8 flagship loops
Magnificent Seven + Netflix
Click a company to see its rounds, framework deep-dive, role-specific checklists, and AI-policy notes for 2025–2026.
Choose a target
All 8 companies
Difficulty 1–5 reflects loop length + bar; reputation tags surface what each company is famous for.
NVIDIA
mag7
C++ and Python heavy; CUDA for GPU roles. LC medium baseline. Jensen flat org (40+ direct reports). Quantified stories expected.
Tesla
mag7
Codility/CoderPad. LC medium–hard. Embedded: C/C++, real-time, multithreading, drivers. Autopilot: C++ + CV (CNNs, sensor fusion).
Netflix
mag7
Less LC-heavy than FAANG. Practical: rate limiters, metadata cache, concurrency-safe DS, graph traversal for recommendations, interval problems. Production-quality code matters.
Apple
mag7
CoderPad, LC-medium dominant (rarely hard). Domain-heavy for specialized teams (Swift, C++, kernel, ML). Privacy-first system design.
Meta
mag7
CoderPad, Python/Java/C++/C#/Kotlin/TypeScript. 100-problem reference: 26% Easy, 60% Medium, 14% Hard. Behavioral grades self-awareness + ROI prioritization.
Microsoft
mag7
CodePair/Codility. LC easy–medium dominant. Azure-flavored system design. Model-Coach-Care framework for managers.
Amazon
mag7
LC medium dominant. Trees (LCA, vertical order, diameter), Graphs (BFS islands, course schedule), OOP design (parking lot, Amazon locker). Behavioral grades all 16 LPs.
Google / Alphabet
mag7
Graph + DP + backtracking heavy. LC hard appears more than peers. 4 hiring dimensions: GCA, Leadership, Role-Related Knowledge, Googleyness. STAR-L mandatory.