Ani Mishra Podcast Transcript
Ani Mishra joins host Brian Thomas on The Digital Executive Podcast.
Brian Thomas: Welcome to Coruzant Technologies, home of the Digital Executive podcast.
Welcome to the Digital Executive. Today’s guest is Ani Mishra. Ani Mishra is a Seattle based software engineering leader with a proven track record of building consumer facing products that achieve widespread adoption. As the head of Door Dash’s, new verticals logistic engineering organization, Ani is responsible for the 24 by seven operational excellence of a large scale system spanning grocery, convenience, retail, and alcohol.
His team’s critical work ensures the seamless and optimal matching of orders, dashers, and stores directly influencing key surfaces in door Dash’s, consumer and Dasher apps.
Well, good afternoon, Ani. Welcome to the show.
Ani Mishra: Hey, Brian. Thank you so much for having me on the show.
Brian Thomas: Absolutely my friend. I appreciate it and making the time know you’re in Seattle, Washington. Today I’m in Kansas City, so there is a two hour difference. I appreciate you making the time. Ani. I’m gonna jump right into your first question.
You’ve led engineering from some of door Dash’s most complex logistics systems. How do you approach designing a platform that operates seamlessly across grocery, convenience, retail and alcohol, each with its own set of challenges?
Ani Mishra: That’s a great question, Brian. And building a system that solves customer problems for multiple verticals and is no easy task.
Like my philosophy for building systems that can solve many customer problems in a scalable way is start with one of the categories of customers. So define the customer problem really well. Understand the problem well. And build a solution that solves the problem for one category of customers and figure out how to build a product that works for the customers.
Get it out as soon as possible. And once you have figured out what needs to be built to solve the problem for one set of customers, that is when you start to like think about how do I scale this solution? So one thing that I keep in mind is even if I’m building a solution for one set of customers, like what would I need to build for it to work for other type of customers or other kind of problems?
So. Keep that in mind while building the system or the platform initially, and optimize for speed initially. Once you’ve figured out what you’re building, what your customers want, that is the time to think more about scaling it and building a more generic platform. How I like to do this is figure out the pilot use cases for the product you’re building.
And get it work for them for one set of customers and then continuously iterate and keep generalizing the solution. And until, you know, you reach a point where your solution works for multiple set of customers and multiple verticals. So the solution kind of evolves from solving one specific problem for one set of customers to solving many problems for a lot of customers.
So how I approach this is, you know, start simple, start small, go to market fast, learn from there, iterate. Then think about building the platform and, you know, generalize your solution to work for a larger set of customers.
Brian Thomas: Amazing. Thank you for breaking that down. Obviously there’s a lot that goes into what you do in your job.
I liked how you tackle solving multiple problems across all these verticals as we discussed, but focusing on one set of customers. Define the customer problem. How do you build that product for that one customer? Optimize for speed and then scale again, you can do this. It’s kind of like a rinse in repeat, and I appreciate your experience and how you broke that down so easily for us here in our audience.
Ani, the next question I have for you, as a leader in such a fast-paced engineering environment, how do you build and maintain high performing teams that can both innovate quickly and operate reliably at scale?
Ani Mishra: Brian, so yes, that is a great question as well. So having a team that can actually build systems like this is critical and there’s a lot that goes into building a team that is excited about solving challenging problems at scale.
Not just solving customer problems, but also solving large scale systems problems. My approach to this is, you know, first we need the right people with the right skillset in the room. So how do we hire top talent? How do we attract, you know, world class engineers to work with us on solving these problems?
You know, what I look for is engineers who actually are passionate about solving customer problems. Obviously, computer science background and large scale systems background is also necessary. But people who are really excited about solving customer problems, like applying the skills in solving these hard customer problems is really critical.
Let’s say we figured out how to hire top talent. Understanding motivation of the people in the team is also very important. Some people are just excited by solving. Are technical problems, but there are other people who are excited by leading other people. Some people like to manage projects. Some people like to, you know, get to the next level in their career.
So I think understanding motivations for the people in my team is, is also very critical to make sure that folks stay motivated. And also, like for me as a leader, it’s very important to have. Enough charter or enough scope so that I can set everybody in my team in their growth path. So it’s very important for me to continuously keep looking one year, two year out and think about the problems that I want to solve in the future and start to like set the stage for solving these problems, get the prototypes out, create enough traction within my org to be able to start solving those problems.
Finally, like one of the most important things I think for like high performing teams and you know, working with high performing engineers is giving people focus. It’s very important for, you know, me to set very clear focus and very clear problem space for the people in my team and making sure they have autonomy to make decisions of their own.
It’s really critical for people to do their best work. Obviously, another thing that is very critical is how do you build systems which are really reliable, which requires a very disciplined approach to building systems. For me, like I said, that as a goal for everybody in my team, that they are actually responsible for the reliability of the system as well.
Whether the system needs four nines, five nines, it is for them to decide based on the criticality of the flow and just building the product is not enough. The product should also be reliable for the customer. So that is kind of like how I think about, you know, building. Uh, high performing teams in fast based cultures operating at like really large scale.
Brian Thomas: Thank you. That’s awesome. Broke a lot down there for being a successful team, obviously, you know, having that good team to solve these complex problems and innovate is so critical these days. If I could just highlight a couple of things that, uh, you mentioned, obviously hiring that top talent. Find out what motivates them.
You know, are they passionate about solving customer problems, defining a clear path and support for your team. Foster an environment and culture with clear goals and give them that autonomy. Lastly, I think it’s important is reliability. It’s key, but everybody shares that responsibility, and I like how you share that message as a team.
Ani, with your early experience at startups like Phoenix, P two P and Mobify, how did these roles shape your approach to innovation and leadership at a much larger company like DoorDash?
Ani Mishra: My background working for really small startups, so I was founding engineer for one of these startups and I was really like first time employees for the other startup, and I think I really learned important lessons working for really small companies early in my career.
That really helped me grow my career in future and like becoming, you know, a leader at DoorDash. And I think what really helped me was that I really embraced my time at these startups and really embraced what it takes to like, you know, write the first line of code for building a product where we don’t know where we are going, and then going from there to a stage where the product is mature and there is people that depend on that product for their everyday life.
So. You know, learning the transition from building an early product to going to a mature product and what it takes to do that. I think that was one of the most important lessons that I learned working for really a small early startups, particularly at Phoenix, which is a Chicago based company that works on real time video streaming at large scale.
I learned how to build our scale systems that scale. And you know, I, I really remember like one time I thought, this system is so complex. How do I understand it well enough to be able to, you know, build a system on top of it and extend the capabilities And, you know, I signed up to be on call. For a month so that I can understand the internals of the system.
And I still remember those were some of the most formative days for me in understanding, you know, how to build large scale systems. And you know, if I reflect at my time at Mobify, I think I really learned how to lead teams and how to build inclusive cultures there. And even today I applied some of those learnings.
And enhancing my team and building a very inclusive culture here at DoorDash. So I definitely think those are some of the most formative days in my career. And I, I recommend everybody to, you know, try a stint at an early startup at some point in your career because those are, you know, the really important and, you know, the most really critical for me to like learn and, you know, execute in future.
Brian Thomas: Absolutely. I appreciate that, and I hear a lot of that from founders and people that were like yourself. Founding engineer as an example, right? Startups, you wear so many hats when you’re in a small company, you’re asked to do a lot more, but it gives you that appreciation of what goes into a startup. You know, wearing many hats.
You’ve learned how to build a product early, right? And then learned how to evolve into that mature product, and you really learned how to scale products, which I think is pretty cool. So thank you for sharing, Ani. Last question of the day. What excites you most about the future of logistics technology, whether it’s automation, ai, or emerging delivery modes?
Where and where do you see the biggest opportunity?
Ani Mishra: This is a very timely question with the, you know, with the emergence of large language models in the last few years, now a lot more is possible than what it was five years ago. And I think while the use of AI is not novel in logistics, like we have seen research and work done on autonomous vehicles in drones, in, you know, food making robots, automation for many years, and you know, AI has been playing a important role in advancement in those areas.
But the emergence of, uh, large language models have made a lot more possible now because now everybody has access to word knowledge. So it has really enhanced the capabilities of all the actors in logistics, and particularly like in last mile delivery and on demand delivery space. I think large language models are already helping associates in the store that do the shopping on their behalf.
When you place an order, it is helping them find items in their store very easily, and it is empowering them to be able to easily discover some of the most obscure items that they might not know how to find in a. Not only it reduces the defects because people are able to, associates in the store are able to find more items.
It’s also making them more efficient. Now they can find the fastest route to shop all the items that the customer want. So it is definitely helping reduce defects as well as making on demand delivery even faster. In addition to that, I’ve also noticed in industry that it has dramatically improved the customer experience.
Now, a large model can understand the intent of the customer when they’re placing an order. Let’s say on Thanksgiving, your, your ingredients seem like you’re gonna prepare Thanksgiving dinner for a family. AI and large models can understand that, hey, this person wants to prepare a dinner for Thanksgiving for their family.
Seems like they’re missing, missing one of the key ingredients, and they can nudge the customer. So it’s also. An assistant for people who are placing the orders on delivery apps and making it easier for them to convey their intent more clearly. And finally, I think LLMs are also playing an important role in safety for workers who are transporting goods for last mile delivery.
And one important use case I like to think about is that a large young model can easily understand free text information. About weather, about traffic, and it can automatically adjust, it can automatically shut down a market and get it back on really fast based on the real time conditions, uh, does ensuring that everybody who’s doing the transportation of goods and drivers s are are really safe.
So there’s really a lot of applications of LMS in logistics, and we are just starting to see it all happen. I think in future we’ll discover a lot more use cases and a lot more applications, not only making these deliveries faster, but [00:12:00] with lesser defects and safer for drivers. I would like to also add that while LLMs are new, the AI applications in logistics are not novel.
That has been happening for a while. We’ll continue to see autonomous vehicles becoming more and more mainstream. Already today in San Francisco and Phoenix, you can call a ride on autonomous vehicle and in Los Angeles you can get a delivery from autonomous vehicles. So that will continue to happen.
It’ll accelerate with the help of LLMs. Another classic problem that AI solves logistics is predicting demand. And that helps logistics companies to, you know, to make sure they have capacity to fulfill that demand. And even in that area we’ll see more and more innovation with LLMs and, you know, acceleration of all of these use cases and their solutions in future.
You know, really exciting time to be in logistics, applying AI and LLMs to solve problems, and I think we’ll see a lot of exciting advancements in this field in the next few years.
Brian Thomas: That’s amazing. Love hearing this type of stuff. It gets me really excited. We talk a lot about emerging tech here on the podcast, but you know, just a few things that I like to highlight.
You covered with the advancement of L LMS today. There’s so much more that’s possible. As you mentioned, we’ve got these autonomous vehicles, you know, robotic process automation, drones, and other types of robotics. This technology has really enhanced the logistic of products from order to delivery. As you mentioned, with improved efficiency, customer experience, you can go as far as reading, customer sentiment, customer intent, which is really awesome.
And the last thing you, you talked about is that free text information. You know, it will drastically improve logistic process while it’s in process, right? It’s real time decision making, which I think is phenomenal. So I appreciate that, Ani, it was such a pleasure having you on today, and I look forward to speaking with you real soon.
Ani Mishra: Likewise. Thank you, Brian.
Brian Thomas: Bye for now.
Ani Mishra Podcast Transcript. Listen to the audio on the guest’s Podcast Page.
