rippling·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Rippling ML engineer round that was basically a coding design exercise: build an expense rule engine, explain your data model out loud, then let AI generate the actual code. The follow-up on scaling and observability is where it got interesting.

Questions Asked (3)

Design and implement an expense rule engine that takes a stream of expense entries and a configurable rule set (category, amount thresholds, date ranges, employee level) to classify each expense as approved, flagged, or rejected, then return aggregated results per rule. AI coding tools are allowed, but you need to walk through your approach, data model, and rule evaluation logic before generating any code.

System DesignData ModelingTechnical Trade-offs

Author's notes

The AI-allowed part sounds like a gift until you realize they want you to explain the design before touching the keyboard.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a modular architecture with a clear data model and rule evaluation pipeline. Walk through the rule engine design, including how rules are represented, evaluated, and aggregated, before discussing implementation details and trade-offs.

Pro tip: Emphasize extensibility and performance: design rules as composable predicates and use streaming aggregation to handle large volumes, showing you think beyond basic functionality.

1. Clarify Requirements and Scope

Ask questions to understand expected scale, rule complexity, latency needs, and output format. Confirm whether rules are static or dynamic, and how conflicts are resolved.

2. Define Data Model and Rule Representation

Design schemas for expense entries and rules. Represent rules as objects with conditions (category, amount range, date range, employee level) and an action (approve/flag/reject).

3. Design Rule Evaluation Engine

Outline a pipeline that parses rules, evaluates each expense against all applicable rules, resolves conflicts (e.g., priority or specificity), and assigns a final status.

4. Plan Aggregation and Output

Define how to aggregate results per rule (counts, sums, etc.) and per status. Consider streaming aggregation for scalability and how to handle late-arriving data.

5. Discuss Trade-offs and Extensibility

Compare batch vs. stream processing, rule conflict resolution strategies, and performance optimizations. Mention how to extend the system for new rule types or actions.

Key Points to Mention

Rule representation as composable predicates or a DSL for flexibility
Conflict resolution when multiple rules match (priority, specificity, or first-match)
Streaming vs. batch processing and implications for latency and throughput
Data partitioning and indexing for efficient rule evaluation at scale
Aggregation strategies (e.g., windowing, incremental updates) and handling late data
Extensibility: adding new rule types, actions, or integrating with ML models for anomaly detection

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

How would you scale this rule engine to handle high transaction volumes?

System DesignTechnical Trade-offs

Author's notes

Talked about partitioning the expense stream by employee or department so rules evaluate in parallel, and caching compiled rule predicates so you're not re-parsing configs on every event.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the current architecture and scale requirements, then propose a multi-layered scaling strategy that addresses data ingestion, rule evaluation, and output delivery. Emphasize trade-offs between latency, throughput, and cost, and highlight how ML models can be integrated efficiently.

Pro tip: Demonstrate awareness of Rippling's multi-tenant, real-time payroll and HR use case by discussing tenant isolation and bursty workloads. Mention that scaling isn't just about infrastructure—it's also about optimizing rule complexity and leveraging ML for dynamic rule prioritization.

1. Clarify Requirements and Constraints

Ask about expected transaction volume, latency SLAs, rule complexity, and whether the system is batch or real-time. Understand current bottlenecks and scaling goals.

2. Architect for Horizontal Scalability

Propose partitioning rules and data (e.g., by tenant or rule type) to enable parallel processing. Use a distributed stream processing framework like Kafka and Flink for real-time evaluation.

3. Optimize Rule Evaluation

Discuss techniques like rule indexing, caching frequent rule outcomes, and compiling rules into efficient decision trees or Rete networks. Consider ML-based rule prioritization to reduce unnecessary evaluations.

4. Ensure Fault Tolerance and Elasticity

Design for auto-scaling, backpressure handling, and exactly-once processing. Use checkpointing and state management to recover from failures without data loss.

5. Monitor, Test, and Iterate

Implement comprehensive monitoring (latency, throughput, error rates) and load testing. Use canary deployments and A/B testing to validate scaling improvements.

Key Points to Mention

Partitioning strategies (e.g., by tenant, rule category) to enable parallel processing
Use of stream processing frameworks (Kafka, Flink) for real-time rule evaluation
Rule optimization techniques: indexing, caching, Rete algorithm, decision trees
ML integration: dynamic rule prioritization, anomaly detection for scaling triggers
Trade-offs between latency, throughput, cost, and consistency
Multi-tenancy considerations: isolation, noisy neighbor problems, resource quotas

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

What monitoring metrics would you expose for this rule engine, specifically around rule hit rate, latency, and false-positive rate?

Product Analytics & MetricsSystem Design

Author's notes

This one I actually liked.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the rule engine's purpose and business context, then structure your answer around the three requested metric categories: hit rate, latency, and false-positive rate. For each, define specific metrics, explain how to instrument them, and tie them to actionable insights for model improvement and system health.

Pro tip: Emphasize that false-positive rate must be measured against ground truth labels, which may require delayed feedback loops; propose a proxy metric like user override rate to monitor in real-time. Also, mention segmenting metrics by rule ID, user cohort, and time to detect drift and prioritize fixes.

1. Clarify context and goals

Ask about the rule engine's role (e.g., fraud detection, content moderation) and business objectives to tailor metrics. Confirm what 'false positive' means in this context and how ground truth is obtained.

2. Define hit rate metrics

Propose overall rule hit rate (rules triggered per evaluation), per-rule hit rate, and distribution of hits across rules. Include coverage metrics like percentage of events evaluated by at least one rule.

3. Define latency metrics

Suggest end-to-end latency (p50, p95, p99), per-rule evaluation time, and queue wait time. Highlight the need to monitor latency under varying load and set SLOs.

4. Define false-positive metrics

Outline false-positive rate (FP / (FP+TN)), precision, and recall if labels are available. For real-time monitoring, propose proxy metrics like user override rate, appeal rate, or downstream conversion impact.

5. Instrumentation and actionability

Explain how to log rule evaluations with metadata (rule ID, timestamp, features) and aggregate metrics. Suggest dashboards, alerts, and feedback loops to retrain or adjust rules.

Key Points to Mention

Segment metrics by rule ID, user cohort, and time to detect drift and prioritize fixes.
Use percentiles (p50, p95, p99) for latency to capture tail behavior.
Define false-positive rate precisely and acknowledge ground truth delay; use proxy metrics like override rate for real-time monitoring.
Monitor rule hit rate distribution to identify overly broad or narrow rules.
Set up alerts for anomalies in hit rate, latency, and false-positive rate.
Tie metrics to business impact (e.g., revenue loss, user churn) to prioritize improvements.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.