I went through the ServiceNow machine learning engineer loop in February 2026, for a role on the Now Assist team (that's their AI-powered workflow automation product). Sharing because the ML interview landscape is fragmented and company-specific variation matters a lot.
First: what they're building
ServiceNow's ML team is doing applied AI, not research. The focus is on LLM-based workflow automation, classification models for IT service management (incident categorization, change risk assessment), and retrieval-augmented generation for their virtual agent. If you're coming from pure research or academic ML, this is applied engineering with a heavy inference-and-serving component. Know that going in.
The Loop (5 rounds)
Recruiter screen, ML coding screen, ML system design, applied ML case, and a behavioral/leadership round.
ML coding screen
60 min, two parts. Part one: implement a simple attention mechanism from scratch in PyTorch (not calling nn.MultiheadAttention, actually writing the math). They're checking if you understand what you're using. Part two: a statistical question about evaluating a classifier on imbalanced data. Precision/recall/F1, why accuracy is a bad metric on 98/2 splits, ROC vs PR curves. Know this cold.
ML system design
Design a system that classifies incoming IT support tickets into categories (incident type, priority, routing) at scale. I talked through: feature engineering on the ticket text, fine-tuning a smaller BERT-class model vs. prompting a larger LLM, latency constraints (they said SLA is 500ms), online vs batch inference, model monitoring for drift, retraining triggers.
The latency constraint is real at ServiceNow. Their platform runs on enterprise SLAs. A 10-second LLM call is not acceptable in many contexts. They pushed me on when I'd use a smaller fine-tuned model vs an LLM API call and I had to defend the tradeoff.
Applied ML case
Given a dataset description (they didn't give actual data, just schema and stats), walk through how you'd build and validate a model to predict whether a change request will cause an incident. This is real ServiceNow domain stuff. Think through: label quality, class imbalance, feature leakage if you include post-change features, how you'd set thresholds for a business that has high cost of false negatives.
What I'd prep
Transformer internals (you will be asked to explain attention). LLM serving patterns: batching, quantization, caching. Imbalanced classification. The Now Assist product, read about it before you go in.
Offer: Senior MLE, $230k base, $260k total cash. Remote eligible. RSUs separate.