Walmart Labs·Data Scientist·Onsite - System Design / Architecture·Senior

Senior

Apr 2026

Summary

System design round at Walmart Labs for a Data Scientist role, focused entirely on building an LLM-based interviewer agent. Pretty deep question that touched everything from prompt engineering to cost controls, so it wasn't a quick chat.

Questions Asked (1)

Design an LLM-based agent that can conduct technical and behavioral interviews. Walk through prompt design and persona, retrieval over question banks and rubrics, conversation state management, follow-up question generation, scoring and feedback mechanisms, evaluation strategy, and cost and latency tradeoffs.

System DesignTechnical Trade-offsProduct Sense & Ideation

Author's notes

This is a big one and I underestimated how much ground it covers.

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AI HintsAI Generated

Suggested Approach

Start by framing the agent as a modular system with clear components: prompt design, retrieval, state management, follow-up generation, scoring, and evaluation. Then walk through each component, highlighting trade-offs and how you would evaluate and optimize for cost and latency. Emphasize the importance of structured rubrics and human-in-the-loop validation.

Pro tip: Anchor your design in real interview best practices: use behavioral anchors and rubrics to ensure fairness, and always include a fallback to human interviewers for edge cases. Mention that you would A/B test the agent against human interviews to measure bias and effectiveness.

1. Define the Agent's Role and Persona

Specify the agent's persona (e.g., friendly but professional) and its objectives: to conduct structured interviews, ask follow-ups, and provide consistent scoring. Use prompt engineering to enforce the persona and interview flow.

2. Design Retrieval and Question Selection

Implement retrieval over a curated question bank and rubrics using embeddings and vector search. Dynamically select questions based on role, difficulty, and candidate responses, ensuring coverage of technical and behavioral competencies.

3. Manage Conversation State and Follow-ups

Maintain a state machine or memory to track the interview phase, asked questions, and candidate answers. Generate context-aware follow-up questions using the LLM, guided by rubrics to probe deeper into incomplete or ambiguous responses.

4. Implement Scoring and Feedback

Use the LLM to score responses against rubrics, providing numeric ratings and rationale. Aggregate scores to generate a final evaluation and constructive feedback, with confidence scores to flag uncertain cases for human review.

5. Evaluate and Optimize for Cost and Latency

Set up offline and online evaluation metrics (e.g., agreement with human scores, bias, latency, cost per interview). Optimize by caching, batching, using smaller models for simple tasks, and streaming responses to reduce perceived latency.

Key Points to Mention

Prompt design: use few-shot examples, chain-of-thought for scoring, and explicit instructions to avoid bias.
Retrieval: hybrid search (keyword + semantic) over question banks and rubrics, with metadata filtering.
State management: use a finite state machine or dialogue manager to track progress and ensure all competencies are covered.
Follow-up generation: condition on candidate's answer and rubric gaps, with prompts that encourage elaboration.
Scoring: rubric-based with behavioral anchors, calibration against human ratings, and confidence thresholds.
Cost/latency: trade-offs between model size, retrieval complexity, and real-time interaction; use caching and async processing.

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