# Resources (Curated, credible)

This repository is intentionally link-light inside each page; this file is the canonical resource list.

## Hiring loops, evaluation signals, and preparation

- Google: How we hire
  - https://www.google.com/about/careers/applications/how-we-hire/
- Amazon: Leadership Principles (core behavioral rubric)
  - https://www.amazon.jobs/en/principles
- Tech Interview Handbook (interview process, prep structure, resume guidance)
  - https://www.techinterviewhandbook.org/
  - Source repo: https://github.com/yangshun/tech-interview-handbook
- Tech Interview Handbook: coding interview rubrics (concrete evaluation dimensions)
  - https://www.techinterviewhandbook.org/coding-interview-rubrics/
- GitLab Handbook: Hiring & Talent Acquisition (transparent hiring-side principles)
  - https://handbook.gitlab.com/handbook/hiring/

## System design and distributed systems

- System Design Primer (canonical topics, question bank, approach)
  - https://github.com/donnemartin/system-design-primer
- Microservices: Martin Fowler (tradeoffs, organizational coupling)
  - https://martinfowler.com/articles/microservices.html
- Palantir: How to ace a systems design interview (thought process + tradeoffs)
  - https://www.palantir.com/2011/10/how-to-rock-a-systems-design-interview/
- Werner Vogels: A Word on Scalability (definition + why it must be designed in)
  - https://www.allthingsdistributed.com/2006/03/a_word_on_scalability.html

## Cloud architecture and reliability

- AWS Architecture Center / Well-Architected pillars
  - https://aws.amazon.com/architecture/
- AWS Well-Architected Framework (official docs)
  - https://docs.aws.amazon.com/wellarchitected/latest/framework/welcome.html
- Azure Architecture Center
  - https://learn.microsoft.com/azure/architecture/
- Azure application architecture fundamentals
  - https://learn.microsoft.com/en-us/azure/architecture/guide/
- Google Cloud Architecture Center + Well-Architected Framework
  - https://docs.cloud.google.com/architecture

## DevOps / platform fundamentals

- Kubernetes documentation
  - https://kubernetes.io/docs/home/
- Docker documentation
  - https://docs.docker.com/get-started/

## ML production readiness and MLOps

- Rules of ML (Google best practices for production ML engineering)
  - https://developers.google.com/machine-learning/guides/rules-of-ml
- The ML Test Score paper (rubric for production readiness, tests/monitoring)
  - https://research.google/pubs/pub46555/
- Machine Learning: The High Interest Credit Card of Technical Debt (system-level ML risk factors)
  - https://research.google/pubs/pub43146/
- Chip Huyen: MLOps Guide (career-oriented curriculum + case studies)
  - https://huyenchip.com/mlops/

## Engineering culture and operating models

- Netflix culture memo (high-performance culture, context not control)
  - https://jobs.netflix.com/culture
- Spotify engineering blog (recent operational practices and trends)
  - https://engineering.atspotify.com/

## Notes on usage

- This repo summarizes these resources in our own words.
- Do not copy/paste proprietary interview questions.
- Use resources to validate _principles_ (tradeoffs, evaluation signals), not to memorize solutions.