Amazon Research Scientist Interview Questions

At my previous company, I owned improving a critical, under-resourced system (Ownership). When stakeholders blamed external constraints, I prototyped a simpler architecture (Invent and Simplify), validated it with real data (Dive Deep), and delivered an 18% efficiency gain with zero downtime (Deliver Results). The team was skeptical initially, but I earned their trust through transparency and rigor. This experience showed me that Amazon's principles—especially Ownership, Bias for Action, and Frugality—differentiate great teams. I've modeled my approach around them ever since.

Sort the array. For each element, treat it as a pivot and use two pointers on remaining elements to find two numbers summing to target - pivot. Move pointers based on whether the sum is too small (move left pointer right) or too large (move right pointer left). This optimizes from O(n³) brute force to O(n²). Key insight: sorting unlocks the two-pointer technique. Skip consecutive duplicates to avoid duplicate triplets.

Combine a HashMap with a doubly-linked list. HashMap maps keys to list nodes (O(1) access); the list tracks order: least-recent at head, most-recent at tail. On get(), move the node to the tail. On put(), insert at the tail; if capacity overflows, remove the head. Doubly-linked structure enables O(1) node removal and reinsertion—the critical insight that avoids scanning.

I'm a full-stack engineer with 5+ years building scalable systems. Early career, I shipped payment infrastructure at a fintech startup, learning how ownership and bias-for-action drive results. Recently, I've focused on high-standards code review and mentoring—helping teams simplify complexity and catch issues early. I'm drawn to Amazon because I admire customer obsession and your bar-raising culture. I'm excited to tackle large-scale problems where attention to detail and invention directly impact customers.