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Okta machine learning engineer interview, what the loop actually looks like in 2026

ml_mike · 5 replies

Okta hired me in early 2025 and I've since interviewed a bunch of candidates on our team, so I can give you both the candidate-side and interviewer-side view here.

First, some context: the ML team at Okta is not giant. Most of it sits under Security Intelligence and some folks work on anomalous login detection, risk scoring, adaptive MFA. If you're expecting a team focused on LLMs or generative stuff, that's not really the core of what they do. The applied ML work is mostly classical, more gradient boosting than transformer architectures. Keep that in mind when prepping.

What the Okta machine learning engineer interview loop covers:

ML fundamentals are taken seriously. Not "explain gradient descent" level stuff, but more applied: why does your risk model degrade over time, what retraining strategy would you use, how would you detect covariate shift in a login-event stream. Think streaming data and imbalanced classes because that's their actual problem.

Coding is Python, but it's not pure LC. Expect a problem where you're manipulating a data structure, maybe something involving sliding windows over event sequences. One candidate told me they got a problem about flagging burst login activity from a stream. This is not a coincidence.

System design for ML roles covers the full MLOps stack. Feature store, model serving, monitoring. A question like "design a real-time fraud scoring pipeline" fits well. They care about latency because this is auth infrastructure, decisions need to be milliseconds.

Behavioral is four to five questions. The one that catches ML candidates is anything about working with data science without a clean handoff. "Tell me about a time you had to ship a model but the data wasn't ready." Very common in security ML because telemetry pipelines are messy.

Comp for senior ML at Okta was around 200k-220k base when I joined, RSUs were a four-year vest, no big equity sign-on that I saw. Market has moved some since then. Not FAANG numbers but not bad for the type of work, which is applied with real stakes.

Feel free to ask questions. I'm just one engineer, I don't speak for the company officially.

5 replies

ae_andre

That imbalanced classes point is huge for security ML. Do they test you on how to handle extreme imbalance (like 0.01% positive rate) or just expect you to mention SMOTE/class weights and move on?

ml_mike

They expect you to go deeper. Saying "I'd use class weights" is table stakes. They want to know how you'd evaluate the model, precision-recall vs AUROC, and how you'd set the operating threshold given a business cost tradeoff. If you can reason about the business consequences of false positives (locking out real users) vs false negatives (letting attacks through) you'll stand out.

infra_ines

Interesting that it's mostly classical ML. Makes sense for security telemetry honestly, interpretability matters more than raw accuracy when you're deciding whether to block someone's login.

sdr_sky

Is there any path into this team for new grads? Or is it all senior+ hiring?

ml_mike

There's a new grad / entry-level ML track but it's small and competes with their intern-to-full-time pipeline. If you have a strong internship doing applied ML (not just notebooks, actual deployed models) you have a shot. It's not easy though. Most of the team is mid to senior.