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by The Engineering Leadership Community (ELC)
We share the most critical perspectives, habits & examples of great software engineering leaders to help evolve leadership in the tech industry.
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- 2 days ago38 min
Developing the modern builder framework and managing the cross-pollination of roles at scale w/ Pratima Arora #274
In this episode, Pratima Arora, Chief Product and Technology Officer @ Smartsheet, dives into what it means to be a modern builder as product, design, and engineering roles converge into today’s post-AI world. She and Jerry discuss why the modern builder model is disrupting traditional engineering processes / responsibilities as individuals embrace a breadth of skills while maintaining individual expertise. They cover how to encourage “cross-pollination” of roles, maintaining product quality while improving iteration speed, facilitating bottom-up contributions, creating context / knowledge graphs to inform agents, examples of good product builders, and what this means for hiring. ABOUT PRATIMA ARORAPratima Arora is the Chief Product and Technology Officer at Smartsheet. In this position, she is responsible for the company’s product management, product marketing, user experience, pricing and packaging, and digital and product partnerships.An accomplished technology executive, Pratima has more than 20 years of experience as a product and technology leader with a passion for building innovative tools that solve real problems.She has held senior product leadership roles at Atlassian and Salesforce, working on some of the companies’ highest-revenue products. Most recently, she was Chief Product and Technology Officer at Chainalysis, where she led the global R&D organization for one of the world’s top blockchain data platforms.She holds a B.S. in Physics, an M.S. in Computer Science, and an MBA from UC Berkeley’s Haas School of Business. The CAFE(S) framework is a research-backed definition of context quality for AI agents.DX collaborated with researchers at Capital One, Google, and UVic to give teams a shared standard for reviewing agent context by breaking it into five properties: clarity, actionability, fidelity, efficiency, and security.Your agent is only as good as its context and the CAFE(S) framework establishes a shared language around context to help you better understand how & why agents go wrong.Check it out here! SHOW NOTES:Introducing Pratima & her role @ Smartsheet Pratima’s insights on the CPTO role / the modern builder How the modern builder idea is disrupting traditional processes Maintaining product quality while improving building speed Understanding boundaries / designated roles as builder role expands Reward your teams & develop a mindset of cross-pollinating roles Benefits of a flexible modern builder role within teams The relationship between modern builders & skill development Strategies for facilitating bottom-up contributions across roles Defining the chief AI officer’s responsibilities / team roles What internal launches / adoption techniques look like @ Snapsheet Creating & maintaining the knowledge graph that informs agents The role of experts / specialities within the modern builder framework What the modern builder role means for the org chart Examples of modern builder archetypes Skills that make a good product builder Hiring insights when it comes to bringing in new modern builder talent This episode wouldn’t have been possible without the help of our incredible production team:Jerry Li - 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.
- 1 weeks ago42 min
Best practices for scaling AI adoption across a large org & the trade offs you will have to make w/ Sagnik Nandy #273
In this episode, Sagnik Nandy (CTO & EVP of Engineering @ Docusign) joins Jerry to discuss keys to success when it comes to peer-led AI transformation. Sagnik shares how they approached AI adoption internally @ Docusign with the use of both champions & multipliers, understanding trade offs associated with AI adoption, determining which tasks to assign to agents, frameworks for measuring the ROI of agentic tools, and how it helps bring value to your customers. He also discusses how to measure the customer productivity index (CPI) and what it takes to mitigate customer issues when scaling AI internally. ABOUT SAGNIK NANDYSagnik Nandy is the Chief Technology Officer and Executive Vice President of Engineering at Docusign responsible for engineering, cloud operations, and technology strategy that enables customers globally to run their organizations on Docusign.Most recently, Sagnik served as the President and Chief Development Officer for Workforce Identity at Okta where led engineering development and operations, product management, and design and user research. Earlier, he joined Okta as their CTO, overseeing the engineering and business technology arms of the company.Prior to Okta, Sagnik served as the Vice President of Engineering at Google where he oversaw several critical components of Google’s core advertising business, the backbone of the company’s growth and revenue. During his 15 years at Google, Sagnik also helped build Google’s Measurement and Analytics offerings and took several products from inception to becoming market leaders.Sagnik has a bachelor’s degree from the BITS, Pilani (Birla Institute of Technology and Science Pilani) and a Ph.D. in computer science and engineering from UC San Diego. The CAFE(S) framework is a research-backed definition of context quality for AI agents.DX collaborated with researchers at Capital One, Google, and UVic to give teams a shared standard for reviewing agent context by breaking it into five properties: clarity, actionability, fidelity, efficiency, and security.Your agent is only as good as its context and the CAFE(S) framework establishes a shared language around context to help you better understand how & why agents go wrong.Check it out here! SHOW NOTES:Introducing Sagnik & his role @ Docusign Strategies for approaching AI transformation internally How to create early goals & iterate quickly Using DocuWiki as a source of truth for agents How multipliers can address architecture-related decision making Understanding the role of multipliers & organizational accountability Trade offs eng leaders have to make when scaling AI adoption Illustrating the standard AI workflow / where human decision making steps in Frameworks for measuring the customer value of agents Steps for mitigating risks of customer-facing issues when scaling AI internally Tackling cost considerations when it comes to AI adoption Best practices for maintaining your AI usage / methodology across a large org How to AI effectively as a CTO Rapid fire questions 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.
- 2 weeks ago43 min
Building your support network, picking the right projects, and why eng leaders make good investors w/ Amit Gupta #272
Amit Gupta, Partner @ SNR Ventures, joins Jerry to discuss his unique eng leadership journey and how that led him to his second career as an investor / coach. We cover some of the lessons learned throughout his time working as VP of various startups and how those experiences shaped his desire to start his own consulting business. Amit defines what a coaching tree is and his philosophy when it comes to mentorship, picking the right projects, and determining what a team needs to work effectively together. Throughout the conversation, we revisit why effective engineering coaching also includes mentorship around company building. Lastly, Amit discusses why eng leaders make good investors and how investing was a natural continuation off of engineering for him. ABOUT AMIT GUPTAAmit Gupta has held leadership roles at multiple high-growth companies from early-stage through the public markets, including Qualcomm, INQ Mobile, Lookout, and Benchling. He combines concentrated, early stage investing with a passion for helping technical founders with company building. In some cases that materializes into a role helping in the trenches and in others a more passive, help as needed voice. Amit has helped countless companies directly and as an advisor to Y Combinator Continuity. The CAFE(S) framework is a research-backed definition of context quality for AI agents.DX collaborated with researchers at Capital One, Google, and UVic to give teams a shared standard for reviewing agent context by breaking it into five properties: clarity, actionability, fidelity, efficiency, and security.Your agent is only as good as its context and the CAFE(S) framework establishes a shared language around context to help you better understand how & why agents go wrong.Check it out here! SHOW NOTES:Amit’s transition from Qualcomm Talent acquisitions challenges faced when starting a company Lessons learned from Amit’s time @ Qualcomm Strategies for building a support networks as an eng leader An example of effective mentorship in Amit’s career Defining work-life integration & how that influences Amit as a coach Transitioning from a second startup / starting his own consulting business Why the work at Benchling wasn’t as hard as the first couple startups Amit’s favorite behavioral interview question Shared traits of great teams who perform well together Frameworks for determining good leaders vs. good managers Ways to feed your high-aptitude leaders An example of a rewarding coaching scenario Amit’s perspective on investing as an extension of building Strategies for determining which bets will be successful Considerations for the eng leader to investor pipeline Rapid fire questions 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.
- 3 weeks ago38 min
Building software factories and making the transition from a pre-AI to post-AI world as a large company w/ Zohar Einy #271
Zohar Einy, CEO & Co-Founder @ Port, shares best practices for building a software factory, how software factories impact the organizational structure of your org, and scaling strategies. He and Jerry discuss why software factories can contribute to a significant ROI, tools for helping your teams navigate the transition from building a product for agents vs. people, and what scary (but healthy) decision-making looks like as an eng leader in a post-AI world. Zohar also shares some of his core beliefs when it comes to developing talent & hiring young engineers @ Port. ABOUT ZOHAR EINYZohar Einy is the co-founder and CEO of Port, the agentic SDLC platform helping companies like GitHub, Visa, and PwC build and scale their AI Software Factories. Port has raised $158M, backed by General Atlantic, Accel, Bessemer Venture Partners, Team8, and TLV Partners.The idea for Port began during Zohar and co-founder Yonatan Boguslavski’s time as engineers in the IDF’s Unit 8200, where they built a platform to help developers move more quickly at scale. After recognizing the same challenge across the broader market, they founded Port in 2022. The CAFE(S) framework is a research-backed definition of context quality for AI agents.DX collaborated with researchers at Capital One, Google, and UVic to give teams a shared standard for reviewing agent context by breaking it into five properties: clarity, actionability, fidelity, efficiency, and security.Your agent is only as good as its context and the CAFE(S) framework establishes a shared language around context to help you better understand how & why agents go wrong.Check it out here! SHOW NOTES:Introducing Zohar & his background / main focus Defining what a software factory is & what that means for the industry Parallels between a physical factory vs. a software factory How the software factory model impacts organizational structure What certification means in the context of software factories Best practices for building a new software factory Don’t make the ability to scale an afterthought Navigating the balance between control & automation when building from scratch How to know when a team / function merits its own software factory Transitioning from a pre-AI to post-AI world as a large company Inside Port’s rebuild using AI & navigating the team through the transition The team’s reaction to building for agents vs. humans Approaching decision-making with a healthy dose of trepidation Characteristics that make for good hires today Examples of product-related decisions that felt scary in the moment Lessons learned while Port transitioned to building for agents How to measure the ROI of software factories Insights on how to best allocate resources across a mature company Hiring hungry, young talent vs. hiring established talent Rapid fire questions 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.
- Sep 29, 202643 min
Utilizing AI to assist in EPD and non-EPD functions & how curiosity can drive organizational change w/ Ali Dasdan #270
Ali Dasdan, CTO @ Dropbox, joins the pod to share insights on the company’s large-scale AI adoption and research as a foundation of successful engineering leadership. First, Ali shares insights on why he continues publishing research despite his role as a CTO and how research / curiosity can drive better trust within your organization. He shares about how his research ultimately guided decision making regarding redrawing Dropbox’s entire architecture and the importance of creating a record of the company’s progression as the rearchitecture occurred. The bulk of the conversation centers around how Dropbox is adopting AI, including tools like Nova and Dash, within both EPD and non-EPD departments. ABOUT ALI DASDANAli Dasdan is a C-suite technology executive with 25+ years building, scaling, and leading global engineering, product, and technical-operations organizations of up to ~1,000 people across a dozen industries, in public companies, high-growth startups, and enterprises in Silicon Valley and London.Today he is CTO of Dropbox, serving over 700 millions of users on an exabyte-scale platform with $2.5B+ revenue, where he also leads the AI strategy and AI enablement. Previously EVP and CTO of ZoomInfo and VP of Engineering at Atlassian (Confluence Cloud, Trello, Jira Work Management). As CTO (at Turn, Vida, Poynt, ZoomInfo, and Dropbox) he defines and owns company-wide technology strategy; in divisional roles (eBay, Atlassian, Tesco, Yahoo) he led engineering for revenue-critical parts of the business. SHOW NOTES:Why Ali continues to publish research as a CTO How research results can alter decision making as an eng leader The connection between research & organizational trust Redrawing the org’s architecture to learn more about it What kinds of info help eng leaders better understand the business / technology Building a record of the org’s progress & development Where Dropbox is at in their AI adoption journey AI @ Dropbox in EPD vs. non-EPD functions Frameworks for implementing AI at the highest level The engagement / ownership model @ Dropbox Insights on Nova, Dropbox’s code review AI workflow Incorporating human-driven feedback into the AI loop What the immediate future looks like for automated developer tools How AI has directly impacted productivity @ Dropbox Addressing bottlenecks related to AI adoption Using AI infrastructure to assist in non-EPD functions Dash as a contextual AI platform Ali’s experience with specialized models vs. foundation models Rapid fire questions 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.
- Sep 23, 202646 min
Implementing scalable and powerful agentic tools across your organization w/ Ozzie Osman #269
In this episode, Jerry discusses key insights on delegating to agentic tools while maintaining high levels of engineering ownership with Ozzie Osman, co-founder @ Monarch. Ozzie shares what it looks to pursue two paths when it comes to AI: agentic-forward and human-orchestrated pipelines. They also cover the specific AI tools that are used in Monarch, including Devin and Voltron; assigning tasks to be AI-first vs. human-led; determining which pieces of customer feedback lead to new features; and common challenges when it comes to AI-generated code and what the human review process for it looks like. ABOUT OZZIE OSMANOsman (Ozzie) Osman is co-founder of Monarch. He is the lead author of the Holloway Guide to Technical Recruiting and Hiring. He has built products and engineering teams at companies including Quora and Google. Ozzie has also started two companies that have been acquired, and advised dozens of other startups. Sinch is the communications infrastructure the AI era runs on.There's a layer of infrastructure behind every text, call, and login code your product sends, and it works exactly like plumbing: nobody thinks about it until it's the reason something broke.Most providers route through 4-6 intermediaries; Sinch connects in 1-2 hops, direct carrier relationships across 600+ connections, handling 900 billion interactions a year across 60+ countries.Routing, compliance, fraud prevention handled automatically rather than manually managed by the business sending the message!Sinch is the reliability layer underneath AI-driven customer communications; the infrastructure that determines whether an AI agent's output actually reaches a real person as a delivered text, connected call, or verified interaction.Check it out here! SHOW NOTES:Introducing Ozzie & his role @ Monarch Where Monarch is at in terms of technology transformation / user adoption Insights on how Monarch is embracing AI with a security-forward mindset What it looks like to pursue both agent- and human-orchestrated AI pipelines Dissecting how an MCP shared infrastructure improves productivity Inside the enablement team @ Monarch Why Monarch uses Devin / prioritizing autonomy How to fine-tune an agent to make it reusable Strategies for determining what is an AI-centric vs. human-centric task Understanding when & why Devin fails Devin’s role in assisting the human-orchestrated processes What a typical flow looks like throughout an SDLC The importance of ownership in engineering when reviewing AI-generated code Frameworks for prioritizing feature requests based on user feedback Ozzie’s perspective on the role of engineering teams Final thoughts on navigating AI-related anxieties 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.
- Sep 17, 202650 min
Navigating from individual AI adoption to team-level transformation while scaling w/ Jakub Oleksy #268
Jakub Oleksy, SVP of Software Engineering @ Github, joins the podcast to discuss how Github is addressing some of the biggest challenges facing the industry when it comes to infrastructure scaling, customer capacity, and using AI to add value to your org’s processes and eng leaders’ decision-making. He shares how scaling looked different at Github six years vs. today, how they navigated the migration to Azure, and what AI transformation looks like individually & at the team level. Jakub also discusses insights for eng leaders when it comes to investing in yourself & your people and making cross-functional decisions. Sinch is the communications infrastructure the AI era runs on.There's a layer of infrastructure behind every text, call, and login code your product sends, and it works exactly like plumbing: nobody thinks about it until it's the reason something broke.Most providers route through 4-6 intermediaries; Sinch connects in 1-2 hops, direct carrier relationships across 600+ connections, handling 900 billion interactions a year across 60+ countries.Routing, compliance, fraud prevention handled automatically rather than manually managed by the business sending the message!Sinch is the reliability layer underneath AI-driven customer communications; the infrastructure that determines whether an AI agent's output actually reaches a real person as a delivered text, connected call, or verified interaction.Check it out here! SHOW NOTES:The current focus @ Github & what scaling looks like Signals that previous scaling methods are no longer viable Migrating to Azure / impact on the engineering org Strategies for orchestrating massive, widespread change across the org How to control the scope while scaling / rebuilding Ownership dependencies & potential rollback with Github’s service migration process Embrace excitement when it comes to prioritization, scaling, & problem solving Addressing capacity & configuration challenges earlier on Github’s “new norm” for processes / organization How to address legitimate vs. illegitimate traffic Keeping up with rapidly changing technology / AI advances Github’s method for tackling the context layer of its AI toolage Where Github is on the AI adoption curve Navigating the transition from individual AI adoption to team-level transformation An example of team-level AI adoption @ Github Jakub’s advice to eng leaders on building infrastructure teams / investing in people How platform leaders can improve their cross-functional change making ability Reducing complexity for platform teams to improve ability to scale An example of a successful transformative moment @ Github Rapid fire questions 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.
- Sep 2, 202635 min
Hiring top-tier talent, leveraging open source models, and staying competitive in the age of AI w/ Benny Chen #267
Benny Chen, Co-Founder @ Fireworks AI, joins the show to discuss his founder journey and share valuable insights on navigating common founder / product dev challenges in today’s agent-first landscape. He and Jerry cover strategies for creating effective messaging, staying competitive in a crowded market space, hiring top-tier talent / what qualities to look for in high-performing engineers, navigating the cultural shift to managing agents, creating data flywheels & how this can help your customers, and more. ABOUT BENNY CHEN As co-founder and early product architect, Benny Chen shaped Fireworks AI’s infrastructure strategy, spearheading the design of scalable systems to support high-throughput AI model serving. Benny’s contributions established the technical foundation for Fireworks AI’s robust and cloud-native architecture, which underpins its ability to meet enterprise demands. Formerly Meta’s Ads Infrastructure Lead, Benny optimized large-scale ad-serving pipelines and developed significant expertise in distributed systems and cloud infrastructure. He holds a B.S. in Computer Science from Stanford University, bringing both leadership and technical depth to the Fireworks AI management team. Sinch is the communications infrastructure the AI era runs on. There's a layer of infrastructure behind every text, call, and login code your product sends, and it works exactly like plumbing: nobody thinks about it until it's the reason something broke. Most providers route through 4-6 intermediaries; Sinch connects in 1-2 hops, direct carrier relationships across 600+ connections, handling 900 billion interactions a year across 60+ countries. Routing, compliance, fraud prevention handled automatically rather than manually managed by the business sending the message! Sinch is the reliability layer underneath AI-driven customer communications; the infrastructure that determines whether an AI agent's output actually reaches a real person as a delivered text, connected call, or verified interaction. Check it out here! SHOW NOTES: Moving from an early idea to a rocket ship Insights on developing / communicating your core message as an early founder Role of open source & inference @ Fireworks AI How Firework AI’s company messaging evolved over time Popular customization features today Strategies for staying competitive in a crowded market Defining the customer data flywheel & how it helps users Common types of data that companies can collect to train their AI models The customer’s next steps after creating a data flywheel Benny’s perspective on acquiring engineering talent as a founder Common traits shared by high-performing engineers @ Fireworks How AI has altered which traits founders prioritize when hiring Navigating the shift from engineering work to managing agents Frameworks to ensure agents are doing the right thing Aligning your metrics with the outcome you’re trying to follow What a typical day looks like for Benny as a founder Advice for founders / eng leaders looking to embrace AI adoption Rapid fire questions This episode wouldn’t have been possible without the help of our incredible production team: Patrick Gallagher - Producer & Co-Host Jerry Li - Co-Host Noah 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.
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