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Palo Alto Networks·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
Jun 2026

Summary

System design round at Palo Alto Networks for a software engineering role. The whole thing was one big sprawling question about building an ML orchestration platform, and it went pretty deep across a lot of dimensions.

Questions Asked (2)

Q1

Design a platform that orchestrates multiple independent ML model services (classification, embeddings, re-ranking, etc.) into end-to-end workflows for external users. Cover request routing, data flow between models, schema validation, intermediate storage and caching, results storage, model version management, failure handling, monitoring, scaling, and multi-tenant auth with rate limiting.

System DesignAPI & IntegrationsTechnical Trade-offs
Author's notes

This question is basically a full system design exam crammed into one prompt.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design a high-level architecture that separates concerns: an API gateway for auth and rate limiting, an orchestrator for workflow execution, and a model registry for versioning. Walk through the end-to-end flow of a request, detailing data flow, validation, caching, failure handling, and monitoring at each stage, and justify key trade-offs.

Pro tip: Emphasize idempotency and exactly-once semantics for workflow steps to handle retries safely, and discuss how you'd version both models and workflow definitions to enable safe rollbacks and A/B testing.

1. Clarify Requirements and Scope

Ask about expected QPS, number of tenants, model types, latency SLOs, and data sensitivity. Define functional and non-functional requirements to guide design decisions.

2. High-Level Architecture

Sketch the main components: API gateway, orchestrator, model services, model registry, storage layers, and monitoring. Explain how they interact and the flow of a request.

3. Deep Dive into Key Areas

Detail request routing, schema validation, data flow between models, intermediate storage/caching, results storage, version management, failure handling, and multi-tenant auth with rate limiting.

4. Address Scalability and Reliability

Discuss scaling strategies (horizontal scaling, autoscaling, partitioning), failure modes (timeouts, retries, circuit breakers), and monitoring/alerting for SLAs.

5. Discuss Trade-offs and Alternatives

Compare design choices (e.g., synchronous vs. asynchronous orchestration, caching strategies, storage options) and justify your selections based on requirements.

Key Points to Mention

  • Use an API gateway for authentication, rate limiting, and request routing to backend services.
  • Implement schema validation at each step using JSON Schema or similar, with versioned schemas to ensure compatibility.
  • Employ a workflow orchestrator (e.g., state machine or DAG) that handles retries, timeouts, and idempotency for each model invocation.
  • Cache intermediate results (e.g., embeddings) with appropriate TTL and invalidation strategies to reduce latency and cost.
  • Store model versions and workflow definitions in a registry, enabling canary deployments and rollbacks.
  • Monitor end-to-end latency, error rates, and resource utilization per tenant, with distributed tracing for debugging.

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

Q2

When would you use RPC versus REST for communication between these ML services, and what are the trade-offs around latency, schema evolution, observability, and backward compatibility?

Technical Trade-offsAPI & IntegrationsSystem Design
Author's notes

Felt more confident here.

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AI HintsAI Generated

Suggested Approach

Start by defining the core differences between RPC and REST in the context of ML services, then systematically compare them across the four dimensions: latency, schema evolution, observability, and backward compatibility. Use a decision framework that considers factors like performance requirements, team familiarity, and ecosystem support, and conclude with a recommendation tailored to typical ML service communication patterns.

Pro tip: Emphasize that the choice often depends on whether the communication is internal (between microservices) or external (to clients), and that gRPC (a modern RPC framework) is increasingly popular for ML services due to its performance and streaming capabilities, but REST remains valuable for its simplicity and ubiquity.

1. Define RPC and REST

Briefly explain that RPC (Remote Procedure Call) focuses on invoking methods on a remote server as if they were local, while REST (Representational State Transfer) is an architectural style using HTTP verbs and resources. Mention common implementations like gRPC for RPC and HTTP/JSON for REST.

2. Compare Latency

Discuss how RPC (especially gRPC with HTTP/2 and Protobuf) typically offers lower latency and higher throughput due to binary serialization and multiplexing, while REST with JSON over HTTP/1.1 may have higher overhead. Note that latency also depends on network conditions and payload size.

3. Analyze Schema Evolution and Backward Compatibility

Explain that RPC with Protobuf enforces strict schema evolution rules (e.g., field numbers, reserved fields) and supports backward/forward compatibility if done correctly. REST with JSON is more flexible but lacks built-in schema enforcement, making compatibility management more manual (e.g., using OpenAPI).

4. Evaluate Observability

Highlight that REST's use of standard HTTP makes it easy to monitor with existing tools (e.g., logs, metrics, tracing). RPC, especially gRPC, requires additional tooling (e.g., gRPC interceptors, OpenTelemetry) but offers rich metadata and streaming observability.

5. Synthesize and Recommend

Conclude with a recommendation: use RPC (gRPC) for internal, high-performance, low-latency ML service communication where schema is stable and teams can invest in tooling; use REST for external APIs, simpler integrations, or when flexibility and broad compatibility are prioritized.

Key Points to Mention

  • gRPC uses HTTP/2 and Protobuf, enabling bidirectional streaming and efficient binary serialization, which reduces latency and bandwidth.
  • REST is stateless and leverages standard HTTP methods, making it easier to cache, debug, and integrate with web clients.
  • Schema evolution in Protobuf requires careful field numbering and reserved fields; JSON schema evolution is more ad-hoc but can be managed with OpenAPI.
  • Observability: REST benefits from standard HTTP logging and tracing; gRPC requires interceptors and may need additional setup for metrics and tracing.
  • Backward compatibility: Protobuf supports adding optional fields without breaking old clients; REST requires versioning strategies (e.g., URL versioning, content negotiation).
  • Consider the ecosystem: ML services often use Python, and while gRPC has good Python support, REST with Flask/FastAPI is more familiar to many developers.

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