Timeline
- Recruiter reached out: June 2026 (direct outreach)
- Recruiter Screen: ~1 week later
- Technical Rounds 1-4: Over 2 weeks
- Result: Pending — recruiter mentioned HC is evaluating feedback and looking for team fit
Background
- ~1.6 years of experience as an AI Engineer at a product-based startup
- Strong problem-solving background
- Working on production GenAI/LLM systems day-to-day
Recruiter Screen (30 min)
Non-technical. Covered:
- Current role and responsibilities
- Current CTC and compensation expectations
- Total experience (~1.6 years)
- Why looking for a switch
- Problem-solving background overview
- Interview process walkthrough
Nothing surprising here. Standard logistics round.
Technical Round 1 (45 min) — Strong Hire
Difficulty: Medium-Hard Topics: Trie + HashMap
The problem involved a combination of Trie and HashMap concepts. I came up with a working approach initially but got stuck optimizing it. The interviewer gave a couple of subtle hints that helped me identify the intended optimal solution.
After coding the solution, we discussed:
- Alternative approaches
- Time and space complexity analysis
- Edge cases
What worked: Thinking out loud through the stuck moment rather than going silent. The interviewer appreciated the structured reasoning even before I found the solution.
Technical Round 2 (45 min) — Strong Hire
Difficulty: Hard Topics: Graphs + Disjoint Set Union (DSU)
This was the hardest round. The problem required a modified Union-Find implementation — not the textbook version. The interviewer pushed on:
- Correctness proofs
- Complexity analysis
- Trade-offs between different approaches
My implementation got slightly messy towards the end due to time pressure, but the discussion around the solution was strong. The interviewer seemed satisfied with my understanding of the approach even where the code wasn't perfectly clean.
Technical Round 3 (45 min) — Hire
Difficulty: Medium-Hard Topics: Dynamic Programming + Binary Search
This round had progressive optimization:
- Start with recursive approach
- Add memoization
- Further optimization
- Final optimal solution using Binary Search
The interviewer kept pushing for the next level of optimization — don't stop after your first working solution.
GenAI Discussion (last ~10 minutes):
Since I work as an AI Engineer, the interviewer pivoted to discussing:
- Production use cases of Generative AI
- Designing agentic workflows
- How I'd leverage GenAI for automation in Google Ads ecosystem
- Challenges in deploying LLM-based systems at scale
This felt natural and conversational. If you have domain expertise, be ready to discuss it — it can differentiate you.
Round 4 — Googliness + DSA + Engineering (45 min) — Lean Hire
This was my weakest round. It combined:
- A DSA problem
- Behavioral/Googliness questions
- Engineering workflow and collaboration questions
What went wrong: I couldn't complete the coding problem in time. Once I got stuck, I didn't communicate my thought process clearly enough. I felt rushed trying to balance the behavioral and coding portions.
Lesson learned: Time management in mixed rounds is critical. If you're stuck on the code, explicitly tell the interviewer your plan and ask if they'd like to see you continue coding or discuss the approach.
Tips
- Don't stop at the first working solution — Google interviewers expect you to optimize multiple times (recursive → memo → tabulation → binary search)
- Practice explaining while stuck — silence kills your signal. Talk through what you're considering even if you don't have the answer yet.
- Be ready for follow-up modifications — they'll twist the problem after you solve the base case
- Focus areas: Graphs, DSU, Trie, Dynamic Programming, Binary Search, HashMaps, complexity analysis
- If you have AI/GenAI experience, prepare to discuss production systems, design decisions, and real engineering challenges — not just theory
- Googliness is real — prepare behavioral stories about collaboration, handling ambiguity, and pushing back respectfully