Tesla·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

May 2026

Summary

Tesla ML Engineer interview with a meaty system design question around building an LLM-based arithmetic solver. One question, but it had a lot of layers to unpack and I don't think I covered all of them cleanly.

Questions Asked (1)

Design an LLM-based system that can solve arithmetic problems across a range of complexity, from simple expressions like 4+5 to things like summing 1 to 100. How do you decide when the model should answer directly versus calling a formula or running code? Walk through your chain logic, tool usage, prompting strategy, guardrails, error handling, and how you'd evaluate and track experiments.

System DesignTechnical Trade-offsA/B Testing & Experimentation

Author's notes

I started with the routing logic because that felt like the crux of it.

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AI HintsAI Generated

Suggested Approach

Start by framing the problem as a routing decision: classify the arithmetic query by complexity and risk, then choose the cheapest reliable path (direct LLM answer, formula, or code execution). Walk through a layered architecture with a router, tool integrations, verification, and a feedback loop for continuous improvement.

Pro tip: Emphasize that the router should be a lightweight, fast model (or even a rule-based classifier) to avoid latency and cost overhead, and always include a fallback to code execution for high-stakes or ambiguous cases.

1. Query Classification and Routing

Analyze the input to determine complexity (e.g., number of operations, presence of variables, range of numbers) and decide the initial path: direct answer, formula, or code execution. Use a small classifier or heuristics to keep latency low.

2. Tool Usage and Chain Logic

For formula-based problems (e.g., sum 1 to 100), invoke a symbolic math tool or pre-defined formula. For complex or ambiguous cases, generate and execute code in a sandbox. Chain multiple tools if needed, with intermediate verification.

3. Prompting Strategy and Guardrails

Use few-shot prompts to guide the LLM in generating correct tool calls or direct answers. Implement guardrails: validate inputs, restrict code execution to safe operations, and set timeouts. Include a confidence threshold to trigger fallback to code.

4. Error Handling and Verification

Detect errors from tools or code execution and retry with a different approach (e.g., fallback to code if formula fails). Verify results by cross-checking with a secondary method (e.g., run code twice with different seeds or use symbolic verification).

5. Evaluation and Experiment Tracking

Define metrics: accuracy, latency, cost per query, and tool usage rate. Set up A/B tests to compare routing strategies (e.g., always code vs. hybrid). Log all queries, decisions, and outcomes for offline analysis and continuous improvement.

Key Points to Mention

Router design: lightweight classifier vs. LLM-based routing, trade-offs in latency and accuracy
Tool integration: symbolic math libraries (e.g., SymPy), code execution sandboxes (e.g., Docker), and API calls
Prompt engineering: few-shot examples for tool use, chain-of-thought for complex problems, and output formatting
Guardrails: input validation, code safety, timeouts, and confidence thresholds for fallback
Error handling: retry logic, fallback strategies, and result verification (e.g., unit tests for generated code)
Evaluation: metrics (accuracy, latency, cost), A/B testing framework, and logging for experiment tracking

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