Roblox·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

Jun 2026

Summary

Two-part system design interview at Roblox for a software engineering role. Both parts were dense and covered a lot of ground fast. Left feeling like I had scratched the surface on most topics rather than going deep on any of them.

Questions Asked (2)

Design a resource loader that supports multiple resource types such as images, configs, and ML models. Cover the public API surface, how new types get plugged in, async and concurrent loading, prioritization, batching, caching at both memory and disk layers, eviction, deduplication, retries and timeouts, dependency resolution, streaming or partial loads, failure isolation, thread safety, and observability.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This was a beast of a question.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then present a layered architecture with a clear public API and pluggable type handlers. Walk through the loading pipeline from request to completion, covering concurrency, caching, and failure handling, and justify trade-offs at each layer.

Pro tip: Emphasize that the loader should be a generic framework with type-specific plugins, and that observability and failure isolation are first-class concerns, not afterthoughts. This shows you think about production readiness and extensibility.

1. Clarify Requirements and Scope

Ask about expected scale, latency SLAs, resource types, and deployment environment to tailor the design. Confirm whether the loader is client-side, server-side, or both, and what dependencies exist.

2. Define Public API and Extension Points

Design a simple, async API (e.g., load(resourceId, options)) and a plugin interface for type handlers. Explain how new types are registered and how the loader remains agnostic to resource specifics.

3. Design the Loading Pipeline

Describe the end-to-end flow: request deduplication, prioritization, batching, dependency resolution, and streaming. Cover how concurrency is managed and how retries and timeouts are applied.

4. Implement Caching and Eviction

Detail a two-tier cache (memory and disk) with appropriate eviction policies (e.g., LRU, TTL). Explain cache key design, invalidation, and how caching interacts with deduplication and streaming.

5. Ensure Robustness and Observability

Discuss failure isolation (e.g., bulkheads, circuit breakers), thread safety, and metrics/logging/tracing. Explain how to monitor cache hit rates, load latencies, and error rates.

Key Points to Mention

Pluggable type handlers via a registry and interface, enabling new resource types without modifying core logic.
Async and concurrent loading with a scheduler that supports prioritization (e.g., priority queues) and batching to optimize I/O.
Two-tier caching (memory and disk) with eviction policies (LRU, TTL) and deduplication of in-flight requests.
Retries with exponential backoff, timeouts, and dependency resolution (e.g., DAG-based loading order).
Failure isolation using bulkheads or circuit breakers to prevent cascading failures, and thread safety via locks or actor model.
Observability through metrics (latency, hit rate, error rate), structured logging, and distributed tracing.

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

Design a ROS-style publish/subscribe messaging system. Address topic definitions, message schema and serialization, service discovery, QoS and reliability modes, message ordering, replay and durability, backpressure and flow control, slow or disconnected subscribers, partitioning and scaling across nodes, real-time and latency constraints, clock sync, fault tolerance, and security. Also compare brokered vs brokerless and federated topologies and justify your design choice.

System DesignTechnical Trade-offsData Modeling

Author's notes

The brokered vs brokerless comparison is where I had the most to say, which was kind of backwards since it came at the end.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a high-level architecture that addresses all listed aspects. Compare brokered vs brokerless and federated topologies, justify your choice based on trade-offs, and dive into key components like QoS, discovery, and fault tolerance. Conclude with how your design meets Roblox's scale and real-time needs.

Pro tip: Anchor your design around a concrete use case (e.g., real-time game telemetry) to make trade-offs tangible, and explicitly state assumptions about scale, latency, and failure models to show senior-level thinking.

1. Clarify Requirements and Constraints

Ask about scale (nodes, messages/sec), latency targets, reliability needs, and deployment environment. Define assumptions to scope the design.

2. High-Level Architecture and Topology Choice

Propose a brokered, brokerless, or federated design. Compare trade-offs (e.g., simplicity vs. latency, central point of failure vs. discovery complexity) and justify your pick.

3. Core Components and Protocols

Detail topic definitions, message schema/serialization, service discovery, QoS modes, ordering, durability/replay, backpressure, and handling slow/disconnected subscribers.

4. Scaling, Real-Time, and Fault Tolerance

Explain partitioning, scaling across nodes, clock sync, latency constraints, and fault tolerance mechanisms (replication, failover, heartbeats).

5. Security and Wrap-Up

Cover authentication, authorization, encryption, and secure discovery. Summarize how the design meets requirements and note potential improvements.

Key Points to Mention

QoS levels (e.g., at-most-once, at-least-once, exactly-once) and their impact on reliability and latency.
Message ordering guarantees (per-topic, per-publisher) and how to handle out-of-order messages.
Durability and replay: persistent storage, message retention policies, and replay for late subscribers.
Backpressure and flow control: credit-based, rate limiting, and dropping policies for slow consumers.
Partitioning and scaling: consistent hashing, sharding topics, and load balancing across brokers.
Clock synchronization (e.g., NTP, PTP) and its role in ordering and real-time constraints.
Security: TLS, authentication (e.g., mTLS, JWT), authorization (ACLs), and encryption at rest/in transit.
Brokered vs brokerless vs federated: trade-offs in latency, complexity, fault tolerance, and scalability.

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