# DeepMind Research Engineer Interview process

I recently underwent the entire process for DeepMind, so I thought I'd share here since there are very few resources on the web about their new process. I was rejected at the end of the process with the feedback that I didn't have enough experience in ML experimentation and modeling.

Recruiter reached out to me on LinkedIn saying they have a position open in Mountain View for DeepMind for Google.

### Coding Screens:
- Two rounds, Google style questions which I'm pretty sure were picked from Google's internal question bank.
  - First round: LC medium. Follow up was a LC medium/hard which I was not asked to code once I explained the solution. Then was asked to code a LC easy. Follow up was a LC easy/medium which I was again asked not to code once I explained the solution.
  - Second round: LC Hard and the question is not on LC. I gave a factorial solution, improved it to an exponential solution with memoization. Later found that there is a polynomial solution, apparent to those who have a strong competitive programming experience. I don't think they expect the polynomial solution though.
  - Different from Google as you have to run the code and it's expected that by the end you have a solution which runs in CoderPad.

### ML screens:
- If one clears the coding rounds, they move forward to ML rounds.
- 2 ML design style rounds.
  - First round probed the depth of ML knowledge. Started with questions around probability. Was asked a number of balls style question on Bayes theorem. Proceeded to the design question. It's not enough to know things, you should know the mathematical intuition as well.
  - Second probed width of ML knowledge. Hardest ML round I ever gave. Another design style question, but they kept adding constraints once I gave a solution. In total, we discussed like 7-8 different techniques across the board. Over the course of the interview at least 7-8 constraints were added over every solution I gave.

### Virtual Onsite:
- If you clear the ML rounds, they move forward to virtual onsite.
- Two non-technical interviews with TLs where they asked about my background and my experience with ML experimentation. I have some experience from Amazon in ML experimentation, so I told them whatever experience I had. Got the feeling during the interview they want someone with more modeling experience than mine.
- One behavioral with their people & culture partner (fancier term for HRBP). Standard questions like why do you wanna join DeepMind.

In total the process took about 6-7 weeks. I was able to expedite a little by telling them I have deadlines due to Google and Salesforce.

**Edit:** Removed an earlier section ranting about the process. I thought that since I was forwarded to the onsite, my ML screen performance was strong. But based on some of the comments here, it doesn't look like that's the case.

### TC: 195K (L5)
### YOE: 4 + MS (2 YOE in US)
### Background: My experience at Amazon was in the intersection of ML and engineering with more bias towards engineering

Edit:

### Discussion Highlights:
- **Mad:** Failure of my bar, unsure if I should provide ML interview prep tips. 
- **Google:** Should I brush up math/stats from university?
- **Other Users:** They mentioned that DeepMind’s positions might be filled through referrals or internal candidates.

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Thank you for sharing. A few questions:
1. How did you prep for the ML interviews?
2. Did they fill positions fast or something? 
3. What level did they interview you for?