Nithin Raj Sudevan first joined Audible as an intern in 2015, and is now a Senior Software Development Engineer who works with colleagues around the world to build and leverage machine-learning capabilities that allow our customers to easily find super-personalized content recommendations. Find out why Nithin has loved working as an engineer at Audible for over a decade.

Can you describe your role at Audible?
Every time you open the Audible app, something shows up on your home page, based on your preferences. My team helps determine which carousels appear, what titles are in them, and in what order. I work across the teams that handle everything from the machine learning (ML) models that learn customer preferences, to the services that deliver those recommendations to their screens in real time. Before this, I spent six years building a good chunk of the ML infrastructure that powers a lot of it. If the app ever feels like it just gets you, that's us!

What is life like at the hub you’re aligned to?
I'm at the Newark hub, in the Innovation Cathedral. The office is fully modern, but there are these historic architectural elements woven throughout the space. You'll be walking to a meeting and notice some detail you've never seen before. The best part is being close to the teams and stakeholders I work with every day, which makes a real difference when you're collaborating across multiple teams.

How does the work you do affect listeners or your colleagues across different markets worldwide?
I collaborate with colleagues in Boston and Berlin, and with stakeholders in the UK, Germany and Australia, to adapt our models and recommendation systems for each marketplace we serve, because how listeners discover and engage with content differs from region to region. Navigating those differences is a constant part of the job.

What was your first Audible listen? What inspired you to check it out?  
Unbroken by Laura Hillenbrand. It came up as a recommendation in the app, and it was not a genre I was into at the time, but I decided to explore it and the story hooked me. It ended up sparking an interest in World War II history and eventually led me to The Making of the Atomic Bomb and American Prometheus.

Can you share a bit about your career journey at Audible?
I started as an intern in 2015, and one of my first contributions was deploying our first ML model, developed by the newly established data science team. That was the beginning of a six-year stretch on the ML Engineering team. Then I moved to the team behind content discovery, dealing with personalization and content and layout optimization. I was fortunate to be part of the initial stage of building our machine learning system, tackling complex problems at scale, and that few engineers get to touch so early in their careers. Experiencing both the back-end world of ML infrastructure and the real-time, customer-facing world of content discovery has given me a perspective I don't think I could have gained any other way. 

Can you share about a time you had a leader at Audible empower you to take on a challenge or explore a new area?
After about six years on the ML Engineering team, my skip-level manager approached me about moving to a different domain, to see if I could successfully transition to something completely different and broaden my experience.

I'd been on the ML team since the beginning and touched every part of the system, so I felt like I could solve anything. Moving to a different team flipped that completely — my impostor syndrome was dialed up to 11. But I wanted to know if I could succeed, and it helped that I was given the option to go back to my old team if it didn't work out.

The two domains couldn't have been more different. In ML, latency was measured in hours, and scale meant processing hundreds of terabytes of data or retraining hundreds of models daily. On the content discovery team, every millisecond of latency mattered, availability was everything, and scale meant tens of thousands of requests per second. Shaving off just 20 milliseconds made a noticeable difference. It rewired how I thought about efficiency, reliability and observability, and it made me a far more well-rounded engineer.

Can you share about a time when you felt that someone at work practiced our People Principle of “Activate Caring” towards you?
I was leading a high-visibility, time-sensitive project with a lot of stakeholders. At the same time, I was going through a personal issue and finding it really difficult to balance everything. It got to a point where I needed to step back from the project.

I knew I was putting my manager in a tough spot. We had spent months understanding the problem and working through the ambiguities, and we were just about to start the design and execution phase. I was worried about what the reaction to the news would be.

To my surprise, my manager immediately prioritized my well-being. At no point was I made to feel guilty. The message was: take your time, get everything settled, and when you're ready, we'll find something to work on. At the same time, a colleague stepped up without hesitation and took on my responsibilities.

That lifted a huge burden off me. It gave me the space to focus on my personal life without any pressure, and I was able to return with a clear head and renewed energy. That experience was a reminder of what Activate Caring looks like in practice: real, tangible support when it matters most. 

Other than the people, what do you love most about the culture here?
The longevity. At one point, the average tenure on my team was eight years. That's almost unheard of in tech. That says more about the culture than anything else I could describe. People don't stick around that long unless the work keeps growing with them.

What are the benefits you find invaluable for your personal well-being, or that help make both your work life and personal life more fulfilling?
The parental leave was huge. Even before my leave started, my manager had already dialed back my responsibilities so I could focus on preparing. When I came back, my manager was great about adjusting my schedule as I transitioned in, and my teammates were extremely supportive.

I also value the wellness days. For someone like me, who doesn't prioritize planning time off, it's a nice forcing function to step away and actually recharge.

What's one quality people interested in working on AI and content discovery need to be successful here?
You have to be the kind of person who can take an ambiguous problem and own it end-to-end. A lot of the problems we solve are novel, with no playbook to follow. You have to be comfortable not having all the answers upfront, curious enough to explore different approaches, and inventive enough to build something new where nothing exists.

What made you choose to work at Audible, and what makes you want to stay?
I was drawn to Audible by its reputation, but the reason I've stayed for over a decade comes down to the problems, the people and the trust. From my early days as an intern, I have been given impactful problems to solve and trusted to figure them out. As I grew from intern to Senior SDE, the scope increased but the trust and freedom stayed the same. I've always had supportive managers who put the right opportunities in front of me and let me make the decisions. And I get to work alongside genuinely smart and caring people every day.

What’s really cemented it for me is that, on multiple occasions, Audible went above and beyond to support me. That's why I've built my career here.

Lightning Round!

Favorite genre? Sci-fi and science history.

Listening from a speaker or headphones? Headphones.

Favorite activity while listening? Gardening in spring/summer and cooking in winter.

Morning person or night owl? Night owl, through and through.

Favorite snack? Mixed nuts.

What’s your favorite listen?

The Dungeon Crawler Carl series. I picked it up as a break from the more serious stuff I usually listen to, and it's hilarious. The range of voices is incredible, and I was surprised to learn it was all done by one narrator. It's one of the best performances I've come across.

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