Hey everyone, I have a question about offer negotiation for a Google SWE position.

Does anyone know what typically happens once you start negotiating? Will Google usually come back with a counteroffer (e.g., match the number, partially increase the offer, or reject the request), or is there any risk that they could pull the offer entirely?

I haven’t received the compensation numbers yet, but I expect to get them soon and want to understand how the negotiation process generally works before I respond. Thanks!

Location: US

Level: L4

Saw this accepted Google L5 SWE offer (offer data source: chill interview)

• Mountain View, 7 YOE
• Base: $280K
• Sign-on: $50K
• RSUs: $750K / 4 years
• Bonus: $42K
• Year 1 TC: $657K

This is way above a normal Google L5 package. Current Bay Area L5 SWE comp averages around $465K, so this offer is roughly 40% higher in year one.

It made me curious whether certain technical specialties consistently get pushed toward the top of the band.

The obvious candidates seem like:

AI/ML / GenAI
TPU / AI infrastructure
large-scale distributed systems
compilers / performance / hardware-software co-design
security / privacy
highly specialized Search / Ads / ranking infrastructure

Google is currently hiring L5-equivalent engineers for things like LLM modeling + GenAI production systems and TPU supercomputer infrastructure, where the required skill sets are much narrower than general backend SWE.

But interestingly, Google’s published base ranges for Senior SWE roles are still broadly similar across AI/ML and infrastructure. That makes me think the biggest difference probably isn’t a formal “AI salary band.”

It’s more likely: scarce specialization + critical team + competing offers + negotiation → much larger equity grant. And public ML/AI L5 submissions aren’t universally higher either — recent examples range around $370K–$456K annual TC. 

So I’m curious: Which L5 specialties actually command the strongest offers right now?

Is it Gemini/model work? TPU infrastructure? distributed systems? security? Or does specialization matter much less than having competing Meta/OpenAI/Anthropic offers?

Preparing for your next interview?

Chill Interview tracks recent interview experiences and recurring question patterns across top companies at here.

A candidate with 10 YOE recently shared these two Bay Area SWE offers with Chill Interview.

Meta E5

• $235K base
• $50K signing bonus
• $600K RSUs / 4 years
• $35.25K annual bonus
• $470.25K Year 1 TC

Salesforce Staff

• $290K base
• $500K RSUs / 4 years
• $43.5K annual bonus
• $458.5K Year 1 TC

Meta looks slightly better in Year 1, but that’s mostly because of the $50K sign-on.

Assuming flat stock prices, recurring bonuses, and no refreshers:

• Meta 4-year: ~$1.73M Salesforce 4-year: ~$1.83M

So Salesforce is actually about $103K ahead over four years, while also giving the candidate a Staff title and $55K higher base.

The company tradeoff is more interesting.

Meta probably has the stronger AI upside. Revenue grew 28% YoY last quarter and the company is spending aggressively on AI infrastructure and superintelligence.

But the organizational risk is hard to ignore. Meta recorded $1.18B in severance expenses from its May 2026 headcount reduction, after several years of layoffs and restructuring. Even strong performers can end up worrying about ratings, reorgs, and whether their team survives the next reshuffle.

Salesforce is the slower but potentially more predictable bet. Revenue grew 11% YoY in its latest quarter, while Agentforce and the broader “agentic enterprise” strategy remain the core growth story. 

So I’d frame it as:

• Meta: slightly higher Year 1 TC, stronger AI/consumer-tech upside, but more org volatility and lower level
• Salesforce: Staff title, higher base, ~$100K more over four years, potentially more stable enterprise environment

Would you take Meta E5 for the AI upside, or Salesforce Staff for the title, higher recurring comp, and potentially less organizational chaos?

Preparing for your next interview?

Chill Interview tracks recent interview experiences and recurring question patterns across top companies at here.

"This interview experience is sourced from Chill Interview"

Interview Summary

The OpenAI SWE virtual onsite covered ChatGPT system design, a custom indexed-sequence data structure, and debugging an existing Python job scheduler codebase. The questions were mostly unfamiliar rather than standard interview problems, with follow-ups around SSE vs. WebSocket, rendering long conversations, data-structure performance, testing, and asynchronous job execution

Interview Details

Round 1 — System Design: ChatGPT

The system design question was to design a simplified version of ChatGPT. At the beginning, the interviewer explicitly said not to focus on large-scale architecture and that persistence was not required.

The discussion later moved into several implementation-level follow-ups. One question was why the response-streaming path should use Server-Sent Events (SSE) rather than WebSocket.

The interviewer also asked about the frontend side of the product: as a conversation becomes very long, how could the client optimize rendering so that displaying a large chat history remains responsive?

Round 2 — Coding: Indexed Sequence With Checkpoints

The first coding problem asked me to implement a simplified ordered sequence supporting:

add(index, value)
get(index)

• add(index, value) inserts a string at the specified position. The element previously at that position and everything after it shift one position to the right.
• get(index) returns the value stored at the requested index.

Initially, I interpreted “vector store” as something related to vector databases or embeddings. The intended meaning was actually closer to a C++ std::vector or Java ArrayList: an ordered dynamic sequence with integer-indexed access. A straightforward linked-list implementation was considered too slow because get(index) could require traversing from the beginning of the list.

Want to learn more questions asked in other interview rounds? I've put up the full version at here.

Preparing for your next interview?

Chill Interview tracks recent interview experiences and recurring question patterns across top companies at here.

Saw this Meta E5 MLE offer (data source: chill interview)

• 8 YOE
• Base: $235K
• Sign-on: $70K
• RSUs: $900K / 4 years
• Year 1 TC: $565K

Everyone talks about frontier models when discussing AI careers. But I wonder if some of the most valuable ML work is still the less glamorous stuff: ranking, recommendation, ads optimization, retrieval, and large-scale inference.

Meta’s core business is still printing money — Q2 revenue grew 28% YoY, ad impressions rose 14%, and average price per ad increased 12%. Meta explicitly says AI is already accelerating the core business. 

So maybe an MLE improving Instagram/Reels ranking or ads by 0.5% can create more business value than a lot of “frontier AI” projects.

That makes me curious: If you were choosing an ML career, would you rather work on frontier models — or on recommendation/ads systems where the impact on revenue is much more direct?

Preparing for your next interview?

Chill Interview tracks recent interview experiences and recurring question patterns across top companies at here