Saw this Netflix Machine Learning Engineer offer (data source: Chill Interview)

• Los Angeles
• Bachelor’s, 0 YOE
• Junior-level
• Base: $335K
• Relocation: $30K
• First-year TC: $365K

The surprising part isn’t just the money. It’s Bachelor’s + 0 YOE + MLE.

Netflix now has a real new-grad pipeline, and its ML org works on things like recommendations/search, personalization, ML platforms, and streaming/discovery systems.

So I’m curious what a profile getting $335K base straight out of school actually looks like.

• Strong Netflix internship?
• Top-tier ML projects?
• Recommendation systems experience?
• Research publications even without grad school?
• Competing Google/Meta offers?

And more broadly: is it easier to land a MLE job than a SWE right now?

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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?

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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?

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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?

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Saw this Google L4 SWE offer (data source: chill interview)

• Master’s, 0 YOE
• AI/ML track
• Base: $205K
• RSUs: $550K / 4 years
• First-year TC: $510K

The title is still Software Engineer, which made me curious what actually changes when you’re on the AI/ML track.

Looking at Google’s current roles, both regular SWE and AI/ML SWE still write production code, do design/code reviews, debug systems, and own reliability. The difference is that AI/ML SWEs also work directly with ML infrastructure, model optimization, data processing, and specialized ML systems.

Some Google AI/ML roles go even deeper across the stack — model architecture, training/inference systems, hardware acceleration, compilers, and production APIs. 

So is the simplest way to think about it: regular SWE = build the product/system

AI/ML SWE = build the product/system + understand how the model and ML stack behave in production

And unlike Research Scientist, you’re still primarily an engineer shipping production systems rather than being evaluated on papers/research output.

Curious: how different is AI/ML SWE day-to-day from normal backend/infra SWE? And if you start in general SWE, how hard is it to move into the AI/ML track later?

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Two Senior SWE offers in SF (data source: chill interview)

Pinterest IC15

• $260K base
• $420K RSUs, vesting 50/33/17 over 3 years
• $470K Year 1 TC

DoorDash

• $240K base
• $480K RSUs, vesting 40/30/20/10
• $432K Year 1 TC

Pinterest is $38K higher in Year 1, but because its initial grant finishes after Year 3, the four-year totals are surprisingly close:

• Pinterest: ~$1.46M
• DoorDash: ~$1.44M

So comp probably isn’t the deciding factor.

DoorDash currently has much stronger business momentum: Q2 revenue grew 36% YoY, orders grew 27%, and it’s expanding beyond restaurants into grocery, retail, international markets, merchant software, and AI products.

Pinterest is growing more moderately—Q2 revenue was up 18% YoY with record users—but it’s leaning heavily into AI-powered visual search, shopping, and advertising. 

The lifestyle angle may favor Pinterest. Its PinFlex model explicitly allows substantial virtual-work flexibility depending on the role, while DoorDash engineering has a reputation for a faster, more ownership-heavy environment. Pinterest itself describes PinFlex as giving employees autonomy over virtual work while still gathering when needed. 

So the tradeoff feels like:

• Pinterest: higher base, higher Year 1 TC, flexibility, AI/search/ads exposure
• DoorDash: faster company growth, broader marketplace problems, potentially more intensity

At basically the same 4-year comp, would you optimize for Pinterest’s flexibility or DoorDash’s growth trajectory?

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A candidate shared two Staff SWE offers with Chill Interview

Uber 5b

• $280K base
• $60K signing
• $960K RSUs, vesting 35/28/22/15
• $42K annual bonus
• $718K Year 1 TC

Stripe Staff

• $290K base
• $60K signing
• $880K equity
• $58K annual bonus
• $628K Year 1 TC

Uber looks $90K better in Year 1, mostly because its equity is heavily front-loaded.

But assuming flat equity values, recurring bonuses, and no refreshers:

• Uber 4-year: ~$2.31M
• Stripe 4-year: ~$2.33M

So the long-term comp is basically a tie.

The bigger question is which company you want to bet on.

Uber is already public and highly liquid. Q2 gross bookings grew 24% YoY, operating income grew 30%, and trailing-12-month free cash flow crossed $10B. The company is also aggressively positioning itself as the platform layer for autonomous vehicles. 

Stripe is still private, but it’s not typical startup paper money either. Businesses on Stripe processed $1.9T in 2025, up 34%, and its latest employee tender valued the company at $159B, giving employees at least some path to liquidity. 

Culture probably doesn’t make this an easy WLB choice. Stripe itself says the work moves quickly, is high-stakes, and can be demanding; Uber similarly describes a fast pace, complex problems, and high expectations. 

So I’d frame it as:

• Uber: much higher Year 1 TC, liquid stock, profitable growth, AV upside
• Stripe: higher base, smoother vesting, private-equity upside, arguably stronger fintech/internet-infrastructure career signal

If the 4-year money is basically identical**, would you take Uber’s liquidity and front-loaded comp—or bet on Stripe eventually being worth much more?**

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Saw this Apple ICT4 Data Scientist offer (data source: chill interview)

• Cupertino, 6 YOE
• Base: $215K
• Bonus: $21.5K
• Sign-on: $50K Y1 / $40K Y2
• RSUs: $180K / 4 years
• Year 1 TC: $331.5K

At first glance, $331K doesn’t look terrible. But the sign-on is doing a lot of work here.

Once it disappears, recurring TC is only around:

$215K + $45K stock + $21.5K bonus = ~$281.5K

That feels pretty light for an ICT4 Data Scientist in Cupertino.

Current reported Bay Area ICT4 DS comp at Apple averages around $336K, with roughly $100K/year in stock. This offer only has $45K/year of initial equity. 

So I’m curious:

Is this a genuine lowball, or does Apple commonly make up for weak initial grants with refreshers later?

If you got this offer, would you push hardest on the $180K RSU grant before signing?

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(offer data points source: Chill Interview)

Meta E6

• $270K base
• $80K signing
• $1.8M RSUs / 4 years
• $54K annual bonus
• $854K Year 1 TC

Sierra Staff

• $340K base
• $2.1M equity / 4 years
• $865K Year 1 TC

At first glance, these offers are almost identical. So this isn’t really a money decision. The harder question is whether Meta is still worth staying at.

Meta has gone through repeated rounds of restructuring and layoffs. In early 2025, it cut roughly 5% of employees through performance-based terminations. In 2026, Reuters reported another roughly 10% workforce reduction tied to its AI-driven organizational overhaul, which also triggered significant internal anxiety and employee pushback.

Even if you’re performing well, that can create a culture where people constantly think about ratings, reorgs, scope, visibility, and whether their team will survive the next restructuring. The compensation is excellent, but the psychological cost of staying can become part of the equation too.

Sierra offers a very different bet: $340K base + $2.1M private equity, with much more potential upside if the company keeps growing.

But Sierra isn’t exactly the “chill” alternative. Employee reviews describe an extremely intense environment; one recent engineering review reported 75+ hour weeks, although that’s anecdotal and shouldn’t be treated as representative of every team.

So the tradeoff feels almost ironic:

Meta: liquid RSUs, proven business, huge TC — but repeated layoffs, reorg anxiety, and a performance-heavy culture.

Sierra: startup risk and paper equity — but a chance to leave big-tech politics behind and join a company much earlier in its growth curve, potentially at the cost of even worse WLB.

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Saw this Google L7 / Senior Staff SWE offer (shared with Chill Interview)

• Mountain View
• Master’s, 15 YOE
• Base: $320K
• Bonus: $80K
• Sign-on: $100K
• RSUs: $2.2M / 4 years
• Year 1 TC: $1.336M

The number is obviously crazy, but I’m actually more curious about the job.

Google L7 is Senior Staff SWE, and at this level the official job descriptions start sounding very different from normal software engineering.

You’re still an IC, but Google describes these roles as owning things like:

• long-term technical direction
• architecture across multiple teams
• ambiguous, multi-year engineering problems
• alignment with senior product / engineering leadership
• mentoring and influencing engineers you don’t directly manage
• driving technical strategy across an org

Some current L7 postings literally describe the engineer as the architectural authority for an organization or the person responsible for a multi-year technical roadmap.

Which makes me wonder what the day-to-day actually looks like.

At L5/L6, I can still picture a strong engineer designing systems, reviewing code and shipping major projects.

But at L7: Are you still writing meaningful amounts of production code? Or is the real skill basically being able to make 5–10 teams move in the same technical direction without having management authority over them?

The comp also puts the scope into perspective.

Also curious: What actually separates someone who tops out at L6 from someone who makes L7? Is it technical depth, org-wide influence, picking the right problems, executive visibility, or something else entirely?

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Saw this IMC Trading ML Engineer offer (shared with Chill Interview)

• Chicago
• PhD
• Base: $290K
• Sign-on: $260K
• Annual bonus: $420K
• First-year TC: $970K

Looking at what IMC actually hires for, this seems very different from a normal tech-company MLE job.

They specifically looks for PhDs with strong ML/probability/statistics backgrounds, deep-learning research experience, and ideally publications at places like NeurIPS, ICML or ICLR. The models are applied directly to trading strategies across equities, futures and options.

Their experienced ML researchers work on things like alpha research, market prediction, signal generation and portfolio optimization.

So maybe the economics are pretty simple:

• At a normal tech company, a great ML model improves a product.
• At a trading firm, a great ML model can potentially directly make money.

And that may explain why someone with zero industry experience can still command this kind of package if their research background is exceptional.

Curious: What does the profile of a PhD getting this offer actually look like? Multiple NeurIPS/ICML papers? Strong research internships? Specialized work in time series / RL / transformers / optimization? Competing Jane Street / Citadel / HRT offers?

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Saw this accepted CoreWeave Senior SWE offer (shared with Chill Interview)

• Bay Area, 6 YOE
• Base: $250K
• Bonus: $37.5K
• RSUs: $1.5M / 4 years
• Year 1 TC: $662.5K

The headline TC looks insane for 6 YOE. But the breakdown is what makes this interesting.

$375K of the $662K is stock. So roughly 57% of this person’s first-year comp depends on CoreWeave equity.

And that’s not exactly a low-volatility stock. CRWV closed around $80.92 today, down from roughly $100 less than a week ago. That’s close to a 19% drop in five trading days.

Which makes accepting this offer feel less like: “Do I want to work at CoreWeave?”

and more like: “How bullish am I on AI infrastructure spending for the next four years?”

Because CoreWeave is basically one of the purest public bets on the AI compute boom.

The bull case is pretty wild:

Q2 revenue hit $2.58B, revenue backlog reached $104.2B, and the company added another $25B+ of customer commitments shortly after the quarter ended. Customers now include Meta, Anthropic, Databricks and a growing list of AI companies and enterprises.

But the other side is equally extreme.

CoreWeave had $35.6B of debt at the end of June and is planning to spend $35–39B on capex this year. Its own filing says it still has significant operating losses and an accumulated deficit of about $4B.

And the whole neocloud model is starting to get questioned: huge debt, massive GPU/data-center commitments, and economics that depend on AI compute demand staying enormous for years. Reuters Breakingviews recently compared the neocloud buildout to the late-1990s broadband infrastructure boom.

I’m curious: Would you take $662K at CoreWeave knowing more than half your comp is effectively a leveraged bet on the AI capex cycle?

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Saw this AMD Senior Hardware Engineer offer (source: Chill Interview)

• Bay Area
• Master’s, 9 YOE
• Base: $210K
• Sign-on: $40K
• Bonus: $31.5K
• RSUs: $500K / 4 years
• Year 1 TC: $406.5K

The comp is solid, but AMD itself is what makes this offer interesting to me. If you work in hardware right now, Nvidia is obviously the company everyone talks about.

But I wonder if AMD might actually be the more interesting career bet. Nvidia still has more than 80% of the AI accelerator market, so AMD is nowhere close to winning yet.

But AMD’s data-center business is moving fast. Revenue there more than doubled YoY to $6.72B last quarter, and Lisa Su expects data-center revenue to more than double again by 2027.

And this isn’t just theoretical anymore. Anthropic agreed to deploy up to 2GW of AMD MI450 GPUs, while OpenAI and Meta are also working with AMD around its new Helios rack-scale platform.

So as an engineer, there are two very different propositions:

• Nvidia: join the company that already won and work on the dominant platform.
• AMD: join the challenger while it’s trying to prove that Nvidia doesn’t get to own the entire AI compute stack.

That second one could arguably give engineers more interesting problems — hardware/software co-design, rack-scale systems, ROCm, packaging, networking, performance-per-dollar — because AMD still has a lot it needs to catch up on.

So I’m curious: If the compensation were reasonably close, would you rather work at Nvidia or AMD right now?

Would you choose the dominant company with stronger ecosystem/network effects, or the challenger where there may be more room to make an outsized impact?

Especially curious to hear from hardware / ASIC / GPU engineers: is AMD genuinely becoming a better place to do cutting-edge AI hardware work, or is Nvidia still in a completely different league internally?

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Saw this DoorDash Senior SWE offer (shared with Chill Interview)

• San Francisco
• 8 YOE
• Base: $240K
• Equity: $480K / 4 years
• Year 1 TC: $432K

The comp is solid, but what I find more interesting is what DoorDash is turning into.

A few years ago I would’ve thought a DoorDash SWE mostly meant marketplace, payments, dispatch, restaurant tooling, etc.

Now DoorDash is building:

• its own autonomous delivery robot, Dot
• its own drone program, DoorDash Air
• software that decides whether an order should be delivered by a Dasher, robot, or drone

DoorDash just got FAA certification to operate commercial drone delivery, and its Q2 results say it’s increasing investment in both land and air autonomy. It expects Dot to handle a high-single-digit percentage of orders in its largest test market by year end.

Meanwhile the core business is still growing fast: Q2 marketplace GOV was $33.1B, up 36% YoY, and adjusted EBITDA rose 40%.

So the interesting question to me is: Is DoorDash quietly becoming one of the more interesting autonomy / robotics companies to work for?

You get the scale and economics of an established marketplace, but also real-world routing, robotics, drones, embedded systems and multi-modal logistics problems.

Or is autonomy still a side bet, and most DoorDash SWEs are ultimately still working on a mature delivery marketplace?

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Saw this Reddit Senior SWE offer (source: chill interview)

• Bay Area, 9 YOE
• Base: $235K
• Equity: $140K (vest every year)
• First-year TC: $375K

The comp is decent, but what I find more interesting is Reddit as a company right now.

On one hand, the business looks really strong.

Reddit did $805M of revenue last quarter, up 61% YoY, and it was just added to the S&P 500. International user growth was also still around 28%.

But there’s also a weird AI problem.

A huge amount of Reddit discovery historically came through Google:

• “best X reddit”
• “company interview reddit”
• “is this product worth it reddit”

Now Google, ChatGPT, Perplexity, etc. increasingly answer those questions directly.

And Reddit itself said U.S. user growth slowed partly because search-engine referrals have become more volatile. U.S. daily users grew only 6% YoY last quarter.

At the same time, AI may actually make Reddit more valuable. The internet is getting flooded with generated content, while Reddit’s biggest asset is still millions of messy, opinionated, first-person human conversations. Reddit now has more than 130M daily active uniques and 26B+ posts/comments.

So maybe the future is: AI steals Reddit’s traffic, but simultaneously makes Reddit’s data more valuable. That makes joining Reddit as an engineer pretty interesting right now.

If AI assistants increasingly summarize Reddit instead of sending users to Reddit itself, does Reddit become stronger because its data is unique — or weaker because users stop visiting the product?

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>These two offer data points are sourced from Chill Interview

eBay — Staff SWE

• $275K base
• $400K RSUs / 4 years
• $27.5K annual bonus
• $402.5K Year 1 TC

Whatnot — Senior SWE

• $250K base
• $700K equity / 4 years
• $425K Year 1 TC

On paper, Whatnot only wins Year 1 by $22.5K. So this isn’t really a comp decision.

It’s an equity + career-risk decision.

eBay is the safer side: public stock, immediately liquid equity, profitable business, and generally a more established engineering environment. Its growth story also looks healthier than the “legacy tech company” label suggests — Q2 2026 revenue and GMV were both up about 15% YoY.

Whatnot is almost the opposite bet. It reportedly raised at a $20B valuation in August, nearly doubling from $11.5B less than a year earlier. If that trajectory continues, the $700K grant could end up being worth considerably more. But it’s still private-company equity, so valuation on paper and actual liquidity are two different things.

Then there’s WLB. eBay has a reputation for being relatively relaxed and sustainable depending on the org, while recent Whatnot employee feedback describes a much faster-paced environment where long hours and workload can be real tradeoffs.

There’s also an interesting leveling wrinkle: Staff at eBay vs Senior at Whatnot. I wouldn’t automatically assume Staff at eBay is better for the résumé, but if your goal is maintaining Staff-level scope for the next move, that matters.

Curious: Would you take the safer $402K Staff offer, or bet on Whatnot’s $700K equity actually becoming a lot more valuable?

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Saw this Meta E6 Data Engineer offer (shared with Chill Interview)

• Menlo Park, 10 YOE
• Base: $255K
• Bonus: $51K
• Sign-on: $40K
• RSUs: $720K / 4 years
• Year 1 TC: $526K

On comp alone, this looks pretty attractive.

But I keep hearing the same warning from people around Meta’s analytics org: “Don’t join Meta as a DE or DS right now.”

Meta cut around 8,000 employees in the May 20 restructuring, and more cuts were reportedly planned later in the year. Public reporting doesn’t give a clean DE/DS breakdown, but some recently laid-off Meta data scientists have said DS and PM were hit particularly hard compared with SWE.

The other concern is career portability.

Meta DE seems pretty different from the platform-heavy Data Engineer role at many other companies. A lot of the work is closer to product analytics + data modeling + pipelines, built on a very mature and heavily internal Meta stack. Former Meta DEs have described spending much more time on SQL/data pipelines than on the kind of cloud/data-platform engineering you might do with Snowflake, Databricks, Airflow, AWS, etc.

That can be great if you want to stay in Meta’s data ecosystem.

But if you get laid off two years later, does an E6 Meta DE actually have an easy path into Staff-level DE roles elsewhere — or do you suddenly discover that a lot of your experience is very Meta-specific?

So this offer feels like a pretty interesting risk/reward trade: $526K TC and Meta on the résumé, versus joining a function that may have less political protection than SWE and potentially less portable experience.

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Saw this Nuro Senior SWE offer (shared with Chill Interview)

• Bay Area, 8 YOE
• Base: $260K
• Bonus: $31.2K
• Sign-on: $30K Y1 + $30K Y2
• Equity: $550K / 4 years
• Year 1 TC: $541K

What makes this one interesting is that Nuro is basically a different company from a few years ago.

They used to be known for those little autonomous delivery vehicles. That business was eventually scrapped, and Nuro pivoted toward licensing its autonomous driving stack instead.

Now the big bet is the Uber + Lucid robotaxi program.

Lucid provides the Gravity SUV, Nuro provides the self-driving system, and Uber provides the marketplace. Testing is already happening in San Francisco, with public service expected there first and Houston planned for 2027.

That actually seems like a pretty interesting strategy.

Nuro doesn’t need to spend billions building its own car factory or consumer ride-hailing network. If its autonomy stack works, it could theoretically become the “self-driving layer” for multiple automakers and fleets.

But the risk is obvious too: Waymo already has a huge head start, and Nuro still has to prove that this licensing model can become a real scalable business.

Would you treat Nuro equity as a real upside bet now that the Uber/Lucid rollout is getting closer — or still discount it heavily until they actually launch commercially at scale?

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A candidate with 8 YOE recently shared these two Seattle SWE offers with Chill Interview.

Microsoft 64

• $210K base
• $180K RSUs / 4 years
• $31.5K annual bonus
• $286.5K Year 1 TC

Apple ICT4

• $215K base
• $300K RSUs / 4 years
• $21.5K annual bonus
• $311.5K Year 1 TC

Apple wins the immediate comp comparison by about $25K in Year 1. Over four years, ignoring stock movement and future refreshers, Apple is also ahead by roughly $100K.

But the career tradeoff is more interesting.

Microsoft is arguably the stronger AI/cloud career bet right now. For an SWE on the right Azure/AI team, the scope and external marketability could be very strong.

Apple is a different kind of bet: extremely strong consumer ecosystem, hardware/software integration, and Apple is also pushing further into on-device AI and its new Siri stack.

Culture/WLB is probably much more team-dependent than company-dependent here. which would you take?

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