Top 10 AI Startups to Watch in Bellevue, WA in 2026

Table of Contents

• From Farmers Market Chalkboards to AI League Tables
• Statsig
• Summation
• Auger
• SeekOut
• CityBldr
• TerraClear
• Centific
• Sportsbox AI
• Aiberry
• MangoBoost
• Reading the Chalkboard - and the Orchard
• Frequently Asked Questions

Statsig

The Experimentation Engine That Put Bellevue on the Map

Statsig is the rare Bellevue startup that went from “quiet infra tool” to global signal overnight when OpenAI acquired it for $1.1 billion in late 2025. That exit shows up in almost every regional ranking now; for example, Seedtable’s 2026 Bellevue startup list calls out the deal as a watershed moment for the Eastside. The company’s core bet was simple but brutal: as products get more AI-heavy, guessing with your roadmap becomes untenable. Teams need to know, with data, whether a new recommendation model, pricing algorithm, or LLM-powered feature actually moves the needle on engagement, revenue, and safety.

The Problem and Product: Moving Beyond Gut-Driven Shipping

Most product orgs still rely on basic A/B tests duct-taped to legacy analytics, which buckle when you’re running dozens of experiments across millions of users. Statsig attacked that by offering a full-stack experimentation and product analytics platform: feature flags and staged rollouts, automated A/B and multivariate testing at massive scale, and real-time metrics pipelines tuned for event-driven apps. Crucially for AI teams, it closes the loop between model changes and business outcomes: you can ship a new ranking model to 5% of users, watch churn, revenue, latency, and custom safety metrics in near real time, and let pre-set guardrails decide whether to ramp up or roll back. That “faster validation and learning” loop mirrors what Ash Maurya describes in his talk on how the best startups use AI for rapid experimentation in 2026, and it’s become table stakes for serious AI product teams.

Why Statsig Stood Out in a Crowded Tools Market

What made Statsig different wasn’t a pretty dashboard; it was infrastructure depth. This is hard-core experimentation plumbing that has to be correct under heavy load, which is why it resonated with customers like Microsoft and OpenAI long before the acquisition. In a region that leans enterprise-first, that fit matters: the platform lines up perfectly with the Eastside’s bias toward measurable impact over hype. Compared to generic analytics tools, Statsig baked in fine-grained cohorting, complex experiment designs, and tight controls around rollouts, which are exactly the knobs AI teams need when shipping features that can both delight users and accidentally tank key metrics if you’re not careful.

What to Watch Next If You’re Betting Your Career on AI Infra

Post-acquisition, the open question is how deeply Statsig’s ideas get woven into the way developers ship on OpenAI’s platform. If experimentation primitives become a default part of that stack, it effectively exports a Bellevue-born way of working to thousands of teams worldwide. Locally, the other thing to watch is how much of the product and engineering footprint stays - and grows - on the Eastside. When a tools company proves it can reach a billion-dollar outcome without decamping to the Bay, it raises the ceiling for every infra-heavy AI startup in Bellevue and Redmond that’s trying to do something similar, and it gives ML engineers here a clear signal that specializing in experimentation and metrics can be just as career-defining as working directly on models.

Summation

The CFO’s New Favorite Kind of AI

Walk into pretty much any mid-market or enterprise finance team on the Eastside and you’ll see the same thing: people buried in CSV exports from three different ERPs, stale dashboards, and a forest of spreadsheet models that only one analyst truly understands. Summation is going after that mess directly with a “decision-grade” AI platform that plugs into ERPs, CRMs, billing systems, and more to answer the questions executives actually ask in real time.

• “What’s our true gross margin by product line, right now?”
• “How did last month’s pricing change play out by region?”
• “Where are we most at risk of missing the quarter?”

Instead of waiting days or weeks for a special report, finance and ops leaders can ask those questions in natural language and get back answers that come with lineage, versioning, and explanations they can audit.

From Fragmented Systems to a Unified Semantic Layer

Under the hood, Summation builds a unified semantic layer across all those scattered systems so the AI isn’t guessing what “revenue” or “churn” mean for your business. Its agents sit on top of that layer to deliver trusted, real-time business answers, a pattern that’s helped land the company on Built In’s list of Bellevue AI startups to watch. For CFOs, the big unlock is that they don’t have to trade speed for control: they still see where numbers come from and can drill down when something looks off.

Why VCs See This as “Core Infrastructure,” Not a Feature

Investors have started to treat this category as plumbing, not a nice-to-have. Summation’s $21 million funding round led by Kleiner Perkins signals that this isn’t just another analytics layer; it’s a potential system-of-record for how CFOs and COOs make calls. Greater Seattle Partners, in its overview of AI startup funding across the region, points out that capital is concentrating around vertical AI plays that sit close to revenue and cost decisions - exactly where Summation lives. In practice, that deep systems integration and governance focus make it a natural fit for Bellevue’s enterprise-heavy mix of SaaS, cloud, and B2B companies.

What to Watch in 2026 If You’re Finance- or Infra-Curious

This is a company in that “baking apple” phase of the orchard: not the flashiest fruit at the stall, but central to the recipe. Three arcs are worth tracking from the Eastside: whether Summation jumps from finance into adjacent functions like supply chain or revenue operations; how tightly it partners with cloud mainstays like Microsoft Dynamics or SAP; and whether it can turn that Series A into a repeatable go-to-market motion instead of a handful of lighthouse wins. If you’re an ML engineer who likes structured data, complex schemas, and very measurable impact, this is the kind of AI work - right at the finance and ops nerve center - that tends to compound into serious career capital in Bellevue.

Auger

Automation for the Boring, Regulated, and Very Expensive Stuff

While a lot of generative AI startups on the Eastside chase chatbots and copilots, Auger is pointed squarely at the unglamorous core of big enterprises: insurance workflows, healthcare claims, underwriting, compliance-heavy back offices. Its pitch is that off-the-shelf LLMs can draft a nice email, but they can’t reliably orchestrate a multi-step claims process across legacy systems, stay inside domain-specific regulations, and leave behind an audit trail that keeps regulators calm. Emerging in the 2025-2026 window with a massive $100 million funding round led by fintech and healthcare specialist Oak HC/FT, Auger is deliberately starting where the constraints are toughest and the budgets are largest.

From Chatbots to Orchestrated Workflows

The gap Auger is attacking shows up every time a bank or health system tries to “AI-ify” an existing process and hits a wall. They’re stuck with long-lived line-of-business systems, strict audit requirements, and fragmented processes that blend documents, calls, and manual approvals. A generic LLM endpoint can’t see all of that, let alone coordinate it. Auger’s platform layers domain-tuned LLMs on top of workflow orchestration, connectors into core systems, and built-in guardrails like robust logging and human-in-the-loop review so you can automate work without losing control.

Why a $100M Bet Makes Sense from Bellevue

On paper, a nine-figure round for a still-young company looks aggressive. In context, it lines up with broader trends: investment across Washington’s AI scene is concentrating in life sciences and enterprise SaaS, with the Washington Technology Industry Association noting in its AI landscape report that regulated and data-heavy sectors are drawing outsized attention. Auger sits right at that intersection. Being anchored near Azure and AWS teams on the Eastside also helps; the company can hire people who understand both hyperscale infrastructure and the security models big banks and payers expect. For Oak HC/FT, which has a long track record in healthcare and fintech, this looks less like a moonshot and more like building the next layer of core enterprise plumbing.

Signals to Watch If You Want to Work on “Serious” AI

For engineers and data folks in Bellevue wondering whether this is a good tree to climb, a few signals matter. First, flagship customers: if Auger starts closing visible deals with banks, insurers, or healthcare payers, that’s proof its compliance story holds up. Second, hyperscaler relationships: showing up in reference architectures or partner programs from cloud providers that already dominate the corridor would validate its platform approach. Finally, exit trajectory: with this level of capitalization, Auger is either on a path to IPO or to becoming a strategic acquisition for a global systems integrator or cloud provider, a pattern you can see across other large AI rounds tracked in recent AI startup funding reports. If you care more about hard enterprise problems than shiny demos, this is the kind of “orchard-scale” bet that can define a whole chapter of your career.

SeekOut

The Talent Intelligence Layer Under Bellevue’s AI Boom

When people talk about AI in Bellevue, they usually start with models and infrastructure, but the real bottleneck most companies feel first is talent. That’s the gap SeekOut stepped into early: giving employers a 360-degree view of both the external market and their own workforce so they can actually see who has which skills, who could be reskilled, and where the real gaps are. Founded by former Microsoft leaders, SeekOut has grown into a unicorn serving numerous Fortune 500s, and it’s become a kind of talent-intelligence grid under the Eastside’s AI scene, quietly influencing who ends up where at companies from downtown Bellevue to Redmond.

From Sourcing Tool to Workforce Operating System

SeekOut started out best known as a power tool for external recruiting, surfacing candidates from public data sources that traditional ATS or LinkedIn-only workflows would miss. By 2025-2026, it had expanded into internal talent mobility, helping companies benchmark internal skills against the market and find employees who can move into AI and ML roles with targeted upskilling instead of expensive external hires. That evolution - from point solution to what feels more like an “operating system” for workforce planning - is why it shows up so consistently in regional lists of top AI startups and in overviews like the AI-powered reboot of the GeekWire 200 ranking of Seattle-area startups.

How Rankings and IPO Talk Translate into Career Bets

Because it’s already at unicorn scale with backing from Seattle stalwarts like Madrona, SeekOut shows up a lot in IPO speculation and ecosystem write-ups. For you, as someone aiming at an AI or ML career, the more practical angle is how central this kind of platform is becoming: as AI rewrites job families and salary bands across the Seattle-Bellevue corridor, the companies that control the visibility into skills and potential control a surprising amount of downstream opportunity. In a no-state-income-tax environment where senior AI and cloud engineers already see very strong total comp, a place like SeekOut can be a high-leverage “tasting sample” in your own orchard tour - a way to work on applied ML that directly shapes who gets hired, promoted, or retrained into the next wave of AI roles on the Eastside.

CityBldr

In a place like Bellevue, where cranes keep edging closer to light rail stops and surface parking lots mysteriously sprout high-rises, the real question isn’t just “what’s getting built?” but “why here, and not across the street?” CityBldr is the AI layer trying to answer that, parcel by parcel, by treating land like a data problem instead of a slow back-of-the-envelope exercise.

Turning Parcels into Data, Not Just Addresses

At its core, CityBldr ingests zoning codes, parcel maps, transaction histories, and a mess of local regulations, then uses machine learning to spot underutilized sites and model what could be built there. It’s often described as a kind of “Bloomberg for real estate” because it gives developers, cities, and investors a single interface to test scenarios: add density near a new transit station, convert an aging office into mixed-use, or prioritize affordable units over luxury inventory. The platform is already live in over 155 cities across the U.S. and Canada, which means a downtown Bellevue parcel is being evaluated with the same rigor as one in other fast-growing metros listed in tools like the F6S index of AI companies in Bellevue.

Why This Matters in a Transit-Shifted, High-Cost Market

Bellevue is feeling simultaneous pressure for more housing near jobs, office-to-residential debates, and the long shadow of regional affordability gaps. CityBldr’s ESG-focused division leans into that by flagging sites with strong affordable-housing potential and aligning them with capital that cares about environmental and social outcomes. That’s not just nice branding; it syncs with how regional groups like Greater Seattle Partners describe investors increasingly looking for tech that can bridge growth and livability in their surveys of AI-backed startups and funding flows.

Career-Wise, This Is AI That Literally Redraws the Map

For someone aiming at an AI or ML role, CityBldr is a different flavor of work than a SaaS dashboard: models have to encode messy zoning rules, infer redevelopment potential, and reflect community priorities, not just click-through rates. If you care about the physical city as much as the digital one, this is a way to have your code show up as new buildings along the East Link line or more mixed-income housing within walking distance of Bellevue’s offices. In the orchard of local AI companies, CityBldr is one of the clearest examples of algorithms shaping not just balance sheets, but the literal skyline you bike past on your commute.

TerraClear

Robots in the Fields, Ten Miles from the Towers

Drive ten miles out from Bellevue’s glass and steel and you hit a different kind of infrastructure problem: rocks. Not metaphorical ones, literal ones - dense, equipment-killing stones scattered across fields that farmers have to clear before they can even think about planting. TerraClear, led by Brent Frei (co-founder of Smartsheet and Onyx Software), is building AI-powered robotics to take that work off human backs and put it onto machines that can see, map, and remove rocks at scale.

The Backbreaking, Low-Margin Problem

Farmers face a nasty combination of labor shortages, rising equipment costs, and pressure to increase yields without trashing their soil. Rock picking sits right at that intersection: it’s tedious, seasonal, and expensive, but skipping it risks catastrophic damage to tractors and implements. For the mid-size operations that don’t have endless capital, every broken tine or bent axle eats straight into thin margins. That’s the wedge TerraClear picked - an unsexy job that happens on almost every farm, every year.

Computer Vision + Robotics Instead of Bent Axles

TerraClear’s system combines aerial sensing (often via drones), computer vision models that detect and classify rocks in those images, high-accuracy GPS, and custom hardware that can autonomously or semi-autonomously remove them. Madrona Venture Group backed the company early, a detail called out in regional investment overviews like the Seattle-area startups to watch in 2026, because this is real edge AI: models running in harsh outdoor conditions, tied directly to heavy machinery and measurable ROI.

Why This Edge AI Matters for the Eastside - and for Your Career

On paper, this looks nothing like a Bellevue enterprise SaaS play, but that’s the point: TerraClear is proof that the region’s AI talent isn’t confined to dashboards and copilots. It’s part of a broader wave of robotics and applied ML that shows up alongside more traditional software in lists of top AI companies in Washington. If you’re an ML engineer who likes dirty data, hard latency constraints, and problems where success is measured in fewer broken parts and higher yields, this is a very different “variety” in the local AI orchard. You’re not just optimizing clicks; you’re helping decide whether the cost of food production inches up or down over the next decade - and doing it from a base on the Eastside that still lets you bike to a downtown Bellevue office when you’re not out in the fields.

Centific

From AI Pilots to Something That Actually Ships

Across traditional enterprises on the Eastside, there’s a familiar pattern: a team spins up a promising ML proof-of-concept in a notebook, gets a small win, and then… nothing. The model never quite makes it into production, data pipelines stay brittle, and every new use case feels like starting from scratch. Centific, based in Redmond, is trying to break that cycle with an end-to-end platform that wraps data engineering, MLOps, and domain-specific accelerators into something large organizations can actually deploy at scale.

What Centific Puts on the Table

Instead of selling just tooling or just consulting, Centific positions itself as an enterprise-grade AI and data platform for sectors like retail and complex information systems. Its offering spans data and database management, model training and deployment workflows, and pre-built analytics components for common industry problems. That “ready-to-deploy” posture is why it shows up in statewide roundups of leading AI providers, including Clutch’s rankings of top artificial intelligence firms in the Bellevue area, even though its headquarters sit next door in Redmond.

Why a $60M Round Changes the Conversation

In late 2025, Centific secured a $60 million funding round, giving it the runway to invest simultaneously in platform depth and go-to-market rather than choosing between them. That places it in the same capital tier as other serious AI infrastructure bets highlighted in recent AI startup funding trackers, and it matters because enterprise buyers increasingly want partners that will still be around in five years. For Eastside companies wrestling with how to standardize data and ML deployment across dozens of teams, a well-capitalized platform provider with local talent access starts to look less like a vendor and more like a core part of their stack.

Product vs. Services - and What It Means for Your Career

The big strategic question for Centific in 2026 is how far it can lean into product without becoming “just another” services integrator. If it can codify repeatable blueprints - retail AI-in-a-box, predictive maintenance stacks, cross-channel personalization - it has a shot at being the default way traditional enterprises in Washington operationalize ML. For you as an engineer or data scientist, that translates into a different flavor of work than joining a single-brand retailer or a pure consultancy: you’re building reusable infrastructure that will be deployed across many customers, but doing it from inside a company that still speaks fluently with CxOs and line-of-business owners. In a region where there’s no state income tax and a dense network of cloud-native enterprises to sell into, that’s a compelling niche to specialize in if you like turning “we have a POC” into “this runs everywhere, all the time.”

Sportsbox AI

On the surface, Sportsbox AI looks like a golf app, but under the hood it’s one of the more interesting computer vision labs on the Eastside. The company turns a standard smartphone video into a full 3D model of your swing, then uses biomechanics and AI to give feedback that would normally require a high-end motion-capture studio and an elite coach standing next to you. For a region full of weekend golfers and serious athletes, it’s a very Bellevue way to apply AI: take a pro-only tool, compress it into your phone, and make it usable between meetings.

The Coaching Bottleneck Sportsbox Is Attacking

High-quality coaching doesn’t scale well. Good instructors are expensive, booked solid, and limited to whoever can physically get to their range, court, or studio. Video tools help, but most either drown you in complex overlays or reduce everything to simple before/after clips. Sportsbox AI targets that gap by using 3D motion capture derived from 2D video to extract joint angles, velocities, sequencing, and other biomechanical metrics, then pairing that with correction cues informed by world-class instructors. The result is feedback that feels like a coach freezing your motion, drawing lines, and explaining what to change - but delivered on demand, wherever you train.

How It Compares to Traditional Coaching and Old-School Video Tools

Why This Belongs in the Bellevue AI Orchard

Sportsbox AI sits at a useful intersection for the local ecosystem: serious computer vision and model optimization work, but applied to something very human and very visual. It has gained traction by combining its AI with the expertise of world-leading instructors, making sure the feedback is coach-approved rather than purely algorithmic. That’s a pattern you see across the region in overviews of the top AI development companies in Washington: the most durable plays pair deep ML with domain experts who know what “good” actually looks like. For an ML engineer or data scientist, this means working on pose estimation, multi-view geometry, and real-time inference on mobile - not just web dashboards - while still being able to commute from a Bellevue apartment instead of a remote field site.

What to Watch in 2026 If You Care About Applied Vision

The roadmap to pay attention to is less about pure user counts and more about where the motion stack shows up next. Expect pushes beyond golf into sports like tennis and baseball, where swing and serve mechanics are equally measurable, as well as into general fitness and potentially physical therapy and workplace ergonomics. On the business side, B2B deals with gyms, training academies, and wellness platforms could turn the tech into a recurring SaaS layer rather than just a consumer subscription. And down the line, deeper integrations with wearables or AR glasses would make the feedback loop even tighter. If you want to work on computer vision that doesn’t live in a lab but in someone’s backswing on a rainy Bellevue driving range, this is one of the more interesting trees to climb.

Aiberry

In the middle of Bellevue’s productivity-obsessed AI scene, Aiberry is pointed at something much less quant-y and much harder to scale: mental health screening. Health systems are short on clinicians, primary-care visits are rushed, and traditional tools lean heavily on self-report questionnaires that miss nuance and can reinforce existing biases. Aiberry’s bet is that a carefully designed, clinician-in-the-loop AI can triage and surface risk signals faster and more consistently, without trying to replace the humans who ultimately make the call.

What Multimodal Screening Actually Means

Instead of just analyzing text or survey answers, Aiberry’s platform looks at multiple channels at once: voice, facial expressions, and speech content. The system processes how someone talks (prosody, pauses, tone), what they say (language patterns), and how their face moves (micro-expressions and affect) to flag indicators that may warrant closer attention from a clinician. In regional AI company roundups like Built In’s profile of Bellevue AI startups, Aiberry is highlighted for using this kind of multimodal pipeline to improve screening efficiency while deliberately working to reduce diagnostic bias across demographic groups.

How It Compares to Traditional and “Pure Chatbot” Approaches

Why Health Systems - and the Ecosystem - Care

For hospitals, universities, and clinics, the appeal is straightforward: an AI assistant that can run in the background and flag at-risk patients means scarce therapist and psychiatrist time is focused where it’s most needed. That fits the broader pattern called out in statewide AI surveys, where healthcare is one of the top verticals drawing AI investment and experimentation, but only when tools are framed as decision support rather than standalone diagnostics. Aiberry’s emphasis on explainable signals and a clear audit trail gives it a better shot at fitting into real-world care pathways and payer expectations than consumer-first wellness bots.

What This Looks Like as an AI/ML Career Path

Working on Aiberry-style problems is a different texture than building recommender systems or growth analytics. You’re dealing with noisy, sensitive multimodal data, complex evaluation metrics, and a regulatory environment that demands robustness over raw speed. For ML engineers and data scientists in Bellevue who want their work to touch something more human than click-through rates, this is a compelling branch of the local AI orchard: you still get to collaborate with cloud and infrastructure experts, but your models end up in clinics and counseling centers instead of just dashboards, and the success criteria are closer to “early intervention and reduced disparities” than “more ad impressions.”

MangoBoost

Under all the friendly chatbots and sleek dashboards in Bellevue’s AI scene, there’s a much more physical problem: data centers running hot, network links saturated, and power bills that make CFOs squint. MangoBoost lives down at that layer, building specialized data processing units (DPUs) and AI-focused chips to offload the unglamorous work of shuttling data, encrypting traffic, and juggling storage so GPUs and CPUs can focus on the actual models.

Why the Data Center Is the Real Bottleneck

As companies on the Eastside move from AI pilots to production workloads, they run into constraints that no amount of prompt engineering can fix: GPU clusters pinned at 100%, network and storage systems that weren’t designed for today’s volume of embeddings and vector searches, and tight power and cooling envelopes. General-purpose CPUs and even GPUs are not optimized for all of that “glue” work. That’s the gap MangoBoost is targeting with DPUs that handle networking, security, and data movement more efficiently, echoing the broader trend described by CTech that “everyone is obsessed with the AI brain, but there’s a whole body that needs to be built around it.”

MangoBoost’s Place in the Eastside Stack

MangoBoost has raised significant capital from backers including Premier Partners to push this hardware-first approach, which is unusual in an ecosystem better known for SaaS and cloud services. In the Washington Technology Industry Association’s statewide AI landscape, infrastructure and tooling companies are highlighted as critical leverage points, and MangoBoost sits right in that category: if it succeeds, every hyperscaler, enterprise, and AI startup running serious workloads in the region can get more out of each rack. The fact that cloud giants like Microsoft and Amazon are just down the road gives MangoBoost direct access to the customers with the gnarliest scaling problems and the budgets to match.

“Everyone is obsessed with the AI brain, but there’s a whole body that needs to be built around it.” - CTech analysis, Calcalist’s technology desk

Career-Wise, This Is AI Work Below the Model Line

For ML and systems engineers in Bellevue, MangoBoost represents a different kind of bet than joining an LLM app or an applied-ML startup. You’re working on PCIe lanes, NICs, and firmware as much as on Python, and your success is measured in microseconds and watts instead of engagement curves. But the leverage is huge: a well-designed DPU shows up as faster training times and cheaper inference for every model that touches the accelerated cluster. As investors and accelerator programs increasingly fund infra-heavy plays in the Seattle area - something you can see in the mix of companies on Y Combinator’s list of Seattle-region startups - roles like this become a way to sit under the entire AI orchard rather than on a single branch. If you like thinking about how everything fits together in the rack, not just in the notebook, this is one of the most interesting places on the Eastside to do it.

Reading the Chalkboard - and the Orchard

By the time you’ve made a second lap past the stalls at the Bellevue Farmers Market, that “Top 10 Apples” chalkboard looks different. You’ve tasted a few samples, talked to the growers, and realized the list is more like a navigation aid than a scoreboard. That’s the right way to treat any “Top 10 AI Startups in Bellevue” list too: as a compressed view of a very full orchard, not a decree about who’s objectively best.

The Chalkboard vs. the Orchard

This particular chalkboard leans toward companies with clearly AI-heavy products, visible funding or traction, deep roots in the Eastside ecosystem, and something big looming in 2026 - IPO potential, acquisition pressure, or serious market expansion. That leaves out plenty of worthy players: bootstrapped teams, niche vertical tools, and very early experiments that don’t show up in databases like F6S’s broader list of Bellevue startups. They’re still part of the orchard; they just don’t fit on this particular chalkboard. The real question, whether you’re job hunting or angel investing, isn’t “who’s #1?” but “who’s best for the kind of problem I care about solving?”

What This Snapshot Says About the Eastside

Even with that subjectivity, the pattern across these ten companies is hard to miss. The Bellevue-Redmond-Kirkland corridor is skewed toward AI that touches real revenue, infrastructure, and human outcomes: experimentation and finance (Statsig, Summation), enterprise automation (Auger, Centific, SeekOut), the built and physical world (CityBldr, TerraClear, MangoBoost), and performance and wellbeing (Sportsbox AI, Aiberry). That mix reflects the local gravity of Microsoft and Amazon, the venture and industry focus documented in the WTIA’s AI startup and investment landscape, and a tax structure that makes equity meaningfully more attractive than in many rival hubs. It’s an ecosystem wired less for hype cycles and more for long, sometimes brutal product journeys.

"Bellevue is the startup city defining its shadow, growing a distinct ecosystem in the orbit of bigger neighbors." - Founder Institute, Seattle Chapter

Turning Lists into Career and Startup Leverage

If you’re aiming for an AI or ML role, the move isn’t to memorize who’s ranked where; it’s to treat each company here as a different “apple variety” and use low-risk samples - internships, side projects, hackathon teams, open-source contributions, and coffee chats - to see which flavors fit your skills and values. The same goes if you’re a founder: use leaderboards like GeekWire 200 or Seedtable as starting points, then walk the rows yourself, talking to alumni, investors, and customers. In an ecosystem where exits like Statsig’s are just one harvest in a longer season, the real advantage goes to people who can read both the chalkboard and the orchard: understanding not just which names are hot this year, but how the whole Eastside canopy is shifting and where you want to plant yourself for the next cycle.

Frequently Asked Questions