Tackling large user traffic with Ajay Sampat, Sr. Engineering Manager, Lyft

In our first episode of Breaking 404, a podcast bringing to you stories and unconventional wisdom from engineering leaders of top global organizations around the globe, we caught up with Ajay Sampat, Sr. Engineering Manager, Lyft to understand the challenges that engineering teams across domains face while tackling large user traffic. Through this episode, Ajay shares his personal experiences and hardships that developers/engineers face in their day-to-day tasks.

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Arbaz: Hello everyone and welcome to the first episode of Breaking 404 by HackerEarth, a podcast for all engineering enthusiasts, professionals and leaders to learn from top influencers in the engineering and technology industry. This is your host Arbaz and today I have with me Ajay Sampat, Sr. Engineering Manager at Lyft, a ridesharing company based in San Francisco, California.

Ajay: It’s great to be here and share my journey with the global HackerEarth community.

Arbaz: So let’s get started with a little bit about yourself? How has your professional journey been?

Ajay:

I moved from Mumbai, India to the United States when I was 18.
I graduated with bachelor's & master's degrees in computer science & engineering from Ohio State & Santa Clara University respectively where I had a deep interest in how computers interacted with each other at lightning speed across the globe over the internet.
I started my career working on block storage and supercomputers at HITACHI.
I learned a lot from the Japanese work culture about focus, dedication, and quality.

KIXEYE

I knew I wanted to work on a consumer-focused product and hence took a leap of faith in online and mobile games with KIXEYE.
I learned about growth culture and tactics from KIXEYE - building out a full stack team that focused on Growth Funnel of Acquisition, Activation, Retention, Revenue, and Referrals.

TEXTNOW

I took those learnings to the telecommunication vertical with TextNow building out the Business Intelligence and growth teams building products on user segmentation and insights, attribution, lifetime value prediction, experimentation, user engagement.

LYFT

Currently, I head the Marketing Automation team at Lyft focusing on the top part of the funnel for strategic investments across paid and owned channels to scale both drivers and riders in a two-way marketplace.

Throughout my professional journey, I have had moments of introspection and self-discovery. I have asked myself:

What do I really enjoy? Product Management or People Management?
Do I want to work for a small, midsize or large company?
What culture and values do I want the company to embody?
What skills do I want to develop?
What personal brand do I want to create?

Arbaz: One thing that all engineers would be inquisitive to know is, what is the biggest fear that you have, being the Sr. Engineering Manager at Lyft?

Ajay: This is not specific to Lyft but my biggest fear is not being able to create a highly functional team that delivers impact on the business. There are a lot of sub-dimensions to this but the key point I would like to highlight is the ability to hire and retain top talent in the competitive bay area market.

Arbaz: The burning question that everyone would love to know from someone working in the Lyft engineering team is: how does Lyft bring up a robust and scalable platform for managing high user traffic at certain times of the day?

Ajay: This is a culmination of years of hard work and learning from hundreds of engineers at Lyft encompassing Infrastructure, Developer productivity, and platform teams. I am fortunate to work with amazingly bright people who are passionate about their craft and the problems they are solving every day. Lyft shares a lot of in-depth articles regarding our technical challenges and our approach to solving those problems in our engineering blog - eng.lyft.com. I would also like to mention that Lyft is a major contributor to the open-source community. You can find our latest and greatest advancements in networking, security, data management at lyft.github.io.

Arbaz: That’s great to know. On the personal side, what is your favorite leisure-time activity that you love to do when not working?

Ajay: Spending quality time with my son - reading him stories, taking him to the park with our dog, working on puzzles and experiencing nature during our camping trip. “This is the greatest joy of my family's life.”

Arbaz: That’s really wonderful. Back to Ajay, the professional, one thing that all tech companies globally are looking for is to minimize technical debt. So, how do you maintain a balance of technical stability (minimize technical debt) while still delivering high-quality code?

Ajay: We like to use this question in our manager interviews. I think this depends a lot on the maturity and criticality of the feature. E.g: Tier 0 core rides API should not be held to the same quality standard of a tier 2 funnel conversion feature. In the early stages of a new feature, it is important to experiment a lot in beta, with small rollouts to gather customer feedback. This might lead to some interim shortcuts and tech debt but once it's decided that an experiment is going to be turned into a long-lasting feature it is important to scope it holistically with test coverage, edge cases, scaling, fallback plan and so on. When it comes to mid to long term planning - it is important to view all workstreams with the same lens - engineering effort vs business impact. This requires that one is accurately able to quantify the impact of working on tech debt or the addition of a new feature and help the business make the appropriate tradeoff.

Arbaz: With all the innovation and new technologies coming up, how do you see the technical landscape changing over the next few years and how will you prepare engineering for that?

Ajay: Jensen Huang, Nvidia CEO once said: “Software Is Eating the World, but AI Is Going to Eat Software”. It is getting increasingly clear that we are moving from a Mobile-first to an AI-first world. It’s all around us from the intelligent vacuum cleaners at home to the smart cars we drive.

Two main areas that intrigue me:

The first is AI plug-ins & IDEs like Kite and PyCharm which are making coding easier and more accessible. They are significantly reducing the barrier to entry to coding and now almost anyone with basic training can build web and mobile apps.
The second is AutoML which is democratizing Machine Learning and providing ML as a service. With advancements in ML libraries like sklearn, tensorflow, xgboost, and tools like DataRobot and H2O.ai, major resource-intensive activities like feature engineering, model selection, training, and tuning are being automated, leading to faster and higher accuracy models.

These technologies will continue to make great strides in the years to come.

Arbaz: Now, taking you a few years back and trying to get the fresh graduate developer out of you here. From a candidate’s point of view, what do you think is the most challenging part of any technical job assessment or interview?

Ajay: My belief is - that for most people it is Anxiety. Let's take a coding interview, for example. Obviously, you need some basic technical knowledge of data structures, algorithms, and problem-solving to do well in a coding interview which I feel most software engineers do. Where most people suffer is they let self-doubt or anxiety get the best of them. I feel if people stay calm and focused during a technical assessment, they will be able to hear the question properly, recollect their learnings, ask the interviewer the right questions and perform their best!

Arbaz: Very well said! Taking you further back in time, what was the first programming language you started to code in?

Ajay: I got my first computer which was a Pentium III in 1999, over 20 years ago. The first programming language I coded in was HTML which was self-taught so I could build a website and have my presence known on the Internet.

Arbaz: What would be your 1 tip for all Developers, Engineering Managers, VPs and Directors for being the best at what they do?

Ajay: Albert Einstein said, “Once you stop learning, you start dying”. The technology landscape is constantly evolving. This makes it very important for everyone to stay up to date with the latest trends that interest them so they can continue to sharpen their skills. That could be the latest front end coding language, cloud service or growth tactic. Luckily, this is much easier now with the plethora of knowledge consumption mediums like blogs, e-magazines, videos & podcasts.

Arbaz: Engineers and Hiring Managers are usually thought of as really serious people who are engrossed in their work and not very social. Although we see most developers plugged in with their headphones and listening to songs. What songs or music genre best describes your work ethic?

Ajay: It has to be deep house with its high momentum and tempos. And like real work and life it has buildups and drops.

Arbaz: Lastly, If not engineering, what alternate profession would you have seen yourself excel in?

Ajay: I can see myself being in stock or commodity trading which runs in the family. Our family business has been an integral part of my childhood and has had a lasting impression on me. It has taught me the value of honesty and hard work. Trading requires constant researching, building long term strategies and relationships which I enjoy a lot.

Arbaz: It was a pleasure having you as a part of today’s episode. It was really informative and insightful to hear from you.

Ajay: Thank you for having me Arbaz and HackerEarth.

Arbaz: This brings us to the end of today’s episode. Stay tuned for more such enlightening episodes. This is Arbaz, your host signing off until next time.

About Ajay Sampat:

Ajay Sampat is a seasoned growth engineering professional with expertise in scaling companies with state-of-the-art growth technology stacks. Ajay currently heads the Marketing Automation team at Lyft. Prior to Lyft, he started the SF office for Canadian startup TextNow and led its Business Intelligence & Growth teams, making it a top 30 Android app and top 100 iOS App, tripling their DAU and revenue. Before TextNow, he spent three years at KIXEYE building out the Growth engineering organization managing multiple successful desktop and mobile game launches. Ajay started his career at Hitachi working on block storage and supercomputers. Ajay has a BS in Computer Science from The Ohio State University and an MS in Computer Engineering from Santa Clara University.

Links:

Twitter: @asampat

LinkedIn: https://www.linkedin.com/in/ajaysampat/

Website: www.ajay.digital

Revolutionizing Mobile Talent Hiring: The HackerEarth Advantage

The demand for mobile applications is exploding, but finding and verifying developers with proven, real-world skills is more difficult than ever. Traditional assessment methods often fall short, failing to replicate the complexities of modern mobile development.

Introducing a New Era in Mobile Assessment

At HackerEarth, we're closing this critical gap with two groundbreaking features, seamlessly integrated into our Full Stack IDE:

Now, assess mobile developers in their true native environment. Our enhanced Full Stack questions now offer full support for both Java and Kotlin, the core languages powering the Android ecosystem. This allows you to evaluate candidates on authentic, real-world app development skills, moving beyond theoretical knowledge to practical application.

Say goodbye to setup drama and tool-switching. Candidates can now build, test, and debug Android and React Native applications directly within the browser-based IDE. This seamless, in-browser experience provides a true-to-life evaluation, saving valuable time for both candidates and your hiring team.

Assess the Skills That Truly Matter

With native Android support, your assessments can now delve into a candidate's ability to write clean, efficient, and functional code in the languages professional developers use daily. Kotlin's rapid adoption makes proficiency in it a key indicator of a forward-thinking candidate ready for modern mobile development.

Breakup of Mobile development skills ~95% of mobile app dev happens through Java and Kotlin — This chart illustrates the importance of assessing proficiency in both modern (Kotlin) and established (Java) codebases.

Streamlining Your Assessment Workflow

The integrated mobile emulator fundamentally transforms the assessment process. By eliminating the friction of fragmented toolchains and complex local setups, we enable a faster, more effective evaluation and a superior candidate experience.

Old Fragmented Way vs. The New, Integrated Way — Visualize the stark difference: Our streamlined workflow removes technical hurdles, allowing candidates to focus purely on demonstrating their coding and problem-solving abilities.

Quantifiable Impact on Hiring Success

A seamless and authentic assessment environment isn't just a convenience, it's a powerful catalyst for efficiency and better hiring outcomes. By removing technical barriers, candidates can focus entirely on demonstrating their skills, leading to faster submissions and higher-quality signals for your recruiters and hiring managers.

A Better Experience for Everyone

Our new features are meticulously designed to benefit the entire hiring ecosystem:

For Recruiters & Hiring Managers:

Accurately assess real-world development skills.
Gain deeper insights into candidate proficiency.
Hire with greater confidence and speed.
Reduce candidate drop-off from technical friction.

For Candidates:

Enjoy a seamless, efficient assessment experience.
No need to switch between different tools or manage complex setups.
Focus purely on showcasing skills, not environment configurations.
Work in a powerful, professional-grade IDE.

Unlock a New Era of Mobile Talent Assessment

Stop guessing and start hiring the best mobile developers with confidence. Explore how HackerEarth can transform your tech recruiting.

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

Develop an understanding of basics of system design and architecture
Familiarize yourself with commonly asked systems design interview questions
Read about system design case studies for popular applications
Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

Plan the extent of the topics and scope of discussion in advance
Clearly define the evaluation criteria and communicate expectations
Quantify constraints, inputs, boundaries and assumptions
Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

Ask follow-up questions to challenge a solution
Probe candidates to gauge real-time logical reasoning skills
Make it a conversation and take notes of important pointers and outcomes
Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

Encourage candidates to explore and consider alternative solutions
Work with the candidate to drill the problem into smaller tasks
Provide context and supporting details to help candidates stay on track
Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.