When I looked into their research, I quickly discovered that it inspired my own research, so in 2016 I decided to apply for an internship. After several interviews with engineers, researchers, and program managers, I didn’t get an offer. However, after meeting a lot of great researchers, I decided to reapply the following year and got the internship. That experience led to a enduring position, and I’ve been here ever since, working on AI and helping interns who are going through the same thing.
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 entire process to take just a few months, which includes a technical and team interview. In my application, I listed researchers I was particularly interested in working with and was fortunate enough to talk to them after the technical interview. I was so excited. It was a unique opportunity to talk about my previous work and brainstorm potential internship projects with world-class researchers I had shadowed for years, and ask them questions about DeepMind.
My recruiters were incredibly helpful in walking me through the process and providing resources to facilitate me prepare for the interviews. I prepared for the technical interview by revising my math, statistics, and computer science courses from my first year of undergrad. For example, reviewing linear algebra, calculus, probability, algorithms, and data structures. I also practiced a few coding exercises where I tried to talk through what I was doing.
For the team interviews, I reviewed the team’s recent work (e.g., articles, blog posts, papers, talks) and thought about how my work might relate to it. I also came up with a miniature list of questions I wanted to learn more about, such as the team’s collaboration style and how previous internships were conducted.
How did you feel when you joined us full time?
It took me a long time to find my footing! With so many electrifying projects 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 one project among many is a challenge, especially with restricted time. This was a challenge I encountered during my internship, and now I enjoy supporting up-to-date employees in this process as they experience the same excitement for the first time.
Why did you get involved in the internship program as a full-time employee?
Having had an internship experience, I can understand what our aspiring and current interns go through. It can be stressful, electrifying, confusing, and inspiring, all at the same time. After receiving so much support during my internship, I wanted to provide that same support to future interns. As a result, I now coordinate my team’s internship program and am on several teams that are constantly trying to improve the program across DeepMind. I also interview, mentor, and manage interns, and spend time connecting and talking to potential candidates (e.g. Gracious Hopper, NeurIPSand research lectures).
What type of work do interns do?
It’s always electrifying to see what the interns decide to do during their time with us. In my team (Game Theory and Multi-Agent) we work closely with the interns to co-develop projects that they can make their own, which has led to an amazing range of projects over the years.
To name just a few public examples, interns designed up-to-date multi-agent environments (e.g. inspired by social deduction game Among Us AND assembly lines), developed infrastructure for studying human-agent interactionsused cooperative game theory language models AND negotiating team formationHe was working on multi-agent inverse reinforcement learning, uncovered examples antagonistic to reinforcement learning, mastered the game of Strategoand applied evolutionary game theory for online learning.
How would you describe the culture at DeepMind? And your team?
In miniature, kind and cooperative. Over the years, I’ve heard dozens of interns and up-to-date hires say the same thing: “I can’t believe how friendly and supportive everyone is!” The amount of time, energy, and support DeepMinders gives to each other is extraordinary, and that goes for veterans of the company as well as up-to-date hires on their first day. Everyone is always joyful to grab a coffee, chat, discuss their work, share feedback, and collaborate on projects.
For example, one of my favorite projects at DeepMind (Learning to Deliver Robust, Real-Time Cultural Transmission Without Human Data), was created through close collaboration between artists, designers, ethicists, program managers, QA testers, scientists, software engineers, research engineers, and more over two years. This diverse and collaborative culture extends to our internships, with internship projects typically involving multiple collaborators and advisors from across the company (spanning roles, teams, and even offices!). For example, several of our Game Theory and Multi-Agent interns work closely with DeepMinders from both our London and Paris offices.
From left to right: some of the project’s contributors: 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).
Do you have any tips for people who want to intern at DeepMind?
For students who are just starting to get interested in AI, there are many resources readily available resources available to you so you can independently learn more about the industry and DeepMind: from identity documents, Blog postsAND conversations Down open source code, demos and tutorials. It’s easier than ever to get involved! You can also attend workshops and conferences, many of which offer student discounts and mentoring opportunities (e.g. Deep Learning Indaba, Collaborative AI). I discovered my love for AI research by talking to professors about their research between classes, working on projects with them, and then connecting with other researchers in areas that excited me.
DeepMind is made up of genial, collaborative, and motivated people from all walks of life, and our internship program reflects that. Whether you’re a bachelor’s or doctoral student in a technical, physical, or social science field with or without AI/ML experience, you’ll likely find an internship opportunity. We offer internships across teams in Research, Engineering, Science, Ethics & Society, and Operations.
Having been through this process (twice, even), I completely understand and relate to how intimidating applying can be. I’ve talked to many incredibly talented students who mistakenly believe that DeepMind is out of their league or that their skills are not good enough, and therefore don’t even apply. If you’re considering applying for an internship, my straightforward advice to you is to just do it. You have nothing to lose, and you and DeepMind may have a lot to gain.