System Design - Batch Processing Service

In Anthropic's software engineering interviews, the Batch Processing Service (often framed as "Design a Batch Inference API") is a common system design problem. Unlike traditional LeetCode-style questions, this problem focuses on building a robust, production-ready system that connects synchronous user requests to high-throughput asynchronous execution on specialized hardware like GPUs. Reddit +3 1 9 3 2

Problem Statement Overview

You are tasked with designing an HTTP API or service that exposes a batch processing function for Large Language Model (LLM) inference. While individual users make single, synchronous requests, the internal system must intelligently group these requests into batches to maximize hardware utilization (e.g., a single GPU handling up to 100 inputs) while maintaining acceptable latency for each user. Exponent +1 4 9

Key Requirements

• Functional: Users submit single requests and receive a unique ID or wait for a synchronous result. The system must group up to Xcap X𝑋 (e.g., 100) requests into a single batch for efficient GPU execution. Status tracking (queued, running, completed, failed) must be available.
• Non-Functional: 0 3 4 8.) 2 Throughput vs. Latency: Balance large batches for efficiency without "starving" low-traffic models. Reliability: Handle partial failures within a batch (if one request fails, the others shouldn't necessarily fail). Scalability: The system should support multiple GPUs and handle traffic spikes using proper queuing. Exponent +4

Common Design Challenges & Follow-ups

• Hybrid Flush Policies: Interviewers often ask how to decide when to "flush" a batch. A common solution is a hybrid policy: flush when the batch reaches maximum size OR when a max-wait timer (e.g., 50ms) expires.
• Head-of-Line Blocking: Discussing how to use per-model or per-priority queues to ensure one slow request doesn't hold up unrelated traffic.
• Backpressure: How the system behaves when input volume exceeds GPU capacity.
• Resource Management: Explaining memory manipulation, GPU scheduling, and the storage of intermediate vs. final results. Exponent +4

Tips for the Interview

• Be Practical: Anthropic values "implementation-heavy" thinking. Don't just say "add a queue"; describe the specific queueing strategy and error schema.
• Expect Evolving Constraints: Interviewers may introduce a new bottleneck mid-interview (e.g., "what if the bandwidth is constrained?") to see how you adapt your design.
• Focus on Trade-offs: Be ready to defend why you chose a specific batch size or wait-time over another. Exponent +5

Would you like to explore a high-level architecture for this specific batching service or see how it differs for asynchronous bulk processing?

[0] - Anthropic System Design Interview (2026 Guide) - Exponent [1] - Anthropic SWE interview loop, full breakdown of all 5 rounds - Reddit [2] - Anthropic SWE Interview Loop Coding & System Design - Reddit [3] - Batched Model Runs - Hello Interview [4] - Anthropic Interview Questions (Updated 2026) - Exponent [5] - Inside the Anthropic SWE Interview Loop: A Full Breakdown of All 5 ... [6] - Anthropic System Design Interview Questions - Coditioning [7] - Anthropic Software Engineer Interview Guide 2026 - PracHub [8] - Design a batch inference API | Anthropic Interview Question [9] - System Design - Batch Processing Service - Anthropic Interview