Ankit Kashyap

People to know

People To Know In Modern Software

For FAANG/Microsoft senior and staff interviews, the goal is not trivia. Know what these people shaped, what tradeoffs they talk about, and what vocabulary they use when discussing systems, product, engineering culture, AI, and platforms.

Use this list as a map:

  • Microsoft / cloud / platforms: especially useful for Microsoft interviews and Azure-aware system design.
  • Distributed systems / engineering craft: useful for staff-level design judgment.
  • AI leaders: useful for current industry awareness and product strategy.
  • Open source / developer tools: useful for understanding ecosystems, leverage, and developer productivity.
  • Founder / product operators: useful for product sense, execution, and business context.

Microsoft, Cloud, And Platform Leaders

Satya Nadella

  • Why know him: CEO of Microsoft and the central figure behind Microsoft’s cloud-first, AI-first transformation.
  • What to learn: Platform strategy, culture change, enterprise product thinking, and how Microsoft frames AI as a productivity layer across work.
  • Interview lens: Useful for answering “why Microsoft” with more depth than product familiarity.
  • Links: X, LinkedIn, Microsoft bio

Kevin Scott

  • Why know him: Microsoft CTO and a key voice on AI, developer tools, and technical strategy across Microsoft.
  • What to learn: How big companies coordinate technical bets across many product groups.
  • Interview lens: Good reference point for discussing AI platform shifts, engineering leverage, and responsible deployment.
  • Links: Behind the Tech, LinkedIn, X

Jay Parikh

  • Why know him: Former Meta engineering leader and Lacework CEO; now a major Microsoft platform/AI executive.
  • What to learn: Infrastructure at massive scale, engineering organizations, and AI platform execution.
  • Interview lens: Useful when talking about the overlap between infra, security, AI tooling, and developer productivity.
  • Links: LinkedIn, X, Microsoft CoreAI announcement

Mark Russinovich

  • Why know him: CTO of Microsoft Azure, creator of Sysinternals, and one of the most respected Windows/Azure systems engineers.
  • What to learn: Operating systems, cloud reliability, incident response, security, and low-level engineering tradeoffs.
  • Interview lens: Great person to study for Azure, distributed systems, debugging, and technical depth.
  • Links: Azure blog, X, Sysinternals

Scott Hanselman

  • Why know him: Longtime Microsoft developer advocate, educator, and communicator across .NET, web development, and developer productivity.
  • What to learn: How to explain technical concepts clearly and empathetically.
  • Interview lens: Useful model for senior/staff communication: precise, human, and practical.
  • Links: Blog, X, Hanselminutes

Anders Hejlsberg

  • Why know him: Creator/architect behind Turbo Pascal, Delphi, C#, and TypeScript.
  • What to learn: Language design, gradual typing, developer ergonomics, and long-term platform evolution.
  • Interview lens: Useful for discussing API design, type systems, backward compatibility, and tooling.
  • Links: GitHub, Microsoft Research profile

Brendan Burns

  • Why know him: Kubernetes co-founder and senior Microsoft/Azure engineering leader.
  • What to learn: Containers, orchestration, cloud-native systems, and how open source becomes infrastructure.
  • Interview lens: Useful for system design questions involving deployment, control planes, orchestration, and platform abstractions.
  • Links: X, GitHub, Kubernetes blog posts

Distributed Systems And Engineering Craft

Martin Kleppmann

  • Why know him: Author of Designing Data-Intensive Applications, one of the best modern distributed systems books.
  • What to learn: Replication, partitioning, consistency, event logs, transactions, and failure modes.
  • Interview lens: Essential for senior system design because he teaches tradeoffs, not recipes.
  • Links: Website, Blog, X

Leslie Lamport

  • Why know him: Turing Award winner; foundational thinker in distributed systems, consensus, and formal reasoning.
  • What to learn: Logical clocks, Paxos, distributed correctness, and specification.
  • Interview lens: Useful when you want to reason cleanly about ordering, consistency, and impossibility.
  • Links: Microsoft Research, Writings

Werner Vogels

  • Why know him: Amazon CTO and one of the public faces of AWS architecture thinking.
  • What to learn: Scalability, reliability, operational excellence, event-driven systems, and cloud primitives.
  • Interview lens: Useful for design discussions involving resilience, blast radius, and operational ownership.
  • Links: All Things Distributed, X, LinkedIn

Martin Fowler

  • Why know him: Influential software architecture author and Chief Scientist at Thoughtworks.
  • What to learn: Refactoring, microservices, patterns, evolutionary architecture, and technical debt.
  • Interview lens: Useful for explaining why a design should evolve incrementally instead of chasing ideal architecture.
  • Links: Website, X, Articles

Kent Beck

  • Why know him: Creator of Extreme Programming, TDD pioneer, and major influence on modern engineering practice.
  • What to learn: Feedback loops, test design, simplicity, refactoring, and engineering economics.
  • Interview lens: Useful for senior/staff discussions about quality, delivery, and engineering culture.
  • Links: Website, X, GitHub

John Ousterhout

  • Why know him: Systems professor and author of A Philosophy of Software Design.
  • What to learn: Complexity, module boundaries, deep interfaces, and design simplicity.
  • Interview lens: Very useful for staff-level conversations about maintainability and long-lived codebases.
  • Links: Stanford page, Book

Charity Majors

  • Why know her: Co-founder of Honeycomb and a leading voice on observability, operations, and engineering culture.
  • What to learn: Observability, debugging production, high-cardinality telemetry, and socio-technical systems.
  • Interview lens: Useful for reliability, incident management, and “how would you operate this?” parts of design interviews.
  • Links: Blog, X, Honeycomb blog

Kelsey Hightower

  • Why know him: One of the most respected Kubernetes/cloud educators and former Google Cloud Distinguished Engineer.
  • What to learn: Clear mental models for Kubernetes, infrastructure, cloud-native systems, and operational simplicity.
  • Interview lens: Useful for explaining complex infra in simple terms.
  • Links: GitHub, X, Kubernetes The Hard Way

AI Labs, Research, And AI Product Strategy

Sam Altman

  • Why know him: CEO of OpenAI and one of the most visible leaders in the current AI platform race.
  • What to learn: AI product strategy, platform ecosystems, distribution, and the social/economic framing of AGI.
  • Interview lens: Useful for current-awareness questions around AI adoption, market structure, and product velocity.
  • Links: X, Blog, OpenAI

Greg Brockman

  • Why know him: OpenAI co-founder and president; known for engineering-heavy product demos and infrastructure work.
  • What to learn: Turning research into usable developer/product experiences.
  • Interview lens: Useful for thinking about how API platforms, demos, and infra combine into product momentum.
  • Links: X, Website, OpenAI profile

Andrej Karpathy

  • Why know him: Former OpenAI and Tesla AI leader, educator, and founder of Eureka Labs; one of the clearest AI explainers online.
  • What to learn: Neural networks, LLMs, software 2.0, AI education, and practical AI engineering intuition.
  • Interview lens: Useful for AI literacy, especially explaining LLMs without sounding hand-wavy.
  • Links: Blog, X, YouTube

Dario Amodei

  • Why know him: Co-founder and CEO of Anthropic, formerly at OpenAI; major voice on AI safety and frontier model scaling.
  • What to learn: Scaling laws, model safety, responsible deployment, and the economics of frontier AI labs.
  • Interview lens: Useful for discussing the tension between product speed, safety, and enterprise trust.
  • Links: Website, Anthropic, X

Ilya Sutskever

  • Why know him: OpenAI co-founder, deep learning researcher, and founder of Safe Superintelligence.
  • What to learn: Deep learning history, scaling, alignment concerns, and the research mindset behind modern LLMs.
  • Interview lens: Useful for understanding why foundation models became the dominant AI paradigm.
  • Links: X, Google Scholar, SSI

Mira Murati

  • Why know her: Former OpenAI CTO and leader behind major product/research launches; founder of Thinking Machines Lab.
  • What to learn: AI product leadership, research-to-product translation, and cross-functional execution.
  • Interview lens: Useful for product-sense discussions about shipping AI safely and usefully.
  • Links: X, Thinking Machines Lab

Demis Hassabis

  • Why know him: Co-founder/CEO of Google DeepMind; central figure behind AlphaGo, AlphaFold, and major AI research breakthroughs.
  • What to learn: Long-horizon research bets, scientific AI, reinforcement learning, and research organizations.
  • Interview lens: Useful for understanding the difference between research milestones and product platforms.
  • Links: X, Google DeepMind, Google Scholar

Fei-Fei Li

  • Why know her: Stanford professor, AI researcher, ImageNet leader, and major voice for human-centered AI.
  • What to learn: Computer vision, dataset-driven progress, responsible AI, and human-centered AI systems.
  • Interview lens: Useful for grounding AI conversations in data, evaluation, and human impact.
  • Links: Stanford profile, X, HAI

Andrew Ng

  • Why know him: Co-founder of Coursera, DeepLearning.AI, former Baidu AI leader, and one of the most influential AI educators.
  • What to learn: Practical ML, AI transformation strategy, data-centric AI, and education at scale.
  • Interview lens: Useful for explaining how AI gets adopted inside companies, not just how models work.
  • Links: Website, X, The Batch

Open Source, Developer Tools, And Developer Experience

Addy Osmani

  • Why know him: Google Chrome engineering leader and one of the best-known voices on web performance and frontend architecture.
  • What to learn: Performance budgets, Core Web Vitals, loading strategy, JavaScript cost, and developer tooling.
  • Interview lens: Useful for frontend/system design questions involving performance, reliability, and user experience.
  • Links: Website, Blog, X

Dan Abramov

  • Why know him: React core team alum, creator/co-creator of Redux, and influential frontend educator.
  • What to learn: React mental models, state management, UI architecture, and explaining abstractions clearly.
  • Interview lens: Useful for senior frontend and platform conversations around framework tradeoffs.
  • Links: Overreacted, X, GitHub

Mitchell Hashimoto

  • Why know him: Co-founder of HashiCorp and creator/leader behind tools like Vagrant, Packer, Terraform, and Nomad.
  • What to learn: Infrastructure as code, developer workflow design, open source commercialization, and product-led developer tools.
  • Interview lens: Useful for platform engineering and “build vs buy vs open source” discussions.
  • Links: Website, X, GitHub

Linus Torvalds

  • Why know him: Creator of Linux and Git, two of the most important foundations of modern software engineering.
  • What to learn: Kernel engineering, open source governance, distributed development, and pragmatic design.
  • Interview lens: Useful for appreciating how tools and operating systems shape every layer above them.
  • Links: GitHub, Linux kernel, Git

Founder And Product Operators Worth Tracking

Patrick Collison

  • Why know him: Co-founder/CEO of Stripe and one of the strongest public thinkers on developer-first products and company execution.
  • What to learn: APIs as products, payments infrastructure, writing culture, and high-quality execution.
  • Interview lens: Useful for product sense and for explaining how platform companies win developer trust.
  • Links: Website, X, Stripe

Jensen Huang

  • Why know him: Founder/CEO of NVIDIA, whose GPUs and software ecosystem are central to modern AI infrastructure.
  • What to learn: Hardware/software co-design, platform ecosystems, accelerated computing, and long-term strategic bets.
  • Interview lens: Useful for AI infrastructure awareness, especially compute bottlenecks and platform leverage.
  • Links: NVIDIA profile, NVIDIA blog

Top Developers Of All Time

There is no official ranking, but these are widely defensible names if someone asks about the most influential developers or computer scientists in software history.

Dennis Ritchie

  • Why know him: Creator of C and co-creator of Unix.
  • What he shaped: Operating systems, compilers, databases, networking, embedded systems, and most of modern infrastructure.
  • Why he is a great interview answer: His work had an unusually high simplicity-to-impact ratio: small, elegant ideas that became a foundation for generations.
  • Links: Bell Labs biography, C history notes

Ken Thompson

  • Why know him: Co-creator of Unix, creator of B, major contributor to Plan 9, UTF-8, and Go.
  • What he shaped: Systems programming, operating systems, text encoding, language design, and distributed development culture.
  • Why he is a great interview answer: He represents elegant systems engineering: simple abstractions, practical tools, and deep technical taste.
  • Links: Computer History Museum profile, Go authors

Donald Knuth

  • Why know him: Author of The Art of Computer Programming and creator of TeX.
  • What he shaped: Algorithms, program analysis, literate programming, typography, and rigorous computer science education.
  • Why he is a great interview answer: He is the person to mention when you want to signal respect for fundamentals and precision.
  • Links: Homepage, TAOCP

Alan Kay

  • Why know him: Pioneer of object-oriented programming, Smalltalk, graphical interfaces, and personal computing.
  • What he shaped: Modern UI thinking, IDEs, object-oriented design, and the idea of computers as creative personal tools.
  • Why he is a great interview answer: He is useful when discussing software as a medium for thought, not just code execution.
  • Links: Viewpoints Research, ACM Turing Award profile

Tim Berners-Lee

  • Why know him: Inventor of the World Wide Web: HTML, HTTP, URLs, and the first web browser/server.
  • What he shaped: The web as a universal information system and the foundation for modern internet products.
  • Why he is a great interview answer: His work shows the power of open standards and simple protocols that scale socially and technically.
  • Links: W3C profile, Web Foundation

Grace Hopper

  • Why know her: Compiler pioneer and major influence behind COBOL and business programming.
  • What she shaped: Programming language accessibility, compilers, and the idea that software should be closer to human reasoning.
  • Why she is a great interview answer: She is a strong answer when you want to emphasize making computing usable for more people.
  • Links: Yale biography, Computer History Museum profile

Barbara Liskov

  • Why know her: Turing Award winner known for data abstraction, CLU, the Liskov Substitution Principle, and distributed systems work.
  • What she shaped: Object-oriented design, type systems, modularity, and fault-tolerant distributed systems.
  • Why she is a great interview answer: Especially useful for architecture conversations about abstraction, correctness, and substitutability.
  • Links: MIT profile, ACM Turing Award profile

Leslie Lamport

  • Why know him: Foundational distributed systems thinker behind logical clocks, Paxos, and formal specification work.
  • What he shaped: Consensus, ordering, distributed correctness, LaTeX, and how engineers reason about concurrent systems.
  • Why he is a great interview answer: Very strong for system design interviews involving consistency, replication, and coordination.
  • Links: Microsoft Research profile, Writings

John McCarthy

  • Why know him: Creator of Lisp and one of the founders of artificial intelligence as a field.
  • What he shaped: Functional programming, symbolic AI, garbage collection ideas, and interactive computing.
  • Why he is a great interview answer: Useful when connecting AI history with programming language design.
  • Links: Stanford memorial page, Lisp history

Edsger Dijkstra

  • Why know him: Major figure in algorithms, structured programming, concurrency, and correctness.
  • What he shaped: Dijkstra’s algorithm, semaphores, disciplined programming, and rigorous reasoning about software.
  • Why he is a great interview answer: He signals respect for clarity, correctness, and reducing accidental complexity.
  • Links: EWD archive, ACM Turing Award profile

Bjarne Stroustrup

  • Why know him: Creator of C++.
  • What he shaped: Systems programming, high-performance software, object-oriented programming in production, and generic programming.
  • Why he is a great interview answer: Useful when discussing performance, abstraction cost, language evolution, and backward compatibility.
  • Links: Homepage, C++ FAQ

Guido van Rossum

  • Why know him: Creator of Python and former Microsoft Distinguished Engineer.
  • What he shaped: Developer productivity, readable programming, scripting, data science, backend tooling, and AI/ML workflows.
  • Why he is a great interview answer: Good for conversations about language ergonomics and why communities matter.
  • Links: Personal page, GitHub, X

Suggested Reading Order

  1. Microsoft focus: Satya Nadella, Kevin Scott, Mark Russinovich, Jay Parikh, Scott Hanselman.
  2. System design depth: Martin Kleppmann, Leslie Lamport, Werner Vogels, Charity Majors, John Ousterhout.
  3. AI awareness: Andrej Karpathy, Dario Amodei, Sam Altman, Mira Murati, Demis Hassabis, Fei-Fei Li.
  4. Developer tools/product: Addy Osmani, Mitchell Hashimoto, Patrick Collison, Jensen Huang.
  5. All-time foundations: Dennis Ritchie, Ken Thompson, Donald Knuth, Alan Kay, Tim Berners-Lee.

Quick Interview Use

  • Mention Mark Russinovich when talking about Azure, cloud reliability, operating systems, or security.
  • Mention Martin Kleppmann when discussing replication, consistency, data systems, or queues/logs.
  • Mention Charity Majors when discussing observability and production debugging.
  • Mention Addy Osmani when discussing frontend performance and web architecture.
  • Mention Andrej Karpathy when explaining LLMs, AI engineering, or “software 2.0”.
  • Mention Satya Nadella / Kevin Scott when framing why Microsoft is strategically interesting right now.
  • Mention Dennis Ritchie if asked for a favorite developer of all time.