ElevenLabs Software Engineer, Full Stack Interview Experience
ElevenLabs · Software Engineer
I got the vibe they were really looking for ex-founders, because they kept pushing on why my side projects never hit production, and one interview was literally turning a Disney-style dubbing spreadsheet into a slick collaborative product.
Interview process
I got in through a referral, and the process was pretty clean: a casual recruiting chat, a 90-minute CoderPad, and then a final loop split across a few interviews. They also sent a PDF on what to expect, which I appreciated. The OA was just two LeetCode-medium-ish problems, but the final round was more product-heavy than I expected for a backend-leaning SWE role: a product-focused behavioral, a practical coding round based on a dubbing review workflow, and a product decomposition/system design round on basically that same workflow. I was more nervous than I needed to be, and overall it was not crazy hard, but the product decomposition round was the one thing that felt genuinely new. I made it to the final round and then got rejected.
Interview rounds · 5
- 1
Recruiter screen
BehavioralThis was a super standard get-to-know-you chat with the head of recruiting. It felt casual, no gotchas, and we didn't talk comp. The main thing I learned was how they see themselves: basically a research lab with a big product arm, so most of the hiring is around productizing the models.
Q1. Can you walk me through your background and what you're looking for?
How they answeredI gave a quick overview of my backend-heavy background, the AI tooling work I'd done, and why I was interested in a full-stack/backend-leaning role. It was very normal and conversational. Nothing felt like a trick question.
- 2
Online assessment
CodingData Structures & AlgorithmsTechnicalThis was a 90-minute CoderPad with 2 LeetCode-medium-ish questions. I finished it in 2/3 the time and got 100%, so it felt pretty straightforward if you've done a lot of standard coding prep. It was more about being clean and fast than handling some weird twist.
- 3
Other round
BehavioralProject DiscussionThis round felt more product-focused than a normal behavioral. Because I told them I was trying to move from backend/distributed systems into more full-stack product work, they kept digging into my side projects instead of my older backend work. The vibe I got was that they cared a lot about why I built things and whether I'd actually shipped something real.
Q1. What product experience do you have, why did you build these products, and what were the challenges?
How they answeredI walked them through my projects, why I built them, and the product decisions and challenges behind them. The pressure point was basically that they thought the projects were cool but wanted to know why none of them had actually hit production. That made me feel like they really value people who've shipped for real, almost ex-founder energy.
Q2. Why ElevenLabs?
How they answeredI said this was one of the few companies where I could immediately see myself using the product in my own life. There were a lot of cool things I'd want to build with their API, so my interest felt real. The people I talked to also seemed genuinely into audio and music, which made it feel mission-driven.
- 4
Technical round
CodingTechnicalData Structures & AlgorithmsThis was the hardest part for me. There was at least one standard data-structures-and-algorithms style technical round, but the memorable one was a practical coding problem that felt like a real ElevenLabs workflow instead of textbook LeetCode. It felt like they already had a solid internal answer in mind and wanted to see whether I could reason my way to something good.
Q1. Instead of tracking dubbing work in a giant spreadsheet, can you write functions that handle edits and propagate which lines or files need to be re-reviewed?
How they answeredI treated it like a practical Python problem around a dubbing workflow. Each row was basically a line, editors could approve it or ask for tweaks, and if a voice actor re-recorded something then that line had to get surfaced again for review and the status had to propagate upward. I used dictionaries and hash maps and wrote functions around those status updates. My read was that this came from a real customer workflow, not just an interview toy problem.
Follow-up questions- Assume you're managing editors and voice actors across many lines in a script.
- If someone changes a line, how should the review status roll up?
- 5
Final / onsite round
System DesignProduct DesignThis was basically a product-leaning system design round, which I hadn't really done before. They reused the same dubbing-review workflow but made me redesign the spreadsheet process into an actual interface. I started too document-centric and had to pivot toward a more media-first experience.
Q1. If the current dubbing workflow lives in Excel, how would you redesign it into a real product interface for editors and voice actors?
How they answeredMy first instinct was a Google-Docs-style design where you click a line, hear the audio, leave comments, and ask for a re-record inline. They pushed me toward something more like reviewing the dub in real life, basically a video player where you can watch, annotate, and comment in context. Once I made that shift, the design clicked. It was much more UI and product-focused than the backend system design prep I'd done.
Follow-up questions- Can you make this feel more like a video player than a document?
Tips from the candidate
If I were prepping again, I'd still grind standard LeetCode mediums because the OA maps pretty cleanly to that, but I would also prep for product-y system design even if you're backend-leaning. Practice taking some ugly spreadsheet or manual workflow and turning it into both backend logic and a real UI. Have crisp stories on why you built your side projects, what tradeoffs you made, and why they did or didn't ship, because they really seem to care about actual product instincts. If all you can talk about is backend infrastructure, I think you're missing part of what they're screening for.
Company culture
I came away thinking they're a small research group with a much bigger product organization around it, so they're hiring mostly for people who can turn model magic into usable product. The interview content felt close to real customer workflows, especially around dubbing and review, which made the process feel practical instead of academic. I also got a strong bias toward founder or entrepreneurial types who have actually shipped things into production. Everyone I talked to seemed personally into audio or music, and it felt like they want believers, not just people chasing a hot AI company.