My background: undergrad from University of Washington, CSE, with about 5 years of YOE.
Timeline:
Week 1: HR Screening
Week 2: Three virtual interview rounds (HM + Project + AI Fundamentals)
Week 3: On-site (Coding Project + System Design)
One day after the on-site: HR was incredibly efficient and gave me a verbal offer right away.
1. HR Screening
Mainly talked about my current interest and my work authorization status; HR gave a rough range for the package. This round was pretty relaxed — just show some interest in the company's business and you're fine.
2. Virtual Interviews (three back-to-back rounds)
Round 1: Hiring Manager Chat. Mostly behavioral + team fit. We talked about what I'd worked on at Amazon; the HM cared a lot about multi-agent orchestration and LLM-related experience.
Round 2: Project Deep Dive. We went deep on a complex project I'd worked on before. My advice: pick a project with a complex architecture and clear challenges/trade-offs to talk through. The safest approach is to prepare an architecture diagram ahead of time so you don't get stuck describing it.
Round 3: AI/ML Fundamentals. Asked some ML basics (e.g. F1 score, classification vs. regression, benchmarking, etc.), plus questions about model inference, pipeline optimization, and how to turn an AI agent into a service. The rest of the questions were about LLMs — you need some understanding of Transformers, context engineering, RAG, grounding, guardrails, and so on.
3. On-site Interview
The on-site was split into two main parts:
Part 1: Hands-on Mini Project (Coding). The problem: implement a small project on the spot, one the interviewer described verbally. Mine was building a high-concurrency web crawler to crawl a specified site, then sort the content per the requirements and export it as CSV.
My take: the problem wasn't hard — if you have real project experience day to day, it should feel pretty smooth. I finished it in about 10 minutes using Python. After I finished, I talked through optimization ideas and edge cases with the interviewer, and the feedback was good. While coding I talked through my thought process out loud, and tried to keep the coding style close to best practices. If you have production experience, it also helps a lot to mention how a production version of certain features would actually be implemented.
Part 2: System Design (Google Sheets). The problem: design a web service similar to Google Sheets. Focus: the interviewer asked me to focus on the backend and the data models. Discussion points: concurrency control (how to handle multiple people editing at the same time), the storage layer choice and how to handle loading/saving at large scale, the snapshot backup system, and the database tables.
Summary and thoughts
Prep advice: fundamentally, an AI Engineer is just a Software Engineer + AI. I'd recommend reviewing the relevant terminology and architecture ahead of time. Even though this loop didn't have a single LeetCode-style question, the grinding I did beforehand still gave me more confidence walking into the interviews.
Language: when writing the small project live, Python's conciseness was genuinely an advantage — it saved a lot of time that would otherwise have gone into boilerplate code.
Speed: I got the offer the day after finishing the interviews — that speed is honestly impressive for 2026.
Wishing everyone an early offer in 2026!
Discussion
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