Practice LeetCode mock interviews to get a job at Big Tech, Start ups, etc.

Systems Thinking is Non-Negotiable: Stripe PMs are expected to think in systems — how products interact with each other, how incentives compound, how seemingly small changes ripple through ecosystems. Technical Depth is Expected: Even for non-technical products, PMs are deeply embedded with engineers and are expected to understand architectural tradeoffs, API design principles, and backend implications. Customer Empathy Meets Business Precision: Stripe deeply values user-first thinking and expects PMs to anchor decisions in business impact. High Ownership Culture: PMs at Stripe often operate like mini-GMs. To stand out: Structured, First-Principles Thinking; Show System-Level Awareness; Technical Curiosity; Demonstrate High Standards.

Prepare 2 stories per PEI theme. Structure your stories using a desired format - there are a few formats (STAR, SCR, SAIL, SCAR) - the main points to cover are: Situation / context, Challenge, Consequence of not solving the challenge, What you personally did (not the team you worked in), Result / outcome, and Learning When in the interview, offer the interviewer 2 stories to choose from "That's an interesting question, 2 stories come to mind... provide 10 second executive summary of each... "which one appeals more to you?" - let them choose what is more interesting to them - this way you will have them engaged from the start. Walk through your story along your structure, maintain top-down communication, do not ramble, allow natural pauses for interviewer to engage and ask questions Be detailed and thorough in your responses, prepare what you thought, prepare verbatims of what people said, how you responded, how you felt in the situation - show the story is actually genuine, make the interviewer feel they were there with you in the situation

We have an hour-long interview where the candidate is given a high-level problem. We expect candidates to ask questions and refine both functional and non-functional requirements. Since the problem statement is intentionally ambiguous, candidates must make assumptions and explicitly list and agree on them. Once we're aligned on scope, we ask the candidate to draw a diagram explaining the system's components, starting from a high-level picture and diving into details. We encourage candidates to think aloud so we can evaluate their reasoning process, problem-solving approach, and ability to handle ambiguity. System design interviews showcase a candidate's experience, theoretical knowledge, and technical expertise. We don't expect candidates to create a perfect design—after all, such systems are built by large teams over years. Instead, we're looking for sound reasoning and a solid approach. Candidates should remember that it's impossible to cover everything in an hour. They are evaluated on their ability to identify appropriate design patterns, demonstrate technical knowledge, and describe a solution that considers key details. A common pitfall is over-engineering a system that doesn't need to handle massive scale, especially when business requirements call for resource efficiency.

Having a technical background works best however don't be shy to take up a program manager role and then interview and switch to a TPM role once you've learnt the ropes The best TPMs i've worked with have been SWE's who converted to TPMs-- they have kickass technical depth that comes naturally to them. However, I have worked with TPMs who were english majors. This means that you can learn a domain over time. It's helpful to be someone who is kind, technical and organised, so that the team can lean on you to figure their schedule out. The most respected TPMs were humble and good listeners and had great documentation skills.

Given AI's long-term strategic impact, I believe foundational knowledge of AI is essential for any PM aiming to drive sustained product success. I focus on three core learning themes for preparation: 1. Learning ML Concepts (via focused online courses/certifications): You'll need to learn the basic science behind AI. This means understanding core Machine Learning concepts - what a model is, how it 'learns' from data (training), and the difference between giving it labeled examples versus letting it find patterns on its own. You don't need to code it, but you must know how it works to define what's possible. 2. Practically Driving Business Value with AI (via identifying side projects, Capstone or even opportunities at your current company): Focus on how AI can deliver real business results. This means identifying opportunities where AI can directly increase efficiency, reduce operational costs, or significantly enhance the speed and quality of the user experience beyond what standard software can do. 3. Designing for Uncertainty (via focused online material): You must learn to design for failure. Traditional software either works or crashes, but AI is always 'sort of right.' The PM's job is to design the product experience to manage user expectations, clearly communicate when the AI might be wrong, and build reliable backup plans (like a human review) to maintain user trust.

Most candidates focus on tools. Strong candidates focus on fundamentals. 1) LLM basics (non-negotiable) Context limits, tokenization, cost/latency, and failure modes (hallucination, prompt sensitivity) 2) Retrieval (RAG is table stakes) Embeddings, vector search tradeoffs, chunking, grounding 3) System design thinking Pipelines: Input → Retrieval → Generation → Verification Plus caching, cost, and reliability 4) Evaluation (biggest gap) Not just accuracy → faithfulness, usefulness, safety Bottom line If you don’t understand how the system fails and how to measure it, you’re not ready.