Sierra AI Agent Engineer Interview Experience
Sierra AI · Machine Learning Engineer
I told Sierra I’d only worked in Python, they said that was fine, and then my first onsite was a TypeScript React debugging round where the interviewer showed up 10 or 15 minutes late and was super rude and cold.
Interview process
I got in through a referral, and the process was a recruiter screen, one LeetCode-style technical screen, then an onsite that included a take-home plus three 1-hour rounds. For a startup, it felt weirdly mature and standardized, more like big tech: everyone I met was in the agent engineering org, the recruiter was polished, and they were even fine with me pushing the coding screen out by over a month. The phone screen was a pretty standard cycle-detection problem dressed up as an Excel question, and then the take-home was much more job-relevant because I had to build a small customer support agent and think about prioritization, metrics, and observability. Most of the loop felt friendly and professional, but the big gotcha was a TypeScript and React debugging round that they framed as conceptual even though I think you really do need to know that stack to pass it. I didn't get the offer, and honestly I think that debugging round was the reason because the rest of the process felt solid.
Interview rounds · 6
- 1
Recruiter screen
BehavioralTechnicalThis was a very light 60 min recruiter chat where he mostly sold Sierra, lightly covered my background, and was unusually flexible about letting me push the coding screen out by more than a month.
Q1. Can you walk me through your background and what languages you're comfortable with?
How they answeredI gave a high level summary of the software work I'd done and kept it pretty broad. I told him I was strongest in Python and didn't really have TypeScript experience.
- 2
Technical round
CodingData Structures & AlgorithmsThis was a standard 1 hour LeetCode-style coding screen with an agent engineer, and the interviewer was genuinely friendly and accommodating even though the question itself didn't feel related to the actual day-to-day work.
Q1. Imagine an Excel-style spreadsheet where cells can reference other cells. How would you detect whether there's a circular reference?
How they answeredI mapped the spreadsheet idea to a graph problem and treated it as cycle detection across cell references. The interviewer said I could start brute force and then optimize, but I just went straight to the more efficient graph-search solution and wrote that out as my main implementation. There weren't extra constraints piled on afterward. The interviewer was also pretty chill about the format and even said I didn't have to narrate every keystroke if that was distracting.
Follow-up questions- You can start with a brute-force approach and then move to a more efficient one if you want.
- 3
Take-home assignment
Artificial IntelligenceProduct DesignExecutionProject DiscussionThe take-home was the most job-relevant part by far: I had to build a small customer support AI agent, choose what to ship under time pressure, and explain how I'd measure it in production.
Q1. Build a customer service AI agent for a fake outdoors company, pick 2 of 5 possible features to implement, and be ready to justify your choices.
How they answeredI built a simple conversational agent on top of the OpenAI API using a basic loop plus tool calls. I kept it lightweight instead of using a bigger framework. I explained why I chose the features I chose, what success metrics I would watch, and what observability I would want if this were live. The main technical thing I had to learn along the way was how tool calling actually worked, and I restarted the architecture a few times before it felt right.
Follow-up questions- If this were running in production, what metrics would you track and what observability dashboards would you want?
- 4
Final / onsite round
DebuggingTechnicalThis was a TypeScript and React debugging round that I think was the real gate, and for me it was easily the worst part of the process because it was not actually language-agnostic and the main interviewer was cold.
Q1. Here's some prewritten TypeScript and React code with several bugs in it. Can you debug and fix it?
How they answeredI had already told them I was basically a Python person and had asked if I could do this round in Python, but they said no. I spent about a week trying to learn enough TypeScript and React to survive it and still felt behind. The interview started 10 to 15 minutes late, there were two interviewers, and the main one was rude and very critical from the start. There were around five or six things to debug, and even if you know the stack it's a time crunch. I pretty much bombed this round.
- 5
Final / onsite round
PresentationProject DiscussionArtificial IntelligenceTechnicalThis round was with two agent engineers in a conference room and was split between my take-home and how well I could explain a technical topic simply.
Q1. Walk us through the agent you built and why you made the design choices you made.
How they answeredI expected this to be a real demo, but they had already run my code from the instructions I submitted a few days earlier, so the live part stayed pretty high level. I shared my screen, talked through the architecture, and explained why I had made certain choices. They asked a few high-level questions, but it was not nearly as deep a dive as I expected.
Q2. Give us a short 5 to 10 minute explanation of a technical topic of your choice.
How they answeredI chose to explain how RAG works. I treated it like a mini teaching exercise where the point was to take something technical and explain it in a simple, concise way instead of trying to sound overly fancy.
- 6
Final / onsite round
BehavioralCross-FunctionalThe last round was with my would-be manager and felt like a classic resume and behavioral conversation, not a trick round.
Q1. Walk me through your resume and why you made each move in your career.
How they answeredI mostly walked through the reasoning behind each step in my career and how one role led to the next. In my case the whole conversation stayed very anchored on my resume rather than drifting into abstract leadership questions.
Q2. Tell me about a time you worked with stakeholders, and tell me about a project you're most proud of.
How they answeredI answered with examples of stakeholder-heavy work and the projects I felt best about. The tone was broad and standard, not adversarial. It felt like they were mainly checking how I think about collaboration, ownership, and what kinds of work I value.
Tips from the candidate
I'd prep this in two separate lanes. First, do the normal graph and tree LeetCode prep because the phone screen was basically that. Second, do not believe the language-agnostic framing for the onsite debugging round. If you're interviewing here, I would absolutely get comfortable with TypeScript and basic React. For the take-home, practice building a simple agent without hiding behind a big framework, understand OpenAI tool calling, and be ready to talk through metrics, observability, and why you chose what to ship. Also, if I could do it over, I would avoid getting boxed into a comp range on the first recruiter call.
It seems like Sierra was hiring very aggressively and had already built a very repeatable process around that. When I was onsite, basically every conference room had interviews going on, and I'm pretty sure they were asking the same technical screen question to everyone. It felt org-dependent rather than team-random, and every single person I met was an agent engineer, not a bunch of cross-functional interviewers.
Company culture
Compared with other AI startups I talked to, Sierra felt much more professional and recruiter-driven in a big-tech way, even though I did run into one clearly rogue interviewer. The role also felt a little forward-deployed-engineer-ish to me, with a lot of customer implementation energy, probably more post-sales than pre-sales, and I noticed they seemed to like engineering profiles that also had a business lens.