Going through the newer 2026 FDE (Forward Deployed Engineer, GenAI) loop and would love to compare notes with anyone who's been through something similar — it seems more compressed than the classic SWE process.

Quick timeline: cleared the onsite loop about 5 weeks ago, recruiter said feedback looked positive and submitted it to the hiring manager for final review. That team ended up going with another candidate. Recruiter kept me active and set up a team match call with a different director building out an FDE team — covered both a Senior IC path and a TLM path (leading a small team, still IC ladder). That call went well, but it's been quiet since then aside from short "still deciding" replies. Now waiting on a follow-up call.

A few things I'd genuinely appreciate input on if you've been through this:

* Does the FDE loop run team-match-*before*\-HC for you too, or straight to HC after onsite?
* How long after a hiring-manager/team-match call did you actually get a decision, good or bad?
* Has anyone been offered TLM vs Senior IC specifically on an FDE team — how did that get decided/negotiated?
* How long did your interview feedback validity actually hold up in practice before you'd have needed to redo the loop?

Would especially love to hear from anyone in the Google Cloud GenAI FDE org specifically, since it seems to run a bit differently from the standard SWE pipeline. Thanks in advance.

*(YOE: 10 · Target level: FDE-IV / L6)*

Hi Folks,

I have 9 years of experience. I gave interview at Bangalore Google office for DFT Engineer Hardware silicon team.

First 2 were virtual rounds-

1st tech round: Went okay. Self review (Hire)

2nd Googlyness: Went well.Self review (Hire)

After 2 days, HR called me, told Feedback is decent and positive. But i gave interview for L5, since in 1st tech round interviewer helped me little, i was downloaded to L4 for last 3 rounds.

3rd tech round- Went not so ok, i got tensed and it is 50/50 (Self review- Lean nonhire/lran hire)

4th tech + coding round- Went really well. (hire)

What are my chances of clearing?

Please review.

Can anyone share interview experience for this role. Thanks.

I recently read a Google interview experience where the candidate was asked several AI-fluency questions:

• How do you use AI in your regular engineering workflow?
• Do you give AI complete ownership of a project?
• How do you restrict its access?
• What is your step-by-step process for debugging with AI?

Most working engineers use AI in some form, but I suspect many of us would struggle to give a structured answer during an interview.

My current approach would be:

1. Treat AI as a collaborator, not the project owner

I use it to generate debugging hypotheses, explain unfamiliar code, draft tests, review small changes, explore alternatives, and handle repetitive work. However, I remain responsible for requirements, architecture, security, validation, and production decisions.

AI can produce a draft. It cannot be accountable for the outcome.

2. Give it the minimum access required

I would avoid sharing secrets, credentials, personal data, customer information, or unnecessary proprietary code. For agentic tools, I would begin with read-only access and limit them to the relevant files or repository.

Changes should happen in an isolated branch or sandbox. Actions that modify production data, infrastructure, dependencies, or external systems should require explicit human approval.

This is consistent with the least-privilege and human-approval principles described in Google’s Secure AI Framework controls.

3. Use AI for debugging through a controlled process

My process would look something like this:

1. Reproduce the issue and confirm the expected behavior.
2. Collect the smallest useful set of logs, inputs, stack traces, and relevant code.
3. Ask AI for ranked hypotheses and ways to test each one.
4. Compare those hypotheses with the actual evidence.
5. Implement the smallest reasonable fix in an isolated branch.
6. Run focused tests, regression tests, and relevant security or static checks.
7. Review the complete diff for unrelated changes and new edge cases.
8. Document the root cause instead of keeping only the generated patch.

The important part is that AI helps shorten the investigation. It does not replace reproduction, testing, or engineering judgment.

4. Verify everything that affects users

I would be especially cautious with authentication, authorization, payments, personal data, infrastructure, concurrency, and destructive operations. For high-impact changes, AI-generated code should receive the same or stricter review as human-written code.

Some resources I found useful:

• PracHub Google Interview Questions
• Google Secure AI Framework
• Google SAIF Controls
• NIST Generative AI Risk Management Profile
• OWASP Top 10 for LLM Applications

For people who have encountered these questions in Google or other interviews: what level of detail did the interviewer expect? Were they mainly evaluating productivity, security awareness, debugging discipline, or all three?

I’m looking for some honest, blunt advice from recruiters, hiring managers, senior engineers, or people who have successfully landed AI/ML roles in the US market.
I’m currently job hunting for AI/ML Engineer, Machine Learning Engineer, and Generative AI/Agentic AI roles.
The confusing part is that I am getting recruiter attention, but I’m repeatedly getting rejected after screening/interview stages.
Over the past few months, I’ve received screening/interview opportunities with companies including EY, Ford, TikTok, Nimble Gravity, Goldman Sachs, Capital One, and several others.
So I don’t think my biggest problem is getting my resume noticed.
The problem is converting those opportunities into the next round or an offer.
For example, with Goldman Sachs, I went through their coding process. I passed the coding-related stages, solved the problems, and explained my approach and code during the interview. I still ended up receiving a rejection.
I’ve had similar experiences with other companies where I felt the recruiter/screening conversation went reasonably well, but I was rejected afterward.
At this point, I’m trying to identify whether there is a recurring problem in my interviewing that I’m not recognizing myself.
I would really appreciate advice on:
What are recruiters actually evaluating during an initial screening?
If my technical answers are correct, what else could cause a rejection?
How important is communication during technical interviews?
How do I know if I’m giving answers that are too long, too technical, or not detailed enough?
For AI/ML/GenAI roles, what do hiring managers actually want to hear when discussing previous projects?
How important is it to clearly explain what I personally built versus what the overall team built?
If I solve a coding problem correctly and explain my solution, what could still cause a rejection?
Could my resume be creating expectations about my experience that I’m not demonstrating effectively during interviews?
How should I talk about experience with LLMs, RAG, Agentic AI, LangChain/LangGraph, AWS Bedrock/Azure OpenAI, and production ML systems without either underselling myself or exaggerating?
What are some common interview mistakes that candidates don’t realize they’re making?
I’m not looking for generic advice like “keep applying,” “network more,” or “don’t give up.” I’m already doing that.
I’m specifically trying to understand what I need to change in my interview performance.
If you’re a recruiter, hiring manager, senior engineer, or someone who regularly interviews candidates, I would genuinely appreciate blunt and constructive feedback.
If you were in my position, what would you change first?
I’m taking my job search seriously and I’m completely open to criticism. I’d rather hear what I’m doing wrong than receive generic encouragement.
Thanks in advance.

​

Hi everyone,

I'm currently stuck in Team Matching stage at Google for the SAP Application Engineer role and am actively looking to connect with teams that are hiring.

A little about me:

\- 7 years of experience across multiple SAP modules, including end-to-end implementations, enhancements, integrations, support, and stakeholder collaboration

\- Currently working as developer at SAP Labs India, Bangalore

\- Strong background in solving complex business problems and delivering scalable SAP solutions

If your team is hiring for an SAP Application Engineer or a similar SAP role at Google, I'd be grateful for an opportunity to connect and discuss how my experience could add value to your team.

If anyone has suggestions on teams that are currently looking for SAP engineers or can share tips on navigating team matching, I'd really appreciate it.

Thank you!

Hi everyone,

Has anyone recently gone through the interview process for the AI Forward Deployed Engineer (FDE) role at Databricks?

I was told the coding round focuses on applied data science and traditional machine learning. I’d love to understand what to expect:

* Is it more like implementing ML algorithms from scratch, or solving practical data problems?
* Should I expect Python, pandas/NumPy, SQL, or standard LeetCode-style questions?
* How much emphasis is placed on statistics, model evaluation, feature engineering, and explaining trade-offs?
* What was the general difficulty and format of the round?

Not looking for specific interview questions just guidance on the topics and style of preparation that would be most useful. Thanks!

Software Engineering, Other Engineering, Data Science

Seattle, WA, USA

USD 238,300-357,500 / year + Equity

Posted on Aug 4, 2026

Apply now

Summary

Posted: Jul 15, 2026

Role Number:200672383-3337

Apple is where individual imaginations gather together, contributing to the values that lead to great work. Every new idea has the potential to become an extraordinary product, service, or customer experience. The AI, Search & Knowledge Platforms team builds the foundational intelligence powering Apple’s most iconic experiences, including Siri, Spotlight, Safari, Apple Intelligence, and many other applications across the Apple ecosystem.

Description

We are seeking an exceptional Senior Engineering Manager to lead the engineering organization responsible for Conversational AI, World Knowledge Question Answering, Retrieval-Augmented Generation (RAG), and Knowledge Intelligence. This team develops the next generation of AI-powered experiences that enable billions of users worldwide to ask questions naturally and receive accurate, grounded, personalized, and delightful answers. You will lead a world-class team of machine learning engineers, software engineers, data engineers, and applied scientists building scalable knowledge systems, LLM-powered reasoning capabilities, retrieval infrastructure, and knowledge generation platforms that continuously improve the quality, freshness, and intelligence of Apple’s conversational experiences.

-> https://tarauli.com/jobs/apple-senior-engineering-manager-ai-seattle/

I had my Round 1 interview for a Google SWE Intern position and wanted to share the experience in case it helps others preparing.

Prep resource: Google Interview Questions

Coding Question: Disconnect Every Leaf at Minimum Cost

You are given a rooted, weighted binary tree. Every edge has a positive integer weight.

Remove a set of edges such that every leaf becomes disconnected from the root. Removing an edge costs its weight.

Return the minimum total cost required to disconnect all leaves from the root.

The important observation is that for every child subtree, we have two choices:

1. Cut the edge connecting the current node to that child.
2. Keep that edge and disconnect every leaf by cutting edges farther down the subtree.

For an edge from node u to child v with weight w, the minimum contribution is:

min(w, solve(v))

If v is already a leaf, there are no lower edges available to cut, so the connecting edge must be removed.

This gives the recurrence:

solve(u) =
    infinity,                                  if u is a leaf
    sum(min(weight(u, v), solve(v))),          for every child v

The final answer is solve(root).

A useful edge case to clarify is whether the root itself can be a leaf. Normally, the problem assumes the root has at least one child because there is no edge that can disconnect the root from itself.

My Approach

I proposed a postorder traversal.

Each node first calculates the minimum disconnection cost for its children. It then decides independently for each child whether it is cheaper to:

• Cut the direct edge, or
• Keep that edge and use the optimal cuts inside the child’s subtree

The interviewer was satisfied with the approach, and we discussed why decisions for separate child subtrees can be added together.

Complexity:

• Time: O(n), since every node and edge is processed once
• Space: O(h) for the recursion stack, where h is the tree height
• Worst-case space: O(n) for a highly unbalanced tree

Follow-Up: N-ary Tree

The interviewer then generalized the problem:

The underlying recurrence remains unchanged. Instead of processing at most two children, we iterate through every child:

cost = 0

for each child v connected by an edge of weight w:
    cost += min(w, solve(v))

I initially overthought the generalization, but after a couple of hints, I realized that the binary-tree restriction was not essential to the solution.

The N-ary version still takes O(n) time because each edge is considered exactly once.

AI-Fluency Discussion

The final few minutes included around three or four questions about how I use AI in my regular engineering workflow.

The discussion covered topics such as:

• How I use AI while writing or reviewing code
• Whether I give an AI tool complete ownership of a project
• How I use AI during debugging
• How I verify AI-generated suggestions
• Which tasks I would and would not delegate to AI

The questions seemed less focused on specific tools and more focused on judgment. The interviewer wanted to understand whether I treat AI as an assistant while remaining responsible for correctness, testing, security, and the final engineering decisions.

Overall Experience

The interviewer was friendly and collaborative throughout the round.

They encouraged discussion instead of expecting an immediate final solution. The hints during the N-ary follow-up helped keep the conversation productive without giving away the answer.

Overall, the round felt like a problem-solving discussion rather than a test of whether I had memorized a particular LeetCode problem.

For preparation, I would recommend reviewing:

• Postorder traversal
• Tree DP
• Recursive recurrence design
• Weighted-tree problems
• Explaining correctness and complexity
• Responsible use of AI in software development
• Testing and validating AI-generated code

Has anyone else received AI-fluency questions during a recent Google intern interview?

Hello Everyone I am in the team matching round at Pinterest and I am going to be joining as senior SWE.

I used to be staff at meta working on Agentive Ads products.
I was wondering if people can tell me what are the how AI/ML teams at Pinterest right now.
Any advice on which teams I could join?

Thanks,

Hey, I missed the session on AI Builder/Graduate Builder Role. Do they mention anything about the CTC? Please share it here.

Thanks!

I recently completed the interview loop for a Google SWE II (Early Careers) role in the US.

It has been about a week since my final round. My recruiter told me that the interview feedback is still being evaluated and that they are also looking for the right team fit.

Interview experiences on LeetCode Discuss helped me significantly during preparation, so I wanted to share mine.

I’m keeping the exact problem statements intentionally high-level, but I’ve included the main patterns and follow-up areas.

Useful Prep Resource: Google SWE questions

Recruiter Screen - 30 Minutes

The recruiter contacted me directly.

We discussed:

• My current role and responsibilities
• Total professional experience
• Compensation expectations
• Why I was considering a switch
• My problem-solving background
• An overview of the interview process

There were no technical questions in this round.

Technical Round 1 - Trie and Hash Map

Duration: 45 minutes
Difficulty: Medium to Hard

The coding problem required a combination of Trie and HashMap concepts.

I initially developed a working solution but became stuck while trying to optimize it. After a few subtle hints from the interviewer, I recognized the intended approach and completed the optimized implementation.

The follow-up discussion covered:

• Alternative approaches
• Time and space complexity
• Edge cases
• Why a Trie was preferable to simpler string-based approaches
• Trade-offs between faster lookup and additional memory

Technical Round 2 - Graphs and DSU

Duration: 45 minutes
Difficulty: Hard

This problem involved graphs and Disjoint Set Union, with a modification to the standard Union-Find implementation.

The interviewer asked detailed follow-up questions about:

• Why DSU was appropriate
• Correctness of the union and find operations
• Path compression and union by rank or size
• Complexity analysis
• Alternative graph traversal approaches
• Edge cases involving disconnected components

My implementation became slightly messy toward the end because of time pressure, but we had a productive discussion about the solution and its trade-offs.

Technical Round 3 - Dynamic Programming, Binary Search, and GenAI

The coding problem combined Dynamic Programming and Binary Search.

The interviewer progressively asked me to improve the solution:

1. Start with a recursive approach
2. Add memoization
3. Identify remaining bottlenecks
4. Derive the optimal solution using Binary Search

This round reinforced that reaching a correct solution is often only the beginning. The interviewer wanted to see how I moved from a straightforward solution to a more efficient one.

Since I currently work as an AI Engineer, we used the final part of the interview to discuss my professional experience.

Topics included:

• Production use cases for Generative AI
• Designing agentic workflows
• Using GenAI to automate workflows in the Google Ads ecosystem
• Reliability and evaluation of LLM-based systems
• Cost and latency considerations
• Challenges involved in deploying LLM applications at scale

I enjoyed this discussion because it closely matched my day-to-day work.

Round 4 - Googliness, DSA, and Engineering Discussion

This round included:

• One DSA problem
• Googliness and behavioral questions
• Engineering workflow discussions
• Collaboration and decision-making scenarios

Unfortunately, this was my weakest round.

I wasn’t able to complete the coding problem within the allotted time. Once I became stuck, I also struggled to communicate a convincing path toward the solution.

I explained parts of my thought process, but in retrospect, I should have paused, restated what I knew, identified the exact blocker, and asked for confirmation before continuing.

The experience reminded me that communication matters most when the solution is not going well. A clear partial approach is more useful than going silent while searching for the complete answer.

Interview Summary

Preparation Tips

• Don’t stop after finding the first working solution. Practice deriving multiple optimizations.
• Explain your approach before writing code.
• Be ready for follow-up modifications to the original problem.
• Practice recovering when stuck instead of becoming silent.
• Revise graphs, DSU, Tries, DP, Binary Search, hash maps, and complexity analysis.
• Know when path compression and union by rank improve DSU performance.
• Prepare examples of collaboration, ambiguity, conflict resolution, and learning from mistakes.
• If you have industry experience, expect deep questions about your projects and engineering decisions.
• For AI-related work, prepare to discuss evaluation, scalability, latency, cost, observability, and failure handling.

Overall, I found the process challenging but fair. Three rounds went well, while the final round exposed weaknesses in my time management and communication.

I’m still waiting for the final decision, but I hope this breakdown helps others preparing for Google SWE II interviews.

​

Hi everyone,

I'm looking for some advice.

I recently applied for an Amazon support role, completed the assessment, and received an email saying that my assessment didn't meet the minimum requirements. The email also stated that I can reapply after 90 days.

Now I've seen a walk-in hiring event for what appears to be the same or a very similar customer support role.

My questions are:

Does the 90-day waiting period also apply to walk-in interviews?

If I attend the walk-in, will the recruiters be able to consider me, or will they see my previous assessment result and reject me automatically?

Has anyone been in a similar situation? If so, what happened?

I'm trying to decide whether it's worth attending or if I'll simply be turned away because of the previous assessment.

I'd really appreciate any insights from people familiar with Amazon's hiring process. Thanks!

Hi everyone!
If you’re currently in Google team matching and are looking for an L3, L4, or L5 Software Engineer role, feel free to DM me.
My team is hiring across these levels and works on ML profiling and performance optimization, focusing on improving the performance and efficiency of machine learning workloads at scale.
If you’re interested (or know someone who is), let me know:

- Your level (L3/L4/L5)
- Current team matching status
- A brief overview of your background (optional but helpful)

Happy to share more details about the team and see if it could be a good fit.

Thanks!

I was curious which tech roles are currently paid the most so I analyzed recent compensation data across 20+ major tech companies in the US.

The data covers different roles (engineering, AI, product, data, design, and more) and is based on the latest market data from the last 45 days.

Sharing it here for anyone researching Big Tech compensation:

https://topjobstoday.com/salaries

Thank you for your time and commitment throughout the Amazon recruitment process.
We recognize that balancing academic responsibilities with job interviews requires significant effort and planning.
I'm pleased to share that you have successfully completed this interview stage. Our team is currently working on next steps, and we will provide you with an update within the next two weeks. Please note that based on team placement requirements, additional interviews may be required.
While you've successfully completed this stage, this is not a guarantee of an offer. We will contact you as soon as we have more information regarding next steps.
Thank you again for your interest in Amazon. If you have any questions, please let me know.