The Engineering Leadership Podcast

This is a special episode, highlighting a session from ELC Annual 2025! OpenAI evolved from a pure research lab into the fastest-growing product in history, scaling from 100 million to 700 million weekly users in record time. In this episode, we deconstruct the organizational design choices and cultural bets that enabled this unprecedented velocity. We explore what it means to hire "extreme generalists," how AI-native interns are redefining productivity, and the real-time trade-offs made during the world's largest product launches. Featuring Sulman Choudhry (Head of ChatGPT Engineering) and Samir Ahmed (Technical Lead), moderated by Lawrence Bruhmeller (Eng Management @ Sigma). ABOUT SULMAN CHOUDHRYSulman leads ChatGPT Engineering at OpenAI, driving the development and scaling of one of the world’s most impactful AI products. He pushes the boundaries of innovation by turning cutting‑edge research into practical, accessible tools that transform how people interact with technology. Previously at Meta, Sulman founded and scaled Instagram Reels, IGTV, and Instagram Labs, and helped lead the early development of Instagram Stories.He also brought MetaAI to Instagram and Messenger, integrating generative AI into experiences used by billions. Earlier in his career, Sulman was on the founding team that built and launched UberEATS from the ground up, helping turn it into a global food delivery platform. With a track record of marrying technical vision, product strategy, and large‑scale execution, Sulman focuses on building products that meaningfully change how people live, work, and connect.ABOUT SAMIR AHMEDSamir is the Technical Lead for ChatGPT at OpenAI, where he currently leads the Personalization and Memory efforts to scale adaptive, useful, and human-centered product experiences to over 700 million users. He works broadly across the OpenAI stack—including mobile, web, services, systems, inference, and product research infrastructure.Previously, Samir spent nine years at Snap, working across Ads, AR, Content, and Growth. He led some of the company’s most critical technical initiatives, including founding and scaling the machine learning platform that powered nearly all Ads, Content, and AR workloads, handling tens of billions of requests and trillions of inferences daily.ABOUT LAWRENCE BRUHMELLERLawrence Bruhmuller has over 20 years of experience in engineering management, much of it as an overall head of engineering. Previous roles include CTO/VPE roles at Great Expectations, Pave, Optimizely, and WeWork. He is currently leading the core query compiler and serving teams at Sigma Computing, the industry leading business analytics company.Lawrence is passionate about the intersection of engineering management and the growth stage of startups. He has written extensively on engineering leadership (https://lbruhmuller.medium.com/), including how to best evolve and mature engineering organizations before, during and after these growth phases. He enjoys advising and mentoring other engineering leaders in his spare time.Lawrence holds a Bachelors and Masters in Mathematics and Engineering from Harvey Mudd College. He lives in Oakland, California, with his wife and their three daughters. This episode is brought to you by Span!Span is the AI-native developer intelligence platform bringing clarity to engineering organizations with a holistic, human-centered approach to developer productivity.If you want a complete picture of your engineering impact and health, drive high performance, and make smarter business decisions…Go to Span.app to learn more! SHOW NOTES:From research lab to record-breaking product: Navigating the fastest growth in history (4:03)Unpredictable scaling: Handling growth spurts of one million users every hour (5:20)Cross-stack collaboration: How Android, systems, and GPU engineers solve crises together (7:06)The magic of trade-offs: Aligning the team on outcomes like service uptime vs. broad availability (7:57)Why throwing models "over the wall" failed and how OpenAI structures virtual teams (11:17)Lessons from OpenAI’s first intern class: Why AI-native new grads are crushing expectations (13:41)Non-hierarchical culture: Using the "Member of Technical Staff" title to blur the lines of expertise (15:37)AI-native engineering: When massive code generation starts breaking traditional CI/CD systems (16:21)Asynchronous workflows: Using coding agents to reduce two-hour investigations to 15 minutes (17:35)The mindset shift: How rapid model improvements changed how leaders audit and trust code (19:00)Predicting success: "Vibes-based" decision making and iterative low-key research previews (20:43)Hiring for high variance: Why unconventional backgrounds lead to high-potential engineering hires (22:09) LINKS AND RESOURCESLink to the video for this sessionLink to all ELC Annual 2025 sessions This episode wouldn’t have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan’s also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, Brian Balfour (Founder & CEO @ Reforge) deconstructs the two core, interconnected challenges leaders face in the AI age: deciding what to build and evolving the Engineering, Product, Design workflow to deliver it. We cover why you should avoid “the local maxima trap” and siphon off "skunkworks" teams to take high-risk, AI-native bets. Brian provides the blueprint for the "Great Distribution Shift," detailing how to reshape your product from the ground up to avoid being left behind as platforms close, and how to emerge as a winner in the new AI landscape. Plus, learn how to rethink what to build, avoid commoditization, compress product discovery from weeks to hours, scale feature variations & prototypes, evolve products to solve harder classes of problems and shift specialist roles from "inboxes" to system builders. ABOUT BRIAN BALFOURBrian Balfour is the Founder & CEO of Reforge, which provides expert training and tools for AI-native product teams. Previously, he served as VP of Growth at HubSpot, spearheading launches like HubSpot CRM and building the growth team that propelled the company’s next chapter. This episode is brought to you by Span!Span is the AI-native developer intelligence platform bringing clarity to engineering organizations with a holistic, human-centered approach to developer productivity.If you want a complete picture of your engineering impact and health, drive high performance, and make smarter business decisions…Go to Span.app to learn more! SHOW NOTES:Brian’s reaction to the 5:1 gap between AI coding usage and actual product quality challenges (1:57)Why your system only goes as fast as the slowest part, and how hyper-optimizing engineering moves bottlenecks elsewhere (4:53)The "Local Maxima" trap: Why turning designers and PMs into mediocre developers is a waste of opportunity cost (6:04)Siphoning off "Skunkworks" Teams for AI-Native Innovation (7:53)Moving from exploring two solution paths to ten by simulating "product reps" through AI prototyping (13:24)Reforge’s AI-native suite (Build + Research): Scaling prototypes, feature variations and compressing product discovery & validation from weeks to hours (15:43)Case Study: How Captions evolved to solve harder classes of problems, using a creator-tool wedge to fund custom AI emotion-models for the media studio market (19:54)Case Study: How Shopify reframed support agents as multimodal "Business Advisors" to provide outsized value (22:24)Navigating the great distribution shift: Understanding the lifecycle from open platforms to closed ecosystems (25:10)The lifecycle of distribution shifts: Navigating the "Open Phase" growth to "Closed Phase" monetization w/ examples from Facebook, Google, and Apple (29:30)OpenAI, memory & context as moat, and why you need to reshape your product from the ground up to win in this distribution shift (31:16)Strategic de-risking for EPD leaders: Building proprietary moats through memory, context, and specialized workflows (32:51)Optimizing EPD workflows and structures: Separate high-risk "skunkworks" from core product optimization, lean cross-functional teams for faster iteration / decisions, and avoiding too many specialized roles (35:25)Dissolving the "Octagon of Specialists": Shifting researchers and PMMs from "inboxes" to builders of self-serve systems (36:57)The five types of product work and why there is no "one-size-fits-all" system for EPD (41:25)Rapid fire questions (43:25)LINKS AND RESOURCESAbout Reforge: Expert training & AI-powered tools for product teamsReforge Build: The prototyping tool discussed for exploring multiple feature variations without designer constraints.Reforge Research: The AI-interviewer tool used to compress user discovery and validation from weeks to hours.Reforge Insights: The platform that aggregates qualitative customer feedback into a self-serve system for EPD teams.Brian Balfour’s Research & FrameworksBrianBalfour.com: Brian’s personal blog featuring deep dives into growth and product strategy.The Next Great Distribution Shift: The foundational article explaining the lifecycle of open vs. closed platforms.The Four Fits Framework: A refresher on the system of Product-Market Fit, Product-Channel Fit, Channel-Model Fit, and Model-Market Fit.Reforge Strategic Deep DivesAI Disruption Risk Assessment: A guide for engineering leaders to determine if their product is at risk of being commoditized.Product-Market Fit (PMF) Collapse: How to identify and avoid the risk of your core product losing relevance in the AI era.MentionsInvest Like the Best podcastThis episode wouldn’t have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan’s also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Rajeev Rajan (CTO @ Atlassian) shares the leadership playbook he used to transform Atlassian’s engineering culture, and how that cultural foundation directly powered the build and launch of Rovo (Atlassian’s new AI powered app). We cover how they reduced ship time from 120 days to zero, why “developer joy” is the metric that matters, and how to create a community of developer productivity champions to scale DevEx transformation. Rajeev also breaks down his principles for systematizing autonomy and empowerment, including frameworks for giving direct reports more ownership. Plus, a look at the future of Atlassian’s “Systems of Work”! ABOUT RAJEEV RAJANRajeev Rajan is the Chief Technology Officer (CTO) at Atlassian. Rajeev joined the company in May 2022 and is responsible for Atlassian Engineering, IT, Security and Trust, and the Engineering Operations teams. His focus areas include the company's continued transformation to Cloud, Developer Platform, and Product lines. Additionally, he is passionate about continuing to develop Atlassian’s world-class engineering organization and making it a top choice for aspiring engineering talent worldwide.A long-time resident of Washington state, Rajeev previously acted as the Vice President and Head of Engineering for Facebook and Head of Office for Meta in the Pacific Northwest Region. Prior to Meta, Rajeev spent more than two decades with Microsoft, first joining as an intern in 1994. During his time there, he worked on many products, culminating in Office 365 where he built and led the team responsible for all of the Cloud Infrastructure for Office 365.Rajeev is married with two children and a spunky yellow lab named Rayna. He is very involved in and passionate about a number of efforts that uplift the local community, ranging from the arts to STEM programs. SHOW NOTES:The "Listening Tour": Grounding leadership in reality and identifying friction points (3:52)The Confluence Editor story: Reducing ship time from 120 days to 0 (6:26)Moving beyond productivity: Why "Developer Joy" is the metric that matters (8:45)Creating a community of Developer Productivity Champions and the power of a Productivity Summit (13:44)Elevating productivity to a company-level OKR and measuring qualitative sentiment (17:12)Leadership framework: Deciding when to "manage through people" vs. "manage through process" (19:05)How to give more direct ownership / responsibility to a DRI (23:03)Alignment conversations about prioritizing developer joy & productivity (24:22)Challenges faced during Atlassian’s developer joy transformation journey (26:23)How the "Developer Joy" foundation enabled building Rovo in just 6 months (30:02)The "System of Work": Expanding Jira's utility beyond engineering to finance, marketing, and legal (33:22)Rapid Fire Questions (40:48) This episode wouldn’t have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan’s also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/5 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, Daniel Lereya (Chief Product and Technology Officer @ Monday.com) shares how they are evolving their engineering roles from developers to builders & system designers, where the lines between product, engineering, and design are intentionally blurred, and developers manage AI Agents as team members, tackling an ever-expanding list of projects. We explore the shift from "developer" to "system designer" and why managing AI agents requires the same skills as managing people. Plus, a case study where the Monday.com team leveraged AI agents to decompose a monolith, autonomously manage the project board and assign strategic / high-risk tasks to humans. ABOUT DANIEL LEREYADaniel Lereya has served as Chief Product and Technology Officer at monday.com since 2023. In this role, he focuses on advancing monday.com’s multi-product vision and operational efficiencies while driving execution to support company growth. Previously, he was Vice President of R&D and Product, leading global teams in shaping and executing the company’s product strategy through innovation and technology. Before joining monday.com, Daniel held leadership and engineering roles at IBM and SAP. SHOW NOTES:The three core principles of monday.com’s culture: Ownership, Transparency, and Speed of Execution (3:59)How AI acts as an accelerant to implement these cultural principles at scale (8:36)Why the “Developer” role is evolving into a “Strategic Builder” and “System Designer” (13:47)Breaking silos: How the “Builder” role blurs the lines between product, engineering, and design (17:13)Real-world example: A designer using AI to submit code and fix UI issues independently (19:09)Case Study: The “Agent Factory” & how a weekend prototype by one leader shifted the product roadmap (21:25)Operationalizing transparency: Using internal tools (“Big Brain”) to align every builder on daily business impact (25:58)The “Kickoff Meeting” framework: A strict protocol for falling in love with the problem, not the solution (32:26)The new management paradigm with AI agents as team members (37:31)Rapid fire questions (42:09) This episode wouldn’t have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan’s also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This is a special episode, highlighting a session from ELC Annual 2025! The true promise of AI isn’t in replicating human intelligence. It’s in developing entirely new forms of non-human intelligence that perceive and understand the world in fundamentally different ways. Jamie Lien (Co-Founder and Chief Scientist @ Archetype AI) and Rashi Agarwal (Head of AI Engineering @ GoodLeap) explore the emergence of "Physical AI" - machines that sense the world through modalities beyond human biology to form internal representations free from our biases and then translate that understanding back to us in human terms. ABOUT JAIME LIENJaime Lien, Ph.D. is Co-Founder and Chief Scientist at Archetype AI, a pioneering startup advancing Physical AI, artificial intelligence that understands the real world through real-time sensor data fusion.With over a dacade of experience in radar-based sensing, signal processing, and hardware engineering, Jaime’s career bridges cutting-edge research and consumer-ready innovation. Before Archetype, she led radar sensing development for Google ATAP’s Project Soli and contributed wireless communication and localization expertise at NASA’s Jet Propulsion Laboratory. ABOUT RASHI AGRAWALRashi Agrawal is Head of AI Engineering at GoodLeap, where she leads enterprise-wide AI initiatives that deliver real business impact. An accomplished speaker, she covers the latest in AI, including context engineering, evaluations, and multi-agent collaboration, while driving Applied AI innovation in the enterprise. Previously, she scaled engineering teams at Yahoo, advancing its multibillion-dollar advertising business. A passionate world traveler to 40+ countries, Rashi brings global perspective and energy to her leadership and storytelling. SHOW NOTES:Archetype AI’s mission: Building a foundation model for physical reality (0:24)The potential for discovery: Using AI to observe phenomena humans cannot perceive (1:36)Augmentation vs. Replacement: Giving humans "superpowers" rather than automating them away (2:48)The "Perfect Storm" for Physical AI: Transformers, self-supervised learning, and commodity sensors (4:04)Defining “Non-Human Intelligence” and removing the constraints of human labels (6:34)Why language is inherently lossy and insufficient for true physical understanding (8:28)Real-world application: How Physical AI aids safety decision-making in the solar industry (9:35)Use case: Improving pedestrian safety and traffic signaling in Bellevue (12:51)The biggest engineering leadership challenge: Embracing the “messiness” of real-world data (14:21)Q&A: Why we shouldn't teach AI physical laws, but let it discover them (16:50)Q&A: Validating models when there is a defined ground truth vs. subjective language (18:49)Q&A: Compute requirements and the future of active learning at the edge (20:05) LINKS AND RESOURCESVideo version of Jaime and Rashi’s session at ELC Annual 2025 This episode wouldn’t have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan’s also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.