Top 10 Companies Hiring AI Engineers in Bellevue, WA in 2026

Table of Contents

• You Can’t Rank Your Life, But You Can Rank Your Options
• ServiceNow
• T-Mobile
• Expedia Group
• Apple
• Stripe
• Snowflake
• Meta
• Google
• Amazon (including AWS)
• Microsoft
• How to Re-Sort This List for Your Own Life
• Frequently Asked Questions

ServiceNow

Where ServiceNow fits in Bellevue’s AI scene

ServiceNow is the kind of company you don’t notice until you join a big enterprise and realize half the workflows you touch run on its platform. In Bellevue’s AI ecosystem, it’s one of the quieter players compared to the glass-tower logos on I-405, but it sits squarely in the “enterprise backbone” tier. Alongside other large B2B platforms highlighted in analyses of top enterprise AI companies transforming business, ServiceNow is focused less on viral features and more on making sure critical processes in IT, HR, and operations stay reliable while they get smarter.

That’s a very different flavor of work from an ad-ranking team or a consumer chatbot. You’re building AI into systems that Fortune 500 companies use to approve access, track outages, and move money. The stakes are high on governance and uptime, and the feedback loop often comes from service owners and compliance teams rather than end users in an app store review.

What they’re actually building with AI

ServiceNow’s core bet is that enterprise workflows are the perfect substrate for practical AI. On the Now Platform, that shows up in a few concrete ways:

• Generative AI for IT and HR: models that help automate ticket triage, suggest root causes, and draft knowledge articles or employee communications.
• Now Assist: conversational interfaces on top of complex workflows, so employees can reset access, provision services, or kick off approvals by asking instead of clicking through a maze of forms.
• Predictive intelligence: ML systems that classify, route, and prioritize incidents and requests based on historical patterns and real-time context.

Because ServiceNow lives in the “boring but essential” layer of corporate infrastructure, AI work here ends up being deeply tied to reliability, governance, and compliance. You’re closer to messy, multi-tenant enterprise data and audit requirements than to shiny demos, which is exactly what many companies in roundups of serious AI adopters look for when they seek out enterprise-grade solutions.

Roles, stack, and compensation on the Eastside

In and around Bellevue, ServiceNow roles tend to sit in platform engineering or applied AI groups that support global products rather than one-off pilots for a single customer. Engineers often act as “glue people,” pairing tightly with product managers and solution architects who bring in requirements from large enterprises across the Seattle-Bellevue corridor. The typical stack includes Python, Java, and TypeScript on top of the Now Platform, with classical ML and deep learning (often PyTorch or TensorFlow) for classification, ranking, and LLM-powered features, plus a heavy emphasis on MLOps: model lifecycle management, observability, and multi-tenant safety controls at scale.

Those bands are in line with what you see across large enterprise vendors in the area and sit comfortably inside the broader $74k-$273k range that ZipRecruiter tracks for Bellevue AI roles. With Washington’s no state income tax, a $200k base plus bonus and RSUs can yield higher take-home than a similar offer in California or New York. Add in the fact that job boards like Glassdoor routinely list hundreds of AI and ML openings in Bellevue alone, and ServiceNow becomes one of several solid anchors if you want to specialize in enterprise AI without leaving the Eastside.

Who should consider ranking ServiceNow higher

If your personal scoring function gives extra weight to long-term stability, deep MLOps experience, and day-to-day contact with real enterprise constraints, ServiceNow deserves a serious look. It’s a strong fit if you enjoy working at the intersection of AI and structured workflows, want to get very good at safety and governance rather than just pushing model quality, and prefer B2B resilience over consumer growth theatrics. You won’t be chasing the next viral feature, but you will see how AI features survive security reviews, procurement gauntlets, and global rollouts - skills that travel well across the entire enterprise AI market on the Eastside and beyond.

T-Mobile

The hometown carrier with real AI stakes

Walk past T-Mobile’s magenta banners in downtown Bellevue and it’s easy to forget how much math is humming underneath. This isn’t just a carrier selling 5G plans; over the last few years T-Mobile has turned itself into a data-heavy, AI-driven telecom giant. Inside those buildings, models are deciding which customer calls get routed where, how to price device promos, and where the next 5G tower should go so your Zoom doesn’t drop halfway across the I-90 bridge.

Where AI actually lives inside T-Mobile

On the ground, AI at T-Mobile shows up in three big buckets: customer experience, network optimization, and finance/operations. Customer-focused teams work on intelligent routing, churn prediction, and personalization in the T-Mobile app and call centers. Network groups use ML to tune 5G performance, predict congestion, and plan new tower deployments. Corporate functions are getting more explicitly AI-flavored too: roles like a recent Finance Manager - Data and AI posting on Built In Seattle focus on forecasting, risk, and pricing models rather than traditional spreadsheet-only finance. Bellevue-based engineering roles such as a Data & AI Engineer-5, advertised on Indeed’s listing for T-Mobile’s data and AI team, emphasize end-to-end pipelines and large-scale data engineering, showing how tightly ML is woven into the company’s data backbone.

Pay, upside, and when T-Mobile climbs your list

Comp-wise, T-Mobile isn’t trying to outbid the very top of FAANG, but senior AI and data engineers at HQ in Bellevue usually land in the low- to mid-$100k base range, with performance bonuses and solid benefits on top. That sits comfortably within the broader AI salary landscape for the Seattle-Bellevue area, and Washington’s no state income tax quietly tilts the math in your favor compared with similar base pay in places like California or New York. The upside here is less about unicorn equity and more about visibility and scale: your models touch tens of millions of subscribers, and because you’re at headquarters, you’re physically close to the decision-makers whose strategies those models influence.

T-Mobile belongs higher on your personal spreadsheet if you want applied, business-critical AI more than frontier research papers; if you’re drawn to sequence data, time-series, and optimization problems; and if staying rooted in Bellevue matters as much as the brand on your badge. For someone weighting “short commute,” “national-scale impact,” and “hands-on with real network and customer data,” it’s the kind of role that can quietly beat a flashier logo across the lake once you actually sort the rows by what you care about most.

Expedia Group

Travel as a playground for applied ML

Expedia Group sits just across Lake Washington in Seattle, but for a Bellevue engineer it might as well be next door: one bus, one bridge, and you’re in a campus where almost every product decision has a model behind it. Unlike some enterprise players along I-405, Expedia’s work is instantly legible to friends and family - hotels, flights, and vacation rentals - but under the hood it’s a dense tangle of ranking algorithms, pricing models, and NLP systems that turn noisy user intent into booked trips. Travel is emotional, seasonal, and chaotic, which makes it a surprisingly rich playground for an AI career that stays firmly grounded in real user behavior.

Core AI problems at Expedia

Most of the serious AI work at Expedia falls into a few well-defined problem spaces that you’ll see reflected in job titles and teams. There’s personalized travel recommendations, where models rank hotels, flights, and experiences under tight constraints; search and discovery, which has to balance price, location, quality, and relevance; dynamic pricing and revenue management, optimizing margins for both Expedia and its partners; and customer service NLP, where LLMs and traditional NLP power chatbots and agent-assist tools. Roles like a recent Senior Machine Learning Scientist - Search & Recommendations give a good sense of what you’d actually tackle day to day: ranking models, experimentation, and close collaboration with product on search and recommendation quality.

Teams, stack, and experimentation culture

Instead of a single central lab, Expedia’s AI organization is woven through product verticals - Lodging, Air, Packages, and more. Data scientists, ML engineers, and relevance engineers embed directly with feature teams, working mostly in Python with common ML libraries on top of big-data tools like Spark and cloud platforms such as AWS or Azure. The culture is strongly experimental: A/B tests, not slide decks, are the final word on whether a model ships, and you see the full funnel from ad click to booking to loyalty retention. That combination - large-scale data, one focused domain, and a mature experimentation stack - creates a learning environment where you can iterate quickly without giving up on rigor.

Compensation and when to rank Expedia higher

For Bellevue-based talent commuting across the lake, senior ML and data science roles at Expedia typically land in the $140k-$220k base salary range, with bonuses and equity on top, according to compensation ranges for machine learning scientists reported on Glassdoor’s Bellevue ML listings. Washington’s lack of state income tax helps that base go further than it would in many competing hubs. Expedia tends to climb the ranking for engineers who enjoy recommendation systems and pricing optimization, want a product that’s both quantitative and human, and prefer a company that’s big enough to have serious infrastructure but not so massive that they’ll spend all day fighting org charts instead of shipping models.

Apple

On-device intelligence as Apple’s real differentiator

Where a lot of Bellevue’s AI story is about cloud platforms and giant data centers, Apple’s twist is making your phone and laptop feel smarter without feeling creepy. The Seattle-area teams working on Siri, Photos, and AR features are part of a bigger push to keep as much intelligence as possible on the device: smarter photo search, better autocorrect, accessibility features, and increasingly, compact language models that run without sending every keystroke back to a server. Analysts who round up the most influential U.S. AI players consistently slot Apple alongside cloud and search giants, with reports like StartUs Insights’ overview of top American AI companies calling out its work on mobile and edge-based AI as a key part of the landscape.

What the work actually looks like in Seattle-area teams

Most of Apple’s AI-heavy roles are still anchored in Cupertino, but the Seattle-area presence has grown into a network of smaller pods tied to larger orgs: Siri and on-device NLP, computer vision for photos and AR, and privacy-preserving ML research. Engineers here split their time between model development in Python and C++ and product integration in Swift, Objective-C, or C++ on iOS and macOS. The problems feel different from a typical cloud-first role: you’re wrestling with latency, power consumption, and model size as much as with loss curves. Quantization, pruning, and clever architecture choices matter because your model has to run on an iPhone on the bus from Bellevue to downtown Seattle, not just on a fat GPU cluster in a Quincy data center.

Compensation, taxes, and the tradeoffs behind the logo

On the spreadsheet, Apple tends to show up as a high-prestige row with solid, if not always top-of-market, compensation in the Seattle-Bellevue area. Senior ML engineers often see $170k-$220k base salaries, plus bonuses and RSUs. Total comp can be very competitive once equity is in the mix, and Washington’s no state income tax quietly boosts your take-home compared with equivalent Apple roles in California. Reports surveying AI and machine learning employers, such as GreyB’s analysis of leading AI companies, tend to group Apple with other “infrastructure-scale” players, which means the brand will open doors later whether you stay in big tech or jump to a startup.

When Apple moves up your personal ranking

Apple’s Seattle-area AI work climbs the list for people who care more about user experience and privacy than about training the largest possible model. If your personal columns put big weights on “hardware + AI,” “edge constraints,” and “features my non-tech friends actually notice,” the tradeoffs start to look attractive: smaller, more tightly scoped teams; a heavy engineering flavor to the ML work; and the satisfaction of seeing your code ship in operating systems rather than just web dashboards. It might not match a cloud giant on pure compensation or paper-publishing volume, but if you want to become the person who knows how to squeeze real intelligence into a few watts and a handful of megabytes, this is one of the more interesting rows to drag toward the top of your Bellevue spreadsheet.

Stripe

Why Stripe shows up on a Bellevue AI shortlist

For a lot of engineers in Bellevue, Stripe sits in an interesting middle lane: not a cloud giant like the towers you see from I-405, but not a fragile early-stage startup either. It’s a payments infrastructure company that lives and dies on the quality of its decisions - who to trust, what to approve, how to price risk - so AI isn’t a side project, it’s the core nervous system. When reports on hiring trends, like Datamation’s overview of companies ramping up AI roles, talk about “AI moving to the center of the product,” Stripe is exactly the sort of company they mean: every extra basis point of fraud caught, or churn prevented, shows up directly in the revenue line.

What Stripe is actually building with AI

Under the hood, Stripe’s AI work clusters around three big problem families that look boring on a slide and fascinating in a Jupyter notebook: real-time fraud and risk scoring on millions of transactions, revenue optimization across subscriptions and payments, and AI-assisted tools that make Stripe’s developer platform feel “self-explanatory” instead of “read the docs and pray.” Those map roughly to areas like fraud detection and risk scoring, smart retries and pricing suggestions for merchants, and code-generation or API-usage helpers that sit inside IDEs and docs.

Teams, stack, and when Stripe moves up your ranking

Stripe’s Seattle-area presence has grown into a cluster of small, high-leverage teams that often list locations as “Seattle/Bellevue” on job boards. ML engineers here are expected to think like infrastructure builders: you’re as likely to be writing robust backend services in Python, Scala, or Ruby as you are to be tuning a PyTorch model or XGBoost ensemble. Real-time inference, careful API design, and tight feedback loops with product are the norm. Senior ML-heavy roles in this market commonly land in the $180k-$230k base range with substantial equity, and because Washington has no state income tax, the effective value of those RSUs can look very different than the same numbers on a Bay Area offer. Stripe tends to rise toward the top of the spreadsheet for people who love probabilistic thinking and real-time systems, want startup-like pace with big-company stability, and care about developer experience as a first-class product rather than an afterthought.

Snowflake

AI where the data already lives

On that imaginary spreadsheet of Bellevue AI options, Snowflake is the row labeled “platform, not app.” Instead of dragging data out to bespoke ML stacks, Snowflake’s bet is to bring AI into the data cloud itself. The company’s Data Cloud is already where a lot of enterprise analytics work happens; the current push is to make it a place where you can also generate embeddings, run vector search, call LLMs, and orchestrate agentic workflows without shipping terabytes across yet another network boundary. For Eastside engineers who like infrastructure and performance as much as loss curves, that’s a very different proposition from working on a single consumer product.

What Snowflake is building in Bellevue

Snowflake’s Bellevue office has become a key hub for both core engine work and new AI capabilities. On the product side, there’s the push around features often bundled under names like Snowflake Cortex and AI services: built-in access to LLMs, embeddings, and vector search directly inside the Data Cloud. On the research and platform side, you see roles like an AI Agent Researcher position in Bellevue on Snowflake’s careers page, focused on agentic workflows that can autonomously compose queries, transform data, and trigger downstream actions. Underneath that sits a lot of plumbing: distributed C++ and Java in the core engine, with Python and popular ML frameworks layered on top to expose AI experiences to data engineers and analysts who already live in SQL.

Compensation, taxes, and when Snowflake rises to the top

Snowflake pays at or above big-tech levels for core AI roles, with Bellevue ML platform and research engineers typically seeing base salaries in the $160k-$230k range, plus equity and bonus according to ranges shown in recent Snowflake postings for the region. Because those RSUs vest in Washington, the state’s no income tax policy means more of that equity actually lands in your bank account than it would in a similar Bay Area offer. Snowflake tends to climb your personal ranking if your columns put serious weight on “infrastructure + AI,” “enterprise leverage,” and “chance to work on agents and retrieval-augmented generation grounded in real customer data.” You’re not optimizing a single app’s funnel; you’re building the primitives thousands of other teams will treat as their starting point.

Meta

Massive recommenders and open-source gravity

Meta is one of the places where AI is the product, not just a feature bolted on later. Feeds, stories, Reels, and ads across Facebook, Instagram, and WhatsApp are all driven by large-scale recommendation systems; content understanding and moderation lean heavily on computer vision and NLP; and Reality Labs adds another layer with AR/VR perception and interaction. On top of that, Meta has become a gravitational center for open-source AI with frameworks like PyTorch and model families such as Llama that other companies build on. Even with some turbulence, including layoffs affecting 331 workers in Washington - including in Bellevue’s Spring District - documented in GeekWire’s coverage of Meta’s Washington cuts, AI remains central to how the company makes money and ships new experiences.

Teams, stack, and what the work feels like

In Bellevue, you’ll typically find ML-focused software engineers embedded in product teams (e.g., Instagram Explore, Feed, or Ads), research scientists working on foundation models or AR/VR-adjacent problems, and data scientists specializing in experimentation and causal inference. The stack is what you’d expect from a company that built much of the modern deep-learning tooling: PyTorch, Python, and C++ on top of very large-scale training and inference infrastructure. Work ranges from improving click-through and watch-time in recommender systems, to building models that can understand multimodal content, to collaborating with FAIR-style research groups to bring new architectures into production.

Comp, volatility, and when Meta deserves a high rank

Compared with Eastside peers, Meta’s AI-heavy roles typically offer top-tier total compensation, with senior engineers and scientists often seeing base pay north of the mid-$100k range and significant RSU grants layered on top. The flip side is volatility: reorganizations like the recent Reality Labs cuts can shuffle priorities quickly, and teams may pivot from one strategic bet to another faster than at more enterprise-focused companies. Meta tends to climb toward the top of a Bellevue engineer’s spreadsheet when you weight “massive scale,” “open-source impact,” and “pace of learning” heavily, and you’re comfortable trading some stability for the chance to work on feeds, ads, and models that quietly shape how billions of people experience the internet every day.

Google

Search, Gemini, and Cloud AI from just across the bridge

If you’re sitting in a Bellevue apartment staring at your spreadsheet of offers, Google usually shows up as one of the “brand name” rows you can see all the way from the I-90 bridge. On the Eastside, that presence is concentrated in Kirkland and Bellevue offices where AI isn’t just a feature add-on: it’s the backbone of search, ads, and the newer Gemini model family that’s being threaded through Google Cloud and productivity tools. Industry roundups like Built In’s list of notable machine learning companies consistently put Google in the core of the AI landscape because its research and infra quietly power workloads for everyone from indie devs to Fortune 500s.

What Google’s Eastside AI teams actually do

On this side of the lake, a big chunk of the work centers on Google Cloud AI and Vertex AI: tools for training, deploying, and monitoring ML and generative models for enterprise customers. You’ll also find teams working on search and recommendation systems (ranking, ads quality, personalization) and on Gemini integration, baking large language models into Cloud products and developer workflows. The stack is what you’d expect: TensorFlow and JAX for modeling, Python and C++ for core services, TPUs and internal distributed training tools under the hood. ML engineers and research scientists are embedded directly into product groups, with infra teams building the platforms that everyone else stands on.

Compensation, tax math, and how it compares on paper

Those bands, drawn from historical Google Cloud AI compensation data for the region, land at the very top of most Eastside spreadsheets. Because they’re paid in Washington, the state’s no income tax policy means that an upper-$200k base plus equity translates into meaningfully higher take-home than a nominally similar offer in California or New York. Add in the fact that Google shows up in lists like Mor Software’s roundup of AI-heavy employers, and you get a mix of strong pay, resume signal, and long-term mobility across the AI ecosystem.

When Google should float toward the top of your sheet

Google tends to rise toward the top of a Bellevue engineer’s ranking when your personal columns put big weights on “research-grade infra,” “developer platforms,” and “billions of users, even if they never know my name.” It’s a fit if you like building tools other engineers rely on, want to sit close to the line where new architectures move from papers into production, and don’t mind trading a slightly longer commute for the chance to work on Vertex AI or Gemini-powered products. If your sheet is weighted more toward scrappy startup culture or hyper-local Bellevue offices, it may slide a few rows down - but for many people, that combination of scale, comp, and Cloud AI focus makes Google one of the most strategically valuable cells on the Eastside grid.

Amazon (including AWS)

The tower you can see from your kitchen table

From a tiny Bellevue kitchen table, you can literally see a chunk of Amazon’s AI story out the window: new glass towers going up downtown, the glow from Seattle’s South Lake Union across Lake Washington, badges on the bus that say “AWS” and “Prime Video.” For a lot of Eastside engineers, Amazon (and AWS in particular) is the default row at the top of the spreadsheet: enormous scale, familiar brand, and a footprint that keeps expanding east of the lake as teams shift into Bellevue and Redmond.

What Amazon is actually doing with AI

It’s easy to think “recommendations” and stop there, but AI at Amazon now threads through almost every business line. Inside AWS, there are generative AI and AGI initiatives building foundational models and customization platforms out of internal “Innovation Centers.” The retail side leans on e-commerce intelligence for search ranking, recommendations, personalization, and fraud detection on Amazon.com. Operations teams push into logistics and robotics with route optimization, warehouse robotics, and computer vision for safety and quality. Then there’s Alexa and device intelligence, which means speech, NLP, and on-device inference work that looks very different from pure cloud infra. A recent local business report noted that Amazon’s Eastside presence has been growing fast, with its Redmond headcount surging by triple digits as part of a broader investment in the region’s tech workforce, according to Seattle Business Journal coverage of Microsoft and Amazon’s expanding Eastside headcounts.

Roles, stack, and how Bellevue fits into the map

Bellevue has quietly become a major secondary hub for Amazon, with a mix of AWS, retail, and corporate teams. On the AI side, that translates into Applied Scientists and ML Engineers in AGI-adjacent AWS groups, SDEs with ML focus working on personalization, ads, and logistics, and robotics/computer-vision engineers tied into Amazon Robotics. The stack looks like what you’d expect from a company selling cloud to everyone else: Python, Java, and C++ on top of the AWS ecosystem (SageMaker, EC2, S3, internal experimentation platforms), plus a blend of deep learning for NLP/CV and large-scale “classical” methods for ranking and forecasting. Teams are famously decentralized; each org feels like its own company sharing a common platform and set of metrics.

Compensation, taxes, and when Amazon belongs near the top

On paper, Amazon’s AI-heavy roles in the Seattle-Bellevue market are highly competitive. Typical base salary bands look roughly like this for the core levels where many ML engineers and applied scientists land:

Those numbers sit on top of substantial RSU packages, and because they’re paid in Washington, the state’s no income tax means a $200k+ base plus equity goes further than it would for an equivalent role in California or New York. Amazon tends to rise toward the top of a Bellevue engineer’s ranking when your columns are weighted heavily toward “scale,” “breadth of domains,” and “cloud + product experience.” If you’re comfortable in a metrics-driven, fast-moving culture and like the idea of rotating between domains - e-commerce, logistics, AGI platforms, Alexa - over a multi-year stint, it’s one of the most flexible and financially strong rows on the Eastside spreadsheet.

Microsoft

Microsoft as the Eastside’s AI backbone

From most Bellevue balconies you can see Redmond’s campus glow on the horizon, and that’s not just office lights - it’s where a huge chunk of the region’s AI capacity sits. Inside Microsoft, AI has gone from “interesting feature” to organizing principle: Copilot threading through Microsoft 365, GitHub, Dynamics, and Windows; Azure AI hosting and fine-tuning frontier-scale models; and applied AI quietly rewiring how meetings, email, and documents work for hundreds of millions of people. Local business reporting has highlighted how aggressively Microsoft has been staffing up on the Eastside, with its Redmond headcount climbing to tens of thousands of employees after double-digit growth in a single year - a signal of how central the region has become to its AI push.

Roles, teams, and the Copilot stack

On the ground, Bellevue/Redmond engineers plug into a dense grid of AI-heavy teams. You see Applied Scientists and ML Engineers in Office and Teams building Copilot features; GitHub engineers working on code completion and developer experience; Azure AI platform teams owning training, inference, and safety tooling; and research-adjacent roles that pair directly with Microsoft Research on foundation models and new architectures. The stack blends cloud and client: Azure AI and internal ML platforms for serving, PyTorch and TensorFlow for modeling, and a lot of C#, C++, and Python to glue models into productivity apps that ship on Windows and the web. That mix - infra, models, and user-facing features - is part of why Microsoft shows up as a core anchor in regional rundowns of top Bellevue AI development companies and clients on Clutch, with many local consultancies and startups orbiting its ecosystem.

Compensation, taxes, and the local network effect

On a spreadsheet, Microsoft tends to look strong but not absurd: very competitive base pay, meaningful equity, and a lot of internal mobility across AI domains. Historical compensation data for the Seattle-Bellevue market shows ranges like these for AI-heavy roles:

Because that income lands in Washington, the state’s no income tax quietly bumps your effective take-home compared with similar nominal salaries in California or New York. The less obvious upside is the network: many of Bellevue’s AI startups - from communication-coaching tools like Yoodli to mental-health platforms like Aiberry and edge-computing players like Armada and Curative AI - are founded or staffed by Microsoft alumni. That makes a Copilot or Azure AI stint more than just a line on the résumé; it’s a front-row seat to the ecosystem you’ll be choosing from the next time you’re hunched over that color-coded spreadsheet, dragging rows up and down to match the life you actually want.

How to Re-Sort This List for Your Own Life

See the list as a model, not a verdict

By the time you hit the end of any “Top 10” article, it’s tempting to treat it like the final ranking on your color-coded spreadsheet: Microsoft in one row, Amazon in another, Snowflake and Stripe and T-Mobile stacked underneath like apartments with different rents and floorplans. But a list like this is closer to a pre-trained model than to ground truth. The features were chosen by someone else (research depth, Eastside presence, compensation, learning environment), and the weights are tuned to their loss function, not yours.

That’s why experienced folks keep repeating a version of the same advice in career threads and talks: use rankings as a starting point, not a destination. Commentators dissecting AI careers on platforms like Medium’s long-form pieces on whether becoming an AI engineer is a smart move make the same point in different words: the “right” choice depends less on which company tops someone’s list and more on how that role lines up with your skills, risk tolerance, and the life you’re trying to build in a place like Bellevue.

Choose your own columns

So instead of asking, “Is Microsoft really #1? Is ServiceNow really #10?” it’s more useful to ask, “What columns am I missing from this sheet?” The article gave you a head start: depth of AI work, local presence, comp (amplified by no state income tax), learning environment, and real-world impact. Now you add the unquantified stuff you noticed on onsite visits and Zoom calls: did the team light up when they talked about their roadmap, how did they handle questions about on-call and burnout, what did the Bellevue commute actually feel like at 8:30 a.m. on I-405?

Let Bellevue’s realities tweak your weights

The other piece your spreadsheet can’t ignore is geography. Bellevue isn’t just a dot on a map; it’s a specific ecosystem where Microsoft, Amazon, Google, and Meta sit within a few freeway exits of AI startups like Armada, Curative AI, Yoodli, and Aiberry. Videos aimed at new grads - like the “become an AI engineer” breakdowns on channels such as Turing College’s guide to what to learn and what to skip as an AI engineer - tend to talk about skills in the abstract. But standing on a Bellevue street corner, you can see how those skills plug into very different day-to-day lives: late nights in a Redmond lab pushing Copilot, shipping fraud models from a downtown tower, or walking to a seed-stage office above a cafe.

The move that matters isn’t copying this article’s ordering; it’s taking ownership of the re-sort button. Maybe you drag “research prestige” higher and “short commute” lower and end up at Google in Kirkland. Maybe you crank up “healthcare impact” and “startup equity” and find yourself interviewing at a mental-health AI company instead of another cloud giant. The list is still useful - not as a verdict, but as a structured way to see your options. Once you start changing the columns’ widths and dragging rows around until one of them finally feels right, you’re not just ranking jobs anymore. You’re designing the version of your life in Bellevue that makes sense for the next few years, glow of the skyline and all.

Frequently Asked Questions