Top 10 Companies Hiring Full Stack Developers in 2026 (What They Look For + How to Stand Out)

Table of Contents

• How to Read This Top 10
• Google
• Meta
• Amazon
• Netflix
• Stripe
• Databricks
• Anthropic
• Airbnb
• Snowflake
• Canva
• How to Use This Top 10 List
• Frequently Asked Questions

Google

The Google table is usually the one with the longest line snaking across the gym floor. The brand, the compensation screenshots, the “I work on Search” mystique - it all feels like the obvious place to stand. But once you get past the logo, Google’s full stack roles are less about magic and more about very specific skills: strong fundamentals, an AI-first mindset, and the ability to ship products at a scale most bootcamp projects never touch.

Tech stack and how AI actually shows up

Most full stack roles at Google orbit a familiar core, even if team details vary. On the backend you’ll see C++, Java, Python, and Go. On the frontend, it’s usually TypeScript with Angular or internal frameworks like Blaze. Infra leans on Google Cloud Platform staples such as Spanner, Bigtable, and Borg/Kubernetes. The twist is that almost every modern product is wrapped around some machine learning or large language model component, so you’re not just building a UI - you’re building the UI that sits in front of ranking systems, recommendations, or code-assist tools. Day to day, that means using AI as a collaborator (for prototyping, refactoring, test generation) while still understanding enough algorithms and systems to catch when the AI is confidently wrong.

What Google actually screens for

Behind the glossy job description, Google still leans heavily on classic signals: data structures and algorithms, system design, and “Googliness” (how you communicate, take feedback, and keep users at the center of your decisions). A typical process is a recruiter screen, one or two technical phone interviews, then a multi-round “onsite” with coding, system design, and behavioral questions, much like the patterns described in Bristow Holland’s breakdown of top full stack employers. The bar is higher than it used to be because AI can now brute-force a lot of LeetCode; what matters is how you reason, debug, and explain tradeoffs, not just whether you reach a working solution.

Compensation and hybrid reality

Comp is a big part of why that line is so long. Data from Levels.fyi’s Google full stack engineer page shows typical total compensation in the $195,000-$331,000+ range, with top levels exceeding $500,000 when equity and bonuses stack up. Aggregated ranges on related salary pages list total packages from roughly $191,000 up into the low millions across all levels, but most full stack engineers cluster in the mid-to-high hundreds of thousands rather than the extreme edges. The tradeoff is flexibility: most roles are hybrid, and many teams expect at least three days a week in hubs like Mountain View or New York, which matters if you were hoping for permanent “work from wherever” life.

Practical ways to stand out in an AI-heavy Google interview

If you already have a few years of experience, your goal is to look like someone who can own a slice of a web-scale product. In practice, that means going deep on at least one backend language from Google’s stack (Java, Go, or C++) plus a modern frontend with TypeScript and a robust framework. Build a portfolio project that feels like it belongs at Google scale: for example, a news or video-feed clone with pagination, caching, background jobs, and monitoring. Use AI assistants to draft parts of the implementation, then treat their output like a junior engineer’s PR: profile it, tighten complexity, add tests, and be ready to explain those tradeoffs out loud in a system design or code review-style interview.

For beginners and career-switchers, it’s totally valid to treat Google as a “later” target. Start with JavaScript/TypeScript and React, add one backend (Node/Express or Python/FastAPI), and get comfortable actually deploying things to the cloud. Focus on one full stack app that you keep refactoring for performance, security, and test coverage. As AI tools get better at spitting out boilerplate, your edge is understanding what that code does, how to debug it, and how to evolve it into something production-ready. Google’s interviews will probe that understanding, not just whether you can copy-paste a clever trick from an assistant.

Meta

Meta is where a lot of React dreams point: you open Facebook, Instagram, or WhatsApp, see infinite feeds and live comments flying by, and think, “I want to build that.” On the surface, full stack at Meta looks like React all day long and massive traffic numbers. Underneath, it’s a mix of tight performance constraints, AB tests wired into almost every feature, and ML systems constantly reshaping what users see.

What full stack work at Meta actually feels like

Most full stack roles sit on top of a stack built around PHP/Hack, Python, and C++ on the backend, with React, Relay, and GraphQL on the frontend. You’re often working on feeds, messaging, or creator tools where every UI decision affects ranking models, engagement, and safety systems. Features rarely ship as static code; they ship as experiments that talk to AI-powered services deciding which content to show, which comments to hide, and which notifications to send. Your job isn’t to out-code an AI model - it’s to understand how your code, metrics, and experiments interact with those models.

How Meta interviews and what they measure

Behind the scenes, Meta’s interviews are blunt about what matters: production-grade React, strong problem solving, and the ability to reason about performance at scale. Coding rounds tend to press on efficiency and clarity. For senior roles, system design interviews dig into how you’d build high-traffic services like a groups feature, messaging system, or short-form video feed. Across levels, they care whether you can navigate a huge codebase, keep UIs fast, and talk about tradeoffs in plain language.

Compensation and where you’ll actually work

Meta sits near the top of the pay charts. Market analyses describe a median total compensation for full stack engineers around $356,000, with some entry-level roles starting at roughly $200,000 in total comp. As one compensation roundup from Entrepreneur’s coverage of Meta hiring put it, entry-level positions “start at $200,000” and sit among the highest-paying junior roles in tech.

“Meta is hiring entry-level roles that start at $200,000 a year, putting them among the highest-paying junior positions in tech.” - Entrepreneur, Business News Desk

The fine print: many engineering roles have shifted back toward in-person or tight hybrid schedules, especially in Washington and California offices. If you were hoping for fully-remote-anywhere, Meta’s reality is closer to “office is your default, remote is the exception,” and that should factor into whether you circle this table on your own list.

How to stand out with React and AI in the mix

To feel like a strong fit, you want to look less like “I followed a React tutorial once” and more like “I’ve lived in a real frontend, and I know how to measure what users do.” For experienced developers, that means:

• Getting genuinely strong at React and modern frontends: hooks, context, performance profiling, accessibility, and state management at scale.
• Shipping projects that show AB testing and metrics, such as a React app where you measure conversion changes when you tweak layout or loading behavior, and documenting the impact.
• Practicing designs for features like a “Facebook Groups clone” or a short-form video feed, thinking explicitly about data modeling, caching, and ranking signals.

For beginners and career-switchers, Meta is still reachable over time if you’re deliberate:

• Build one polished React SPA backed by a simple API (Node/Express or Python/Django) with features like infinite scroll, optimistic updates, and real-time updates via WebSockets.
• Use AI to generate boilerplate tests and documentation, then edit for clarity and correctness; be ready to talk about this collaboration in interviews as “I treated the AI like a junior teammate.”
• Learn enough SQL and querying to pull simple metrics (daily active users, retention, click-through rate) and visualize them inside your app.

Amazon

Compared to the “shiny” social apps, Amazon’s full stack roles can look almost ordinary at first glance: shopping carts, order histories, internal dashboards. But when you zoom in, you’re dealing with systems that power global retail, logistics, streaming, and AWS itself. That combination - huge scale plus very practical products - is why Amazon quietly hires more full stack developers than almost anyone else.

Where full stack fits in at Amazon

Most teams share a common backbone: Java and Python on the backend (with some Node.js), React and TypeScript on the frontend, and “AWS everything” underneath. That usually means services like Lambda, DynamoDB, S3, API Gateway, and container platforms like ECS or EKS. AI shows up in recommendations, logistics optimization, Alexa, and internal developer tools, but as a full stack dev you’re more often integrating AI/ML services (for example, calling a recommendation or anomaly-detection API) than training models yourself. The expectation is that you can design secure, reliable services, then plug in AI where it actually improves the customer experience.

Compensation and hybrid expectations

Part of Amazon’s draw is that it pairs “learn a ton” roles with serious pay. Analyses of full stack salaries on sites like 6figr’s Amazon compensation breakdown and Levels-style datasets put median total compensation around $212,000, with senior full stack engineers reaching $391,000+ when you combine base, bonus, and stock. You’ll see everything from traditional day shifts in major hubs to less typical options like overnight engineering support in job categories on ZipRecruiter’s Amazon full stack listings. The catch is location: many roles follow a hybrid model with a strong in-office presence in places like Seattle, New York, or Austin, and some teams are still very office-centric.

How to stand out as an experienced engineer

If you’ve already shipped production systems, Amazon wants to see that you can own a slice of a distributed architecture and think in terms of customers, not just code. Concretely, that might look like:

• Going deep on Java or Python with solid testing practices, paired with React/TypeScript on the frontend.
• Building a side project on AWS that mirrors how Amazon actually structures things: separate microservices, an API gateway, DynamoDB or RDS, and SQS/SNS for async jobs.
• Preparing STAR-format stories (Situation, Task, Action, Result) that map directly to Leadership Principles like Customer Obsession, Ownership, and Dive Deep.

How to stand out as a beginner or career-switcher

For newer developers, Amazon is reachable if you treat it as a multi-step goal instead of a first job by default. Start by using the AWS free tier to deploy something real, like a simple e-commerce or booking app:

• Backend in Node/Express or Python/FastAPI on Lambda, with an API Gateway front door.
• Frontend in React (Next.js is a bonus) that talks to those APIs and handles real-world concerns like loading states and error messages.
• Database in DynamoDB or Postgres on RDS, with basic authentication and input validation.

Let AI tools help you scaffold infrastructure-as-code (CloudFormation, Terraform, or CDK) and write boilerplate, but manually verify IAM permissions, error handling, and scaling settings. For the written exercise, practice summarizing that project in a one- or two-page narrative that explains customer impact, tradeoffs you made, and what you’d improve next - because that’s the kind of thinking Amazon is really screening for behind the glossy brochure.

Netflix

Netflix is the classic “longest line in the gym” table: everyone knows the logo, everyone’s seen the product, and the stories about compensation travel fast. What’s less obvious from the outside is that most Netflix full stack roles are effectively senior by default. You’re not hired to grow into ownership; you’re hired because you already know how to design, ship, and operate customer-facing systems with very little hand-holding.

What “full stack” means at Netflix

On paper, the stack looks familiar: Java and Node.js on the backend, React (with TypeScript increasingly common) on the frontend, all running on top of AWS with heavy use of microservices and internal tooling. In practice, Netflix leans hard on AI in personalization, recommendations, content ranking, and streaming optimization, plus AI-augmented developer tools. As a full stack engineer, you’re usually not training models yourself; you’re building the services and UIs that sit between those models and millions of users, making sure experiments are safe, performance is predictable, and failures are observable.

Interviews, autonomy, and culture fit

The interview loop reflects that expectation of autonomy. Candidates typically go through a recruiter screen, two technical interviews (covering coding and high-level system design), and then two intensive “culture fit” rounds that probe Netflix’s “freedom and responsibility” culture. You’re evaluated less on whether you can follow a spec and more on whether you can define one, collaborate with product and design, and own a service end-to-end. That aligns with broader senior-level expectations described in full stack role roundups on sites like Built In’s full stack job boards, where high-impact roles consistently emphasize ownership and system thinking over narrow coding tasks.

Compensation and where work actually happens

Netflix is known for top-of-market, all-cash offers. Many senior engineers land total compensation packages in the $450,000-$600,000+ range, trading equity-heavy structures for higher salary and bonus numbers. That’s a huge part of the appeal - and also part of the pressure. Remote policy is flexible but highly team-dependent: some groups are effectively hybrid around hubs like Los Gatos or Los Angeles, while others are more open to distributed work. Unlike some companies with rigid global policies, you often don’t know the exact flexibility until you’re in specific team conversations, so it’s worth treating “remote at Netflix” as a question to ask, not an assumption.

How to make Netflix part of your roadmap (without fixating on it)

If you’re already experienced, the way to stand out is to look like someone who could start tomorrow and take over a service: strong Java or Node on the backend, React/TypeScript on the frontend, and a track record of owning production systems. That means being able to talk through incidents you’ve handled, how you’ve improved reliability, and how you’ve used AI tools to speed up safe changes (for example, generating test scaffolding or log analysis scripts, then tightening them by hand). For beginners and career-switchers, Netflix is realistically a later milestone. Use it as a north star rather than a first job: in your own projects, prioritize operability (logs, metrics, tracing), automated tests, and clear documentation. Treat AI as your junior collaborator - great for boilerplate and refactors, but never a substitute for understanding what your code does and how it behaves in production. Over time, that combination of depth, ownership, and AI-aware judgment is what turns “a long line at a famous table” into a realistic option instead of just a daydream.

Stripe

Imagine opening your browser to a payments dashboard you built: payouts updating in real time, webhooks firing, charge failures spiking in one region. That’s the kind of surface area Stripe full stack engineers live in - money actually moves when you ship code. It’s less about chasing viral feeds and more about making it effortless and safe for businesses to get paid.

How Stripe’s stack supports product-focused engineering

Stripe’s full stack work usually centers on a backend built with Ruby, Java, or Go, and a frontend in React with TypeScript. You might be building merchant dashboards, onboarding flows, or internal tools that sit on top of complex payment and compliance systems. AI is woven into fraud detection, financial risk modeling, and even developer tooling; your job is often to integrate those AI-driven signals into clear UIs and APIs - surfacing why a charge was blocked, how risk is scored, or which automated action to take next.

Compensation, remote culture, and the fine print

Stripe’s appeal isn’t just the tech; it’s also the combination of pay and autonomy. Market snapshots describe total compensation for mid-to-senior full stack engineers commonly in the $250,000-$400,000 range, mixing base salary with meaningful equity. On top of that, Stripe has built a reputation as a remote-friendly, distributed company, which stands out in a world where many big names are pulling engineers back to offices. That lines up with broader hiring trends noted in analyses like WriteUpCafe’s web developer trends report, where remote-first cultures and strong internal tooling are becoming key differentiators for full stack roles.

“Full-stack developers bring end-to-end ownership to the table, allowing businesses to move faster with fewer handoffs and less miscommunication.” - Harsh Mangla, “Why Hiring a Full-Stack Developer is the Best Decision for Your Business”, LinkedIn

Practical ways to stand out (no hype required)

For experienced engineers, Stripe is a great fit if you can show that you think like a product person who happens to write code. That might look like building a subscription billing or invoicing app with a clean, well-documented REST or GraphQL API; a React/TypeScript dashboard that feels “Stripe-like” in polish; and careful handling of edge cases like idempotent requests, retries, and audit logs. Use AI tools to help generate test suites and API docs, then refine them so they read like something you’d be proud to hand to a paying customer.

If you’re a beginner or career-switcher, you don’t need a fintech job on your résumé to signal “Stripe-shaped” skills. Start by implementing a simple checkout or client billing flow in your portfolio - even if you’re not processing real cards. Focus on clear error states, mobile-friendly design, and security basics like input validation. Let an AI assistant scaffold components or suggest UI variants, then make the hard calls yourself about naming, structure, and user experience. The combination of one or two solid, payments-flavored projects and a clear story about how you guide AI rather than just copy-paste from it is exactly the kind of signal a company like Stripe is scanning for behind the scenes.

Databricks

Databricks is that booth with the full candy bowl and a short line: not as neon-highlighted as Google or Meta for most beginners, but quietly one of the most interesting places to be if you care about data, AI, and serious infrastructure. Instead of optimizing a single product feed, you’re helping build the platform other companies use to run their own analytics and machine learning workloads.

Stack and how AI shapes the work

Day to day, Databricks full stack engineers sit on a stack built around Scala, Java, and Python on the backend, with React on the frontend. Under the hood you’re standing on Apache Spark and major cloud providers, wiring together notebooks, dashboards, and collaboration tools that sit directly on top of huge data and AI pipelines. Instead of just calling a recommendation API, you’re often building the UI and service layer that helps customers run SQL, train models, and debug jobs across petabytes of data. AI isn’t a side feature here; the whole platform exists so other teams can build, serve, and monitor AI and analytics workloads.

What Databricks looks for in a “full stack” dev

Because the product is a data and AI platform, Databricks leans heavily toward engineers who understand distributed systems and data engineering concepts, even if you’re coming from a more traditional web background. Think about things like partitioning, caching, failure modes, and how to design UIs that make complex data workflows feel approachable. That lines up with broader hiring trends highlighted in resources like Edureka’s discussion of full stack demand, where companies increasingly want full stack developers who can reason about scalability, cloud services, and data-heavy architectures instead of just CRUD apps.

Compensation, location, and how to shape your prep

On the pay side, Databricks sits firmly in the “quiet table, great candy” category: total compensation for full stack roles often exceeds $300,000 once you include equity, with hybrid-friendly expectations anchored around hubs like San Francisco. You’ll see the same macro forces here that analysts call out in broader WorkTech predictions on sites like Solutions Review’s 2026 forecast: AI is baked into both the product and the development workflow, and experienced engineers are still critical to guide, review, and harden what AI tools produce.

To stand out, experienced devs should build something genuinely data-intensive: for example, a dashboard over a big public dataset (NYC taxis, open city budgets, or GitHub activity) with a Python or Scala backend, a React front-end, and at least some notion of batching, caching, and failure handling. Let AI help you sketch out Spark jobs or visualization options, then tune them for correctness and performance. If you’re a beginner or career-switcher, start simpler - Python + FastAPI + React - but keep data central: ingest CSVs, transform them into a database, and visualize trends. In your README, add a short “How I’d scale this if data grew 100x” section; that kind of thinking is exactly what turns a basic project into something that looks Databricks-shaped, even if you’re not yet running Spark clusters yourself.

Anthropic

If Google is the table with the loudest crowd, Anthropic is the one where the conversations are quieter - and a lot more about what AI should and shouldn’t do. Instead of arguing over which JavaScript framework is hottest, people here are debating how to make large language models safer, more reliable, and easier for real humans to control. For a full stack developer, that means you’re not just building “an app that uses AI”; you’re building the scaffolding that keeps powerful models usable, auditable, and aligned with human goals.

Where full stack fits in an AI-first company

Anthropic’s stack looks familiar on the surface: Python on the backend, TypeScript and React on the frontend, plus cloud services and specialized AI serving infrastructure under the hood. The difference is that almost everything you ship wraps a language model: playgrounds, dashboards, safety review tools, annotation systems, or integrations that embed models into other products. You’re often designing APIs that mediate between unstructured prompts and responses on one side, and very structured safety, logging, and billing requirements on the other. That blend - solid web fundamentals plus deep AI integration - is exactly the kind of “specialized full stack” profile described in roadmaps like TheBitForge’s full stack developer roadmap, where LLMs are treated as a core platform primitive.

What Anthropic looks for and how interviews reflect it

On the hiring side, Anthropic leans toward engineers who can move fast without breaking safety: strong fundamentals, comfort with rapid prototyping, and genuine interest in AI ethics and reliability. Interviews tend to be highly technical but very practical - think building or debugging small full stack features that call model APIs, reasoning about failure modes, and sketching how you’d monitor or review model behavior. They’re not looking for someone who can beat an AI at coding tasks; they’re looking for someone who can direct AI systems, catch subtle issues, and design human-in-the-loop workflows.

“Experienced software developers will remain vital in 2026 specifically to provide the human oversight needed for sophisticated AI-driven development agents.” - WorkTech Predictions, Solutions Review

Compensation, remote reality, and how to prepare

Anthropic aims to stay competitive with Big Tech on pay: many full stack roles land in the $250,000-$450,000 total compensation range when you combine salary and equity. Most engineering work is anchored around a hybrid schedule in San Francisco, but there’s meaningful flexibility for exceptional remote candidates. That fits the broader pattern described in market overviews like Varniktech’s look at full stack opportunities: top roles increasingly cluster where AI and cloud expertise intersect, and employers are willing to pay a premium for people who can own complex, high-impact systems end-to-end.

To stand out as an experienced dev, build a full stack app that wraps a language model API: a Python or Node backend that proxies requests, handles rate limiting and logging, and a React frontend for chatting, reviewing outputs, and tagging problematic responses. Add simple safety filters, an “escalate for human review” flow, and a page that visualizes model behavior over time. Treat your AI assistant like a junior teammate - great for drafting code and tests, but always subject to your review and edits. If you’re a beginner or career-switcher, start smaller: a markdown-to-summary tool, a code explainer, or a content classifier that uses an LLM through a hosted API. Focus on clear error handling, input validation, and a short “Safety considerations” section in your README. That combination - solid web skills, thoughtful AI integration, and visible concern for safety - is exactly the kind of signal that makes a company like Anthropic take a closer look.

Airbnb

Airbnb is the booth that smells like coffee and looks like a design studio: polished screenshots of stays, maps full of pins, hosts and guests smiling on the brochure. If you’re drawn to UX, storytelling, and products that connect people in the real world, this is the kind of full stack work that feels very different from yet another internal dashboard.

Stack, marketplaces, and how AI shows up

Under the pretty UI, Airbnb runs on a very practical stack: Ruby on Rails and Java on the backend, React on the frontend, and cloud infrastructure plus internal tools behind the scenes. Full stack engineers here are usually working on marketplace flows where both sides have something at stake: search and filtering, booking and payments, messaging, reviews, and trust/safety. AI threads through those flows in specific ways: dynamic pricing suggestions for hosts, ranking and personalization in search, fraud detection on bookings, and smart nudges that help guests and hosts communicate better.

Compensation, flexibility, and the reality behind the brochure

Comp-wise, Airbnb sits in that tier where “design-forward consumer product” does not mean “underpaid.” Full stack roles often land in the $240,000-$380,000 total compensation range when you combine base, bonus, and equity. Their much-talked-about “Live and Work Anywhere” policy gives substantial location flexibility compared to many big tech peers, but it’s still bounded by time zones, tax rules, and team needs rather than “work from any beach, forever.” In the broader market, companies of all sizes are chasing similar full stack profiles; job boards like Robert Half’s full stack listings show hundreds of openings spanning everything from marketplaces to fintech and enterprise tools.

“Full-stack developers are among the most in-demand roles because they can work on both the front-end and the back-end, making them highly versatile for companies of all sizes.” - Edureka Instructor, “Full Stack Developer: Job, Salary & Who’s Hiring in 2025!”, YouTube

Portfolio ideas that look Airbnb-shaped

To look like you belong at a place like Airbnb, your projects should feel like they care about people, not just endpoints. For experienced devs, that means building something marketplace-like: a booking platform for classes, a home-exchange app, or a rentals search with rich filters. Show off React UIs that are responsive, accessible, and thoughtfully animated; a backend (Rails, Node, or Java) that models hosts/guests, listings, and reservations; and at least one AI-powered helper, like a pricing suggestion tool or personalized search using an LLM API. If you’re a beginner or career-switcher, focus on one clean, believable app rather than five tiny ones: seed it with realistic data, make the mobile experience great, and handle edge cases like cancellations and messaging gracefully. You can absolutely use an AI assistant to help with layout ideas, copy, or boilerplate code - just be ready to explain what you changed, why you changed it, and how you’d improve the product based on real user feedback, the same way an Airbnb team would expect from any full stack engineer.

Snowflake

Snowflake is one of those “quiet tables” you might walk past at first glance: no flashy consumer app, no infinite social feed, just the promise of a data cloud that most non-engineers never see directly. But if you care about scalability, cloud infrastructure, and building tools that other companies rely on for analytics and AI, this is exactly the kind of full stack environment that can accelerate your growth.

Why Snowflake matters for full stack developers

Snowflake’s entire business is a cloud data platform that runs on top of major public clouds. Instead of building a single product UI, you’re building consoles, dashboards, and tooling that let other teams store, query, and share massive datasets - and increasingly run AI workloads on top of them. The problems are less about fancy animations and more about multi-tenant isolation, secure access patterns, and making very complex data operations feel approachable to humans who aren’t database experts.

Stack, scalability, and interview expectations

Under the hood, Snowflake leans on a backend written largely in Java and C++, with a frontend built in React. The platform is designed to be cloud-agnostic, running across major providers, so even as a full stack engineer you’re expected to understand how cloud infrastructure, databases, and networking interact. Hiring managers look for people who can reason about system scalability and performance, and who can design tools that make those concerns visible and manageable for end users.

Interview loops follow a familiar big-tech pattern: coding rounds to test your fundamentals, system design interviews that zero in on how you’d scale data-heavy features, and conversations focused on how you collaborate with product, data, and infra teams. In a market where many companies want “full stack” to also mean “comfortable with the cloud,” general job boards like Indeed’s full stack engineer listings show hundreds of roles that expect similar hybrid skills: solid web development plus real understanding of distributed systems and databases.

Compensation, hybrid work, and what to practice

On compensation, Snowflake sits firmly in upper-tier territory: full stack engineers typically see total packages around $220,000-$380,000, depending on level and equity. The work model is usually an office-centric hybrid, with time expected in key hubs rather than fully remote by default. For some people, that structure is a downside; for others, the combination of high-impact data work and strong pay makes it worth planning a relocation.

How to build a Snowflake-shaped portfolio

To look like a fit, you want projects where data is the main character, not an afterthought. For experienced developers, that might mean building an admin console with multi-tenant dashboards, role-based access control, and query monitoring. Think about indexing, partitioning, and caching strategies, and document how you’d evolve your architecture if data volume or concurrency spiked 100x. For beginners and career-switchers, start with a simpler stack (Node or Python on the backend, React on the frontend) but make reporting and analytics a first-class feature: pagination, complex filtering, CSV exports, and basic query optimization. Add a small AI layer by letting users generate or refine queries with an LLM, while you enforce strict validation and access control on the backend. That mix of web fundamentals, data-awareness, and AI integration is exactly what turns “I built a CRUD app” into “I can help build a data platform like Snowflake.”

Canva

For a lot of people, the “Canva table” is the first one that makes full stack work feel tangible: you’re not just moving JSON around, you’re helping non-technical people drag, drop, and create designs that look professional. If you naturally notice spacing, typography, and whether a button “feels” right, Canva-style roles are where that instinct becomes part of your job description instead of a side hobby.

Design-forward stack, with AI in the editor

Under the hood, Canva’s stack is surprisingly classic: Java services on the backend, TypeScript and React on the frontend, plus mobile work in Swift and a lot of custom rendering logic for the editor itself. What makes it different is how much of your full stack work is visible, interactive, and tightly coupled to AI. You’re building collaborative editors, template browsers, and asset libraries that lean on AI for layout suggestions, automatic copy, and image generation. Instead of calling a recommendation API for a feed, you might be calling a model that proposes color palettes, generates icons, or rewrites slide headlines in a friendlier tone, and then wrapping that in guardrails and an interface normal people can actually trust.

What Canva looks for, and what it offers

Canva tends to hire design-conscious engineers who can talk comfortably about visuals and UX, not just database schemas. They care whether you can build smooth, performant UIs, collaborate with designers, and keep accessibility in mind while you ship fast. Market snapshots put Canva’s compensation in the neighborhood of $150,000-$280,000+ (USD-equivalent), depending on level and region, with a flexible “work from anywhere within certain zones” policy. That combination of creative product, solid pay, and geographic flexibility mirrors what many design- and product-led startups are chasing on platforms like Y Combinator’s software engineer job board, where React/TypeScript plus UX awareness is one of the most common profiles.

“Companies now prefer developers with deep, expert-level proficiency in one specific area… alongside broad stack knowledge.” - WriteUpCafe, “Top 7 Hiring Trends for Web Developers in 2026”

Building a Canva-shaped portfolio (for pros and switchers)

To look like a fit, your work needs to say “T-shaped full stack” with a tall bar in frontends and UX. For experienced devs, that might mean a rich editor: a slide or poster builder with drag-and-drop, snapping guides, keyboard shortcuts, and real-time collaboration. Use an LLM to propose layout variations, rewrite text, or suggest palettes, then show how you constrained it so outputs stay on-brand and safe. For beginners and career-switchers, one polished React/TypeScript project can go a long way: build a simple design tool (social media graphic maker, moodboard, or presentation helper) with a Node or Python backend that stores user projects. Let AI scaffold some CSS or component code, but then refine spacing, hierarchy, and interactions yourself. Study how design-focused engineers present their skills on profiles like React and UI specialists on Toptal, and aim for that same balance of technical depth and visual care. Your goal isn’t to out-draw or out-write the AI inside the editor - it’s to be the person who decides when, where, and how that AI shows up for real users.

How to Use This Top 10 List

By now, this Top 10 might feel a bit like that poster in the gym: big names, big numbers, lots of noise. The key shift is treating it less like a ranking you have to climb and more like a map of patterns. Each “table” on this list exposes what thousands of companies are really rewarding right now: T-shaped skills, comfort with modern stacks, and the ability to work with AI instead of trying to beat it at coding puzzles.

Decide what you’re actually optimizing for

Different companies on this list are excellent for different reasons. Before you aim at any of them, decide what you’d circle in neon for yourself: compensation, learning environment, AI exposure, remote flexibility, or something else. That clarity makes it much easier to ignore the gym noise of headlines and hype and focus on roles that match how you actually want to work.

Build 2-3 projects that mirror real roles

Look back over the sections and you’ll see the same advice repeated in different accents: don’t just learn syntax, build 2-3 serious projects that look like smaller versions of work at these companies. The public lists of openings (like the SimplifyJobs new-grad positions tracker on GitHub) tell a similar story: full stack roles expect a real frontend, a real backend, and a real deployment, with at least some AI in the product or your workflow. Treat each project like a case study you can walk through in detail: architecture, tradeoffs, tests, and how you’d scale or secure it if usage exploded.

Practice being the person who directs the AI

Across the market, employers are not expecting you to out-code a language model; they’re expecting you to guide and correct it. As one skills overview from Talent500 puts it, “AI agents will write most greenfield code; companies now value developers who can optimize, guide, and refine AI-generated code.” That’s your lane. Use assistants to draft solutions to coding problems or scaffolding for system designs, then critique them: fix complexity issues, add tests, tighten security, and be ready to explain why your version is better. You’re training yourself to act like the reviewer these companies wish they had more of.

“AI agents will write most greenfield code; companies now value developers who can optimize, guide, and refine AI-generated code.” - Talent500 Editorial Team, In-Demand Full Stack Developer Skills 2026

Make your own list instead of chasing the longest line

Finally, remember that the “Top 10” is just one poster on a much bigger wall. There are banks, design tools, enterprise AI vendors, and hundreds of startups that didn’t make this list but still offer great full stack roles. Stories from self-taught devs on communities like r/learnprogramming’s full stack success threads usually share the same pattern: pick a stack, build something real, iterate in public, and use each opportunity (even QA or support roles) as a stepping stone. Use this Top 10 as an X-ray of what strong jobs tend to look like, then build your own shorter list that fits your skills, your life constraints, and the way you want to work with AI. The companies here are signals; your projects and judgment are what turn those signals into an actual path.

Frequently Asked Questions