← Early-stage Startup Interview Insights
The instructions were vague and I kept waiting for the interviewers to redirect me, but they just sat there nodding.
Start by clarifying the scope and goals of the review, then systematically examine the codebase across key dimensions like data handling, model training, evaluation, and deployment. Prioritize issues based on impact and feasibility, and propose actionable improvements with trade-offs.
Pro tip: Focus on high-impact, low-effort fixes first—early-stage startups value pragmatic solutions over perfection. Also, frame issues as opportunities for improvement rather than criticisms.
Ask about the model's purpose, current performance, team size, and timeline to tailor your review. This shows you understand the business context and avoid over-engineering.
Check for data leakage, inconsistent preprocessing, missing values, and reproducibility. Data issues often cause the biggest problems in ML systems.
Look for proper train/validation/test splits, overfitting, inappropriate metrics, and lack of hyperparameter tuning. Suggest improvements like cross-validation or better metrics.
Identify code smells, lack of modularity, missing tests, and poor documentation. Propose refactoring and adding tests to ensure reliability.
Categorize issues by impact and effort, and suggest a roadmap. Discuss trade-offs between quick fixes and long-term refactoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Turned out the solution was basically two dataclasses, maybe ten lines total, mostly copy-pasted from existing code in the repo with variable names swapped.
Start by clarifying the evaluation goals and constraints, then outline a modular architecture with clear interfaces. Walk through the core components—dataset, judge prompt, model invocation, parsing, and metrics—and discuss trade-offs and failure modes. Emphasize iterative refinement and validation against human judgments.
Pro tip: Mention that you'd start with a small, high-quality labeled dataset to calibrate the judge and measure agreement (e.g., Cohen's kappa) before scaling. This shows you prioritize reliability over speed and understand the importance of ground truth.
Ask about the evaluation task (e.g., pairwise comparison, scoring), input/output formats, latency/cost constraints, and how results will be used. This ensures alignment and prevents over-engineering.
Propose a modular pipeline: dataset loader, prompt template, LLM API client, response parser, and metrics calculator. Define clear interfaces so components can be swapped (e.g., different models or prompts).
Design a prompt that instructs the LLM to act as an impartial judge, with clear criteria, examples (few-shot), and output format (e.g., JSON). Include chain-of-thought reasoning to improve accuracy.
Code the pipeline, then validate the judge against a human-labeled subset. Compute agreement metrics (e.g., accuracy, F1, Cohen's kappa) and iterate on the prompt or model until satisfactory.
Address parsing errors, API failures, and rate limits with retries and fallbacks. Discuss cost optimization (e.g., caching, batching) and monitoring for drift in judge performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is the one I actually felt good about.
Start by clarifying the task, current prompt, and accuracy metric, then propose a systematic iteration process: establish a baseline, hypothesize specific prompt weaknesses, make targeted modifications, and evaluate with a held-out test set. Emphasize controlled A/B testing and data-driven decisions to ensure improvements are real and not due to noise.
Pro tip: Before changing the prompt, quantify the baseline error and categorize failure modes; this prevents random tweaking and shows you can diagnose before prescribing. Also, version prompts like code and log all experiments to enable reproducibility and learning.
Confirm the exact task, success criteria (e.g., accuracy, F1), and constraints (latency, cost). Ensure you have a representative test set and baseline performance.
Examine baseline errors to identify patterns: ambiguous instructions, missing context, formatting issues, or model biases. Prioritize the most impactful issues.
Based on failure analysis, make targeted changes: add examples, clarify instructions, adjust temperature, or restructure output format. Change one variable at a time when possible.
Run both original and modified prompts on the same test set, compute metrics, and perform statistical significance testing. Use a holdout set to avoid overfitting.
If improvement is significant, deploy; otherwise, analyze results and iterate. Document changes, results, and learnings for future reference.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.