The Pragmatic Engineer

• From Software Engineer to AI Engineer – with Janvi Kalra

  Supported by Our Partners•⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more.•⁠ Sinch⁠ — Connect with customers at every step of their journey.•⁠ Cortex⁠ — Your Portal to Engineering Excellence.—What does it take to land a job as an AI Engineer—and thrive in the role?In this episode of Pragmatic Engineer, I’m joined by Janvi Kalra, currently an AI Engineer at OpenAI. Janvi shares how she broke into tech with internships at top companies, landed a full-time software engineering role at Coda, and later taught herself the skills to move into AI Engineering: by things like building projects in her free time, joining hackathons, and ultimately proving herself and earning a spot on Coda’s first AI Engineering team.In our conversation, we dive into the world of AI Engineering and discuss three types of AI companies, how to assess them based on profitability and growth, and practical advice for landing your dream job in the field.We also discuss the following: • How Janvi landed internships at Google and Microsoft, and her tips for interview prepping• A framework for evaluating AI startups• An overview of what an AI Engineer does• A mini curriculum for self-learning AI: practical tools that worked for Janvi• The Coda project that impressed CEO Shishir Mehrotra and sparked Coda Brain• Janvi’s role at OpenAI and how the safety team shapes responsible AI• How OpenAI blends startup speed with big tech scale• Why AI Engineers must be ready to scrap their work and start over• Why today’s engineers need to be product-minded, design-aware, full-stack, and focused on driving business outcomes• And much more!—Timestamps(00:00) Intro(02:31) How Janvi got her internships at Google and Microsoft(03:35) How Janvi prepared for her coding interviews (07:11) Janvi’s experience interning at Google(08:59) What Janvi worked on at Microsoft (11:35) Why Janvi chose to work for a startup after college(15:00) How Janvi picked Coda (16:58) Janvi’s criteria for picking a startup now (18:20) How Janvi evaluates ‘customer obsession’ (19:12) Fast—an example of the downside of not doing due diligence(21:38) How Janvi made the jump to Coda’s AI team(25:48) What an AI Engineer does (27:30) How Janvi developed her AI Engineering skills through hackathons(30:34) Janvi’s favorite AI project at Coda: Workspace Q&A (37:40) Learnings from interviewing at 46 companies(40:44) Why Janvi decided to get experience working for a model company (43:17) Questions Janvi asks to determine growth and profitability(45:28) How Janvi got an offer at OpenAI, and an overview of the interview process(49:08) What Janvi does at OpenAI (51:01) What makes OpenAI unique (52:30) The shipping process at OpenAI(55:41) Surprising learnings from AI Engineering (57:50) How AI might impact new graduates (1:02:19) The impact of AI tools on coding—what is changing, and what remains the same(1:07:51) Rapid fire round—The Pragmatic Engineer deepdives relevant for this episode:•⁠ AI Engineering in the real world•⁠ The AI Engineering stack•⁠ Building, launching, and scaling ChatGPT Images—See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠—Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

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  1:09:30
• How Kubernetes is Built with Kat Cosgrove

  Supported by Our Partners•⁠ WorkOS — The modern identity platform for B2B SaaS.•⁠ Modal⁠ — The cloud platform for building AI applications.•⁠ Cortex⁠ — Your Portal to Engineering Excellence.—Kubernetes is the second-largest open-source project in the world. What does it actually do—and why is it so widely adopted?In this episode of The Pragmatic Engineer, I’m joined by Kat Cosgrove, who has led several Kubernetes releases. Kat has been contributing to Kubernetes for several years, and originally got involved with the project through K3s (the lightweight Kubernetes distribution).In our conversation, we discuss how Kubernetes is structured, how it scales, and how the project is managed to avoid contributor burnout.We also go deep into: • An overview of what Kubernetes is used for• A breakdown of Kubernetes architecture: components, pods, and kubelets• Why Google built Borg, and how it evolved into Kubernetes• The benefits of large-scale open source projects—for companies, contributors, and the broader ecosystem• The size and complexity of Kubernetes—and how it’s managed• How the project protects contributors with anti-burnout policies• The size and structure of the release team• What KEPs are and how they shape Kubernetes features• Kat’s views on GenAI, and why Kubernetes blocks using AI, at least for documentation• Where Kat would like to see AI tools improve developer workflows• Getting started as a contributor to Kubernetes—and the career and networking benefits that come with it• And much more!—Timestamps(00:00) Intro(02:02) An overview of Kubernetes and who it’s for (04:27) A quick glimpse at the architecture: Kubernetes components, pods, and cubelets(07:00) Containers vs. virtual machines (10:02) The origins of Kubernetes (12:30) Why Google built Borg, and why they made it an open source project(15:51) The benefits of open source projects (17:25) The size of Kubernetes(20:55) Cluster management solutions, including different Kubernetes services(21:48) Why people contribute to Kubernetes (25:47) The anti-burnout policies Kubernetes has in place (29:07) Why Kubernetes is so popular(33:34) Why documentation is a good place to get started contributing to an open-source project(35:15) The structure of the Kubernetes release team (40:55) How responsibilities shift as engineers grow into senior positions(44:37) Using a KEP to propose a new feature—and what’s next(48:20) Feature flags in Kubernetes (52:04) Why Kat thinks most GenAI tools are scams—and why Kubernetes blocks their use(55:04) The use cases Kat would like to have AI tools for(58:20) When to use Kubernetes (1:01:25) Getting started with Kubernetes (1:04:24) How contributing to an open source project is a good way to build your network(1:05:51) Rapid fire round—The Pragmatic Engineer deepdives relevant for this episode:•⁠ Backstage: an open source developer portal•⁠ How Linux is built with Greg Kroah-Hartman•⁠ Software engineers leading projects•⁠ What TPMs do and what software engineers can learn from them•⁠ Engineering career paths at Big Tech and scaleups—See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠—Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

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  1:08:47
• Building Windsurf with Varun Mohan

  Supported by Our Partners•⁠ Modal⁠ — The cloud platform for building AI applications•⁠ CodeRabbit⁠⁠ — Cut code review time and bugs in half. Use the code PRAGMATIC to get one month free.—What happens when LLMs meet real-world codebases? In this episode of The Pragmatic Engineer,  I am joined by Varun Mohan, CEO and Co-Founder of Windsurf. Varun talks me through the technical challenges of building an AI-native IDE (Windsurf) —and how these tools are changing the way software gets built. We discuss: • What building self-driving cars taught the Windsurf team about evaluating LLMs• How LLMs for text are missing capabilities for coding like “fill in the middle”• How Windsurf optimizes for latency• Windsurf’s culture of taking bets and learning from failure• Breakthroughs that led to Cascade (agentic capabilities)• Why the Windsurf teams build their LLMs• How non-dev employees at Windsurf build custom SaaS apps – with Windsurf!• How Windsurf empowers engineers to focus on more interesting problems• The skills that will remain valuable as AI takes over more of the codebase• And much more!—Timestamps(00:00) Intro(01:37) How Windsurf tests new models(08:25) Windsurf’s origin story (13:03) The current size and scope of Windsurf(16:04) The missing capabilities Windsurf uncovered in LLMs when used for coding(20:40) Windsurf’s work with fine-tuning inside companies (24:00) Challenges developers face with Windsurf and similar tools as codebases scale(27:06) Windsurf’s stack and an explanation of FedRAMP compliance(29:22) How Windsurf protects latency and the problems with local data that remain unsolved(33:40) Windsurf’s processes for indexing code (37:50) How Windsurf manages data (40:00) The pros and cons of embedding databases (42:15) “The split brain situation”—how Windsurf balances present and long-term (44:10) Why Windsurf embraces failure and the learnings that come from it(46:30) Breakthroughs that fueled Cascade(48:43) The insider’s developer mode that allows Windsurf to dogfood easily (50:00) Windsurf’s non-developer power user who routinely builds apps in Windsurf(52:40) Which SaaS products won’t likely be replaced(56:20) How engineering processes have changed at Windsurf (1:00:01) The fatigue that goes along with being a software engineer, and how AI tools can help(1:02:58) Why Windsurf chose to fork VS Code and built a plugin for JetBrains (1:07:15) Windsurf’s language server (1:08:30) The current use of MCP and its shortcomings (1:12:50) How coding used to work in C#, and how MCP may evolve (1:14:05) Varun’s thoughts on vibe coding and the problems non-developers encounter(1:19:10) The types of engineers who will remain in demand (1:21:10) How AI will impact the future of software development jobs and the software industry(1:24:52) Rapid fire round—The Pragmatic Engineer deepdives relevant for this episode:• IDEs with GenAI features that Software Engineers love• AI tooling for Software Engineers in 2024: reality check• How AI-assisted coding will change software engineering: hard truths• AI tools for software engineers, but without the hype—See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠—Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

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  1:27:54
• How to work better with Product, as an Engineer with Ebi Atawodi

  Supported by Our Partners•⁠ WorkOS — The modern identity platform for B2B SaaS.•⁠ The Software Engineer’s Guidebook: Written by me (Gergely) – now out in audio form as well.—How do you get product and engineering to truly operate as one team? Today, I’m joined by Ebi Atawodi, Director of Product Management at YouTube Studio, and a former product leader at Netflix and Uber.Ebi was the first PM I partnered with after stepping into engineering management at Uber, and we both learned a lot together. We share lessons from our time at Uber and discuss how strong product-engineering partnerships drive better outcomes, grow teams, foster cultures of ownership, and unlock agency, innovation, and trust.In this episode, we cover:• Why you need to earn a new team's trust before trying to drive change• How practices like the "business scorecard" and “State of the Union” updates helped communicate business goals and impact to teams at Uber• How understanding business impact leads to more ideas and collaboration• A case for getting to know your team as people, not just employees• Why junior employees should have a conversation with a recruiter every six months• Ebi’s approach to solving small problems with the bet that they’ll unlock larger, more impactful solutions• Why investing time in trust and connection isn't at odds with efficiency• The qualities of the best engineers—and why they’re the same traits that make people successful in any role• The three-pronged definition of product: business impact, feasibility, and customer experience• Why you should treat your career as a project• And more!—Timestamps(00:00) Intro(02:19) The product review where Gergely first met Ebi (05:45) Ebi’s learning about earning trust before being direct(08:01) The value of tying everything to business impact(11:53) What meetings looked like at Uber before Ebi joined(12:35) How Ebi’s influence created more of a start-up environment (15:12) An overview of “State of the Union” (18:06) How Ebi helped the cash team secure headcount(24:10) How a dinner out helped Ebi and Gergely work better together(28:11) Why good leaders help their employees reach their full potential(30:24) Product-minded engineers and the value of trust (33:04) Ebi’s approach to passion in work: loving the problem, the work, and the people(36:00) How Gergely and Ebi secretly bootstrapped a project then asked for headcount(36:55) How a real problem led to a novel solution that also led to a policy change(40:30) Ebi’s approach to solving problems and tying them to a bigger value unlock (43:58) How Ebi developed her playbooks for vision setting, fundraising, and more(45:59) Why Gergely prioritized meeting people on his trips to San Francisco (46:50) A case for making in-person interactions more about connection(50:44) The genius-jerk archetype vs. brilliant people who struggle with social skills (52:48) The traits of the best engineers—and why they apply to other roles, too(1:03:27) Why product leaders need to love the product and the business (1:06:54) The value of a good PM(1:08:05) Sponsorship vs. mentorship and treating your career like a project(1:11:50) A case for playing the long game—The Pragmatic Engineer deepdives relevant for this episode:• The product-minded software engineer• Working with Product Managers as an Engineering Manager or Engineer• Working with Product Managers: advice from PMs• What is Growth Engineering?—See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠—Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

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  1:15:27
• Building Reddit’s iOS and Android app

  Supported by Our Partners• Graphite — The AI developer productivity platform. • Sentry — Error and performance monitoring for developers.—Reddit’s native mobile apps are more complex than most of us would assume: both the iOS and Android apps are about 2.5 million lines of code, have 500+ screens, and a total of around 200 native iOS and Android engineers work on them. But it wasn’t always like this.In 2021, Reddit started to double down on hiring native mobile engineers, and they quietly rebuilt the Android and iOS apps from the ground up. The team introduced a new tech stack called the “Core Stack” – all the while users remained largely unaware of the changes. What drove this overhaul, and how did the team pull it off?In this episode of The Pragmatic Engineer, I’m joined by three engineers from Reddit’s mobile platform team who led this work: Lauren Darcey (Head of Mobile Platform), Brandon Kobilansky (iOS Platform Lead), and Eric Kuck (Principal Android Engineer). We discuss how the team transitioned to a modern architecture, revamped their testing strategy, improved developer experience – while they also greatly improved the app’s user experience. We also get into: • How Reddit structures its mobile teams—and why iOS and Android remain intentionally separate • The scale of Reddit’s mobile codebase and how it affects compile time• The shift from MVP to MVVM architecture• Why Reddit took a bet on Jetpack Compose, but decided (initially) against using SwiftUI• How automated testing evolved at Reddit • Reddit’s approach to server-driven-mobile-UI• What the mobile platforms team looks for in a new engineering hire• Reddit’s platform team’s culture of experimentation and embracing failure • And much more!If you are interested in large-scale rewrites or native mobile engineering challenges: this episode is for you.—Timestamps(00:00) Intro(02:04) The scale of the Android code base(02:42) The scale of the iOS code base(03:26) What the compile time is for both Android and iOS(05:33) The size of the mobile platform teams (09:00) Why Reddit has so many mobile engineers (11:28) The different types of testing done in the mobile platform (13:20) The benefits and drawbacks of testing (17:00) How Eric, Brandon, and Lauren use AI in their workflows(20:50) Why Reddit grew its mobile teams in 2021(26:50) Reddit’s modern tech stack, Corestack (28:48) Why Reddit shifted from MVP architecture to MVVM(30:22) The architecture on the iOS side(32:08) The new design system(30:55) The impact of migrating from Rust to GraphQL(38:20) How the backend drove the GraphQL migration and why it was worth the pain(43:17) Why the iOS team is replacing SliceKit with SwiftUI(48:08) Why the Android team took a bet on Compose (51:25) How teams experiment with server-driven UI—when it worked, and when it did not(54:30) Why server-driven UI isn’t taking off, and why Lauren still thinks it could work(59:25) The ways that Reddit’s modernization has paid off, both in DevX and UX(1:07:15) The overall modernization philosophy; fixing pain points (1:09:10) What the mobile platforms team looks for in a new engineering hire (1:16:00) Why startups may be the best place to get experience (1:17:00) Why platform teams need to feel safe to fail (1:20:30) Rapid fire round—The Pragmatic Engineer deepdives relevant for this episode:• The platform and program split at Uber• Why and how Notion went native on iOS and Android• Paying down tech debt • Cross-platform mobile development—See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠—Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

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  1:26:08

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