Datadog ML Engineer interview typically runs about 7 rounds: intro screen, coding round, virtual on-site across different days, hiring manager conversation, and coordinator calls. The process usually takes several weeks and is notably bloated and bureaucratic.
$118K
Avg. Base Comp
$181K
Avg. Total Comp
4-5
Typical Rounds
3-6 weeks
Process Length
Our candidates report that Datadog’s ML Engineer interviews can feel surprisingly generic unless there’s a clear team anchor behind them. The strongest signal we see is not raw difficulty, but whether the conversation ever becomes specific to the product area, data problems, or engineering context you’d actually own. In one recent experience, the candidate left with the sense that the process was more about proving you can move through the funnel than about testing deep ML judgment, which is a useful clue about how to prepare mentally for the interviews here.
A recurring theme is that the evaluation can feel light on depth even when the interviewers themselves are strong. That means candidates should pay close attention to the moments where the conversation shifts from surface-level implementation to tradeoffs, system boundaries, and how an ML solution would fit into Datadog’s observability environment. We’ve seen that the people who do best are the ones who can quickly connect their background to a concrete team need, because the process seems to lose momentum when that connection is missing.
The non-obvious risk is not technical failure so much as ambiguity. Multiple candidates have described the experience as bureaucratic and hard to map to a real role, which suggests Datadog is screening for alignment as much as capability. If you can make your experience feel directly relevant to a specific product or team problem, you’re much more likely to turn a process that feels broad and repetitive into one that feels coherent.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Datadog process.
The process felt much longer than it needed to be. I went through roughly seven stages total, starting with an intro screen, then a coding round that honestly didn’t feel very relevant, followed by a virtual on-site spread across different days, and finally a hiring manager conversation. Between almost every stage, there was an extra call with a coordinator, which made the whole thing feel pretty bloated and bureaucratic.
A couple of the interviewers were clearly solid engineers, but the actual content was surprisingly easy and didn’t do much to reveal real depth. What bothered me most was that it wasn’t tied to a specific team, so I never got a good sense of who I’d be working with or whether the role actually matched my background. It felt more like busywork than a serious evaluation. In the end, I didn’t get an offer, and my main takeaway was that this process seems worth it only if you already have a connection to a specific team. Otherwise, it’s a lot of time spent on calls that don’t tell you much.
Prep tip from this candidate
Be ready for a drawn-out process with multiple coordinator touchpoints and a coding round that may feel generic rather than deeply ML-specific. If you can, try to get clarity early on about the exact team and scope, since the lack of team context was a major downside here.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Datadog
Write a function that tests whether a string of brackets is balanced.
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Synthesized from candidate reports. Individual experiences may vary.
An initial screening call to discuss your background and interest in the ML Engineer role. The candidate described this as the first step before moving into technical interviews.
A technical coding interview that the candidate felt was not especially relevant to the ML Engineer role. It appeared to be a standard assessment of problem-solving and coding ability rather than deep machine learning work.
A multi-stage virtual onsite spread across different days, with several interviews rather than a single onsite block. The candidate noted that the content was relatively easy and did not strongly probe depth, and that the process felt disconnected from a specific team.
A final conversation with the hiring manager near the end of the process. This was the last substantive interview before the decision, following the earlier screens and virtual onsite rounds.