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.