Two-part question and I did not pace myself well at all.
Use a structured STAR format for both stories, but emphasize the failure first to show humility and learning. For the failure, focus on root cause and systemic fixes; for the success, highlight your specific technical contributions and measurable impact. Tailor both to ML engineering at Reddit by including scale, experimentation, and cross-functional collaboration.
Pro tip: Choose a failure that is significant but not disqualifying, and explicitly state what you would do differently today. For the success, quantify impact with metrics like engagement lift or latency reduction, and credit the team while clearly delineating your role.
For each story, spend 1-2 sentences on the project, your role, and the business goal. Avoid excessive background; focus on what matters for the lesson or impact.
Explain what went wrong, your specific actions, and use a root cause analysis technique (e.g., 5 Whys) to identify the underlying issue. Be honest about your mistakes without being overly self-critical.
State what you learned and how you changed your approach. Give a concrete example of applying that lesson later to show growth.
Describe the project, your specific contributions (e.g., model architecture, feature engineering, deployment), and why it worked. Use 'I' for your actions and 'we' for team efforts.
Provide metrics (e.g., CTR lift, latency reduction, cost savings) and connect the success to Reddit's values or ML challenges (e.g., large-scale personalization, real-time inference).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.