sardine Machine Learning Engineer Interview: Questions & Prep (2026)

What sardine does and why ML engineers matter

Sardine is a fraud detection and compliance platform used by fintechs, banks, and crypto companies to catch payment fraud, device spoofing, and money-mule networks in real time. ML is not a support function here; it is the core product. Engineers build and maintain models that score transactions at scale under tight latency budgets, while keeping decisions explainable enough to satisfy compliance teams.

The ML Engineer role sits at the intersection of applied research and production engineering. Interviewers will test you on fraud-specific problems: class imbalance, real-time feature pipelines, graph-based ring detection, and model drift. Understanding the business cost of a wrong call (blocking a genuine customer versus missing a fraudster) matters as much as knowing the math.

As of July 2026, sardine had 35 open roles tracked on knok jobradar, signaling a period of active growth. The interview process typically spans a coding screen, an ML depth discussion, a system design round, and a final conversation with leadership. Candidates report the process is structured and moves at a reasonable pace.

These questions reflect what sardine builds and the signals from candidates who have interviewed there. The focus is fraud ML, production systems, and real-world trade-offs.

1. How would you design a real-time transaction fraud scoring system with strict latency requirements?
2. Fraud datasets are heavily imbalanced. What techniques do you use at training time, and how do you evaluate models so that class imbalance does not mislead you?
3. Have you used graph-based methods (graph neural networks, graph features) to detect fraud rings or collusion? Walk us through your approach.
4. How do you detect and respond to concept drift in a fraud model that is already running in production?
5. Beyond AUC-ROC, which metrics matter for a fraud use case, and how do you set classification thresholds?
6. Sardine works across payment rails such as cards, ACH, and crypto. How would you build a model that generalizes across rails without overfitting to one?
7. Describe your approach to feature engineering for behavioral signals such as typing rhythm, scroll patterns, or device fingerprints.
8. How do you decide between retraining a model on a fixed schedule versus online or streaming updates?
9. Walk me through a time you improved an ML model that was already live in production. What changed, and what was the measurable outcome?
10. How do you balance false positives (flagging good customers) versus false negatives (missing fraud) when setting model thresholds?
11. How would you explain a model's fraud decision to a compliance officer who needs a documented audit trail?
12. If you were designing a shared feature store for fraud signals used across multiple models, what would your architecture look like?

Q: Walk me through a time you improved a live ML model in production.

*Situation:* At my previous company, our transaction fraud classifier had been live for several months. We noticed that precision on flagged transactions was dropping while the volume of legitimate customers being blocked was rising.

*Task:* I was responsible for diagnosing the issue and proposing a fix without taking the model offline, since it was scoring live payments.

*Action:* I ran a feature drift analysis comparing incoming feature distributions against the original training window. Two device-fingerprint features had shifted significantly, likely because a major phone OS update had changed how certain browser properties were reported. I retrained the model on a rolling window that included post-update data, added a monitoring job to alert on feature drift above a set threshold, and ran a shadow deployment to validate the new model before switching live traffic.

*Result:* After the swap, precision on flagged transactions recovered and the false-positive block rate dropped. The monitoring job caught a smaller drift event the following quarter before it could degrade the model.

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Q: How have you handled a strict latency constraint in an ML system?

*Situation:* Our risk scoring API had a strict latency SLA for a card-present payment flow. The initial model was fast enough in testing but too slow under production traffic spikes.

*Task:* I needed to reduce tail latency without meaningfully sacrificing recall on fraud.

*Action:* I profiled the inference path and found that feature computation (especially real-time aggregation queries) was the bottleneck, not the model itself. I moved the most expensive aggregations to a precomputed feature store refreshed on a regular cadence, accepting a small staleness trade-off. I also pruned lower-importance features using SHAP values, retrained on the leaner feature set, and benchmarked a lighter model variant. The recall difference turned out to be negligible.

*Result:* Tail latency dropped within SLA. Fraud recall stayed within an acceptable range of the original model, and the precomputed feature store became the standard pattern for other models in the pipeline.

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Q: Tell me about a time you worked with highly imbalanced data.

*Situation:* I was building a first-party fraud model where genuine fraud events made up a very small fraction of all transactions. A naive model would just predict 'not fraud' almost always and still look great on accuracy.

*Task:* I needed a model that caught fraud at a rate the business could act on, while keeping the false-positive rate low enough not to frustrate customers.

*Action:* I used stratified sampling, oversampling on the minority class during training, and calibrated the output probabilities rather than relying on raw scores. I evaluated every candidate model on precision-recall curves and a cost-weighted metric that assigned different weights to false positives and false negatives based on the business's stated tolerance. I also set thresholds separately for different transaction risk tiers rather than using a single global cutoff.

*Result:* The final model caught a much higher share of fraud compared to the baseline, with a false-positive rate that the customer experience team accepted. The tiered-threshold approach became standard practice for the team going forward.

Use STAR, then add a 'so what' layer for ML roles.

For ML interviews at a company like sardine, plain STAR (Situation, Task, Action, Result) gets you started, but interviewers often probe the Action step further. Break your Action into three parts: what you diagnosed, what you chose to do and why you ruled out other options, and how you validated your decision before committing. This shows engineering judgment, not just execution.

For system design questions, use a layered structure.

Start with problem framing: what are you optimizing for, and what are the hard constraints (latency, explainability, regulatory requirements)? Then move through data (how it arrives, what it looks like), features (real-time versus precomputed, freshness requirements), model choice (why this family, what trade-offs), serving (how it runs in production), and monitoring (how you know when something breaks). Sardine's work touches all of these layers, so thinking end-to-end signals that you can own a model from idea to production.

When discussing trade-offs, name the stakeholder.

Instead of saying 'there is a trade-off between precision and recall,' say 'a higher recall setting catches more fraud but blocks more genuine customers, which the customer experience team and the compliance team both care about.' Grounding trade-offs in who is affected shows you understand ML as a product decision, not just a technical one.

Domain knowledge, not just ML theory.

Sardine's interviewers are not looking for someone who can recite gradient boosting math. They want someone who understands why fraud ML is specifically hard: adversarial actors who adapt, extreme class imbalance, real-time latency constraints, and the asymmetric cost of a wrong call in both directions. Connecting your ML knowledge to these specific challenges will set you apart.

Production experience over notebook experiments.

Candidates who have only built models in notebooks or competition environments typically struggle in the system design discussion. Sardine's ML runs at scale in production. They want to see that you have shipped something, debugged it when it broke in the real world, and thought about monitoring and drift from the very beginning.

Business impact awareness.

ML engineers at sardine work closely with product and compliance teams. Interviewers pay attention to whether you frame your work in terms of outcomes (fraud caught, customer blocks avoided, revenue protected) rather than just model metrics. Candidates who only talk about AUC without connecting it to a business result tend to receive lower evaluations.

Clear, structured communication.

For system design especially, interviewers want to follow your thinking in real time. Speak out loud as you work through a problem. State your assumptions before you build on them. Ask a clarifying question before you dive in rather than after you have committed to a direction.

Fraud ML fundamentals first.

Review techniques specific to fraud detection: imbalanced learning methods (oversampling, cost-sensitive training, threshold calibration), anomaly detection approaches (isolation forests, autoencoders), and graph-based fraud ring detection. Read publicly available engineering writing from fintech ML teams. Understand what AML means in practice and why explainability matters in regulated contexts.

System design practice out loud.

Practice designing an end-to-end fraud scoring system by speaking through it, not just writing notes. Cover data ingestion, feature pipelines (real-time versus batch), model serving, and monitoring. State trade-offs explicitly as you go. Review how feature stores work and when precomputation is preferable to real-time computation.

Coding and ML depth.

Practice ML coding problems: implementing evaluation metrics from scratch, writing feature engineering logic on time-series transaction data, and working with graph data structures. Review gradient boosting internals and practice explaining SHAP values in plain language to a non-technical audience.

Sardine-specific research.

Read sardine's public blog posts and product documentation to understand the problems they are solving. Prepare your STAR stories focused on production ML work. Do a mock interview with a peer where you explain a past ML project in under five minutes and then field follow-up questions.

While you prep, knok checks 150+ job sites nightly, applies to ML Engineer roles that match your resume, and messages HR for you, so your job search keeps moving while you focus on interview preparation.

Treating fraud as a generic classification problem.

Candidates who talk about general classification without acknowledging the adversarial, imbalanced, and compliance-heavy nature of fraud signal a lack of domain awareness. Make sure your examples and answers are grounded in why fraud ML is specifically hard.

Ignoring latency and production constraints.

Saying 'I would use a large neural network' without discussing how it fits into a real-time scoring pipeline is a red flag. Always think out loud about serving infrastructure, latency budgets, and what happens under traffic spikes.

Reporting only model metrics.

Candidates who say 'my model achieved a good AUC' without explaining the business outcome (fraud caught, customers unblocked, revenue protected) miss what sardine's interviewers are actually listening for. Translate every metric into a business result.

Skipping clarifying questions in system design.

Jumping straight into a design without asking about latency requirements, data volume, regulatory constraints, or the team's existing infrastructure often leads to a solution that does not fit the actual problem. Interviewers want to see you gather requirements before you build.

Underselling the full ML lifecycle.

Many candidates describe their work as 'I built a model that achieved...' without mentioning deployment, monitoring, retraining cadence, or incidents they handled. At sardine, what happened after you shipped is often the most interesting part. Make sure your stories cover the full lifecycle.