Research Engineer Interview Experience
The coding plus ML stats round was the hardest by far. They basically asked me to implement an all_gather on noisy nodes, derive how many rounds you’d need for a target error, then figure out a better algorithm using the fact you’re transmitting floats.
Interview process
I got in through a referral, then did a recruiter screen, two 60 min technical screens, and three more technical interviews after that. I got rejected after those technical rounds, so I never made it to the hiring manager chat and maybe a team presentation, which sounded like the next steps. The whole process felt a lot less gameable than a normal big-tech loop. The first screen was more of a real algorithm problem, the second was about writing correct code fast, and the later rounds were practical ML/debugging plus one really hard stats-heavy round. The ML stats round was the one that hit hardest because it felt like graduate-level information theory, and across the process I got fewer hints than I'm used to.
- Recruiter screen
- Technical interview
- Final round
Interview tips
They're hiring in a way that really tests raw fundamentals and doesn't care whether you've seen a similar question before, so I'd absolutely brush up on graduate-level intro information theory (vs leetcode), because that kind of math showed up very directly. I'd do practical coding problems where you build some small system and then keep extending it, because that felt much closer to what they were testing than standard interview patterns. I'd also review transformer internals well enough that you can debug existing code and talk through KV caching without being spoon-fed.
Company culture
Compared to a lot of other places, the interviewers were less generous with hints and less assistive overall, and the level of engagement varied a lot by person. The process felt pretty hands-off once I got into the technical rounds.
Questions asked
Overview
This was the hardest round for me by far. It started as coding, then turned into a pretty deep information-theory style question with almost no hand-holding.
Question types asked
Specific questions asked
Implement all_gather across multiple nodes.
If the channels between nodes are noisy, derive a worst-case formula for how many rounds you need to get the answer within a target error.
How would you improve that, since the naive bound is too slow?
Can you design a better algorithm by using the fact that you're transmitting float values rather than arbitrary real numbers?
I first coded an all_gather implementation. After that, the round shifted into deriving a bound on the number of rounds needed when communication is noisy and you want the final answer within a given error tolerance. Then they pushed on how to improve the naive bound because it was way too inefficient. The important insight was to exploit the fact that you're sending float values, not arbitrary reals, and use that to design a better algorithm. This felt like graduate-level information theory.
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