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Palo Alto Networks machine learning engineer interview: what the loop looks like for a security ML role

ml_mike · 4 replies

Went through the PANW MLE interview for a role on their threat intelligence ML team. Posting a breakdown because 'ML at a security company' has a different flavor than ML at a consumer or ads company and I didn't find much that was specific.

Context on the team The role was building models to classify malware, detect anomalous network behavior, and rank threats by severity. The domain matters because the data is different: sparse, highly imbalanced, adversarial (attackers actively try to evade your models). If you don't know how to handle class imbalance at the level of real deployment, you will struggle in this interview.

Loop: 5 rounds Recruiter + hiring manager call Take-home ML fundamentals System design Behavioral

Take-home Given a labeled dataset of process events (benign vs malicious). Asked to: build a classifier, evaluate it with appropriate metrics (hint: accuracy is the wrong metric here), describe how you'd handle concept drift. I used LightGBM, focused on precision/recall/F1, discussed SMOTE and cost-sensitive learning for the imbalance. Got explicit positive feedback on the drift section, which is apparently where most candidates are thin.

ML fundamentals Deep. They asked: explain gradient boosting from scratch, when does a random forest outperform a gradient boosted tree, how does SHAP work and why might SHAP values be misleading on correlated features, what is isotonic regression calibration and why would you use it after training. That last one is genuinely security-specific: if you're scoring malware likelihood and sending that score to analysts, calibration matters a lot because analysts trust the number.

System design Design a real-time malware classification pipeline. The inputs are file metadata and behavior logs (API call sequences). Design from ingest to prediction to feedback loop (including how you incorporate analyst labels to retrain). I spent a lot of time on the feedback loop because that's the real hard part: you need to capture whether an analyst override was because the model was wrong or because the analyst had additional context.

Comp Senior MLE, Bay Area: $235k total, base $165k. 4-year vest. 2026 offer.

4 replies

hardware_hugo

The SHAP + correlated features question is brutal because the 'right' answer is 'SHAP can mislead you when features are correlated because the shapley value of one feature bleeds into another.' Most people stop at 'SHAP measures feature importance' and call it a day.

sec_sasha

concept drift in malware detection is genuinely the hardest operational problem in security ML. attackers retool constantly. if your model is good in January it might be garbage by March against a new packer. glad they're testing for it.

ml_mike

exactly. the drift question was specifically about: how do you know when your model is drifting without ground truth labels in real time? i talked about monitoring prediction distribution shift (incoming feature distributions vs training) as an early signal before you have confirmed labels. they liked that framing.

consultant_cam

The feedback loop design question is a great hiring signal. A lot of ML candidates think the job ends at model deployment. It doesn't. The best ML engineers think about how the model gets better over its lifetime. Good sign that PANW evaluates for it.