Top 10 Highest-Paying Backend and DevOps Jobs in 2026 (Salary Breakdown)

Table of Contents

• You’re Already Using Rankings - Now Use Them for Your Career
• Principal DevOps Engineer
• Staff Backend Engineer
• Backend Architect
• Cloud Architect
• Site Reliability Engineer
• MLOps Engineer
• Platform Engineer
• DevSecOps Engineer
• Infrastructure Engineer
• Senior Backend Developer Go/Rust Specialist
• How to Use This Top 10 Without Letting It Use You
• Frequently Asked Questions

Principal DevOps Engineer

What they do

Behind every “Deploy” button and green status dashboard, Principal DevOps Engineers are the people quietly running the show. Instead of tuning a single pipeline, they own the entire infrastructure story end to end: source control, CI/CD, environments, observability, and release strategies for dozens or even hundreds of services. At this level, you’re designing organization-wide standards, leading incident playbooks, mentoring other senior engineers, and making the big tradeoffs between speed, reliability, security, and cost. You’re less the person writing one-off scripts and more the person defining the platform and policies that everyone else builds on.

Salary in 2026

On the ranking board, Principal DevOps / DevOps Architect roles sit near the very top. Across US tech, you’ll commonly see a $200,000 - $280,000 base salary, with total compensation in the $400,000 - $600,000+ range once stock and bonuses are included. Data aggregated in Glassdoor’s principal DevOps salary reports and similar principal-level breakdowns shows some “DevOps Architect” titles at large cloud or FAANG-style companies reaching around $500,000 - $750,000 in TC. These numbers skew heavily toward senior folks with 10-15+ years in the field, working in high-cost tech hubs and at companies where stock grants are a big part of pay.

Skills, stack, and AI angle

To get paid at this level, you need deep, hands-on command of at least one major cloud (AWS, Azure, or GCP), containers and orchestration (Docker, Kubernetes), Infrastructure as Code (Terraform or CloudFormation), and observability stacks for metrics, logs, and tracing. You’re also expected to be comfortable with security and compliance (IAM, secrets management, policies as code) and with leadership tasks like architecture reviews and long-term roadmapping. AI doesn’t replace this role; it raises expectations. Companies increasingly look to principals to integrate AIOps platforms, AI-assisted runbooks, and automated remediation, and reports like Coursera’s DevOps salary analysis note that DevOps pros who can modernize infrastructure with automation and cloud-native patterns are commanding the strongest premiums.

How to get there (runtime)

If this job were a series, its “runtime” would put it around Season 6-8 of your career. A realistic arc looks like: 0-2 years as a Junior DevOps/Cloud/Backend engineer learning Linux, Git, CI/CD, and cloud basics; 3-5 years as a mid-level DevOps or SRE owning services and pipelines; 6-9 years as a Senior DevOps/Platform engineer leading major projects and mentoring; and 10+ years before you’re truly operating at Principal/Architect scope. For beginners and career-switchers, the next episode isn’t “Principal DevOps” - it’s landing that first hands-on DevOps or backend role, building solid Python, SQL, CI/CD, Docker, and cloud skills, and using AI tools as your assistant rather than a shortcut around fundamentals.

Staff Backend Engineer

What they do

Staff Backend Engineers are the people who stop the plot from falling apart when traffic spikes, new features ship, and three different teams all need changes to the same core service. Instead of just implementing tickets, they own critical back-end systems end to end: designing and scaling high-traffic APIs, safeguarding data models, and setting patterns that other engineers follow. They’re often the technical lead for core domains like payments, authentication, or search, with deep influence over architecture and a big say in what “good” looks like across multiple teams - without necessarily being people managers.

Salary in 2026

On the salary charts, Staff Backend Engineers rank near the top for individual contributors. In US tech hubs, base salaries commonly sit around $170,000 - $230,000, with typical total compensation in the $300,000 - $450,000+ range once stock and bonuses are included. Aggregated datasets such as 6figr’s staff backend salary breakdown show some staff-level backend roles at top-tier companies reported in the high $400,000+ band. These numbers usually belong to engineers with 8-12+ years of experience, strong distributed-systems skills, and a track record of owning revenue-critical services.

Skills, stack, and AI angle

Staff-level backend work goes far beyond writing endpoints. You’re expected to be fluent in at least one major backend language - often Python, Go, Java, or increasingly Rust - plus SQL databases like PostgreSQL or MySQL, caching layers, and queues. You need to understand microservices, event-driven design, and tradeoffs between consistency, latency, and cost. You’re also DevOps-literate: comfortable with CI/CD, containers, cloud deployments, and monitoring. AI tools such as code copilots can speed up boilerplate and refactors, but they don’t design robust data models or choose the right consistency model; at staff level, your value is in the architecture and judgment that AI can’t safely automate.

How to get there (runtime)

If this role were a series, you’d usually hit “Staff” somewhere around Seasons 4-6. A realistic arc looks like: 0-2 years as a Junior Backend Developer learning to ship features and fix bugs; 3-5 years as a Backend Engineer owning smaller services and collaborating closely with product; 6-9 years as a Senior Backend Engineer leading projects and mentoring; and 8-12+ years before you’re fully operating at staff scope across multiple teams. For beginners and career-switchers, the next right episode is much smaller: get solid with one backend language (often Python), learn SQL and data modeling, build and deploy simple APIs, and get comfortable with Git, Linux, Docker, and a major cloud provider. Once that foundation is real - not just copied from AI - you can gradually take on larger services, cross-team projects, and the kind of deep system design that justifies staff-level compensation.

Backend Architect

What they do

Where a staff backend engineer might own a few critical services, a Backend Architect is responsible for how all those services fit together. They define the high-level architecture for APIs, microservices, data flows, and integration patterns across the product. That means deciding where to draw service boundaries, how data should be modeled and shared, and which cross-cutting concerns (logging, auth, rate limiting, observability) must be handled consistently everywhere.

This role lives at the intersection of code and long-term strategy. Backend Architects spend much of their time reviewing designs, creating reference implementations, and working with leads in security, DevOps, and product to balance performance, reliability, compliance, and cost. A good architecture can save a company millions over the years; a bad one can lock teams into slow, painful rewrites.

Salary in 2026

Because their decisions affect everything “under the hood,” Backend Architects are paid near the top of the individual-contributor ladder. Typical US tech-hub ranges are a base salary around $150,000 - $200,000, with total compensation roughly in the $275,000 - $425,000 band once stock and bonuses are included. Analyses like Research.com’s highest-paying IT careers report and architect-level overviews from firms such as the Alexander Technology Group consistently list software and cloud architects among the best-compensated roles in tech, reflecting the leverage and risk that come with their decisions.

Skills, stack, and AI angle

To be credible as a Backend Architect, you still need to be hands-on enough to prototype and debug. That usually means deep experience with at least one backend language (often Python, Java, Go, or Rust), strong SQL and data modeling skills, and a solid grasp of NoSQL, caches, and queues. On top of that, you’re expected to understand system design patterns (sagas, event sourcing, CQRS, circuit breakers), reliability and observability, and security fundamentals like authentication, authorization, and encryption.

AI changes what you architect but not the fact that you need to architect it thoughtfully. Many systems now include LLM APIs, vector databases, and model-serving layers, and high-paying-job roundups note that roles touching AI and cloud together are seeing some of the strongest growth in demand. As an architect, you’re the one deciding how those AI components integrate with existing services, how to protect training and prompt data, and how to keep costs from spiraling as usage grows. AI tools can help you explore designs faster or generate boilerplate diagrams, but they can’t yet own the tradeoffs between latency, consistency, security, and budget.

How to get there (runtime)

If this role were a show, its runtime would put it well past a quick miniseries. Most Backend Architects start out as backend developers shipping features, then spend several years owning entire services, leading projects, and gradually taking on cross-team responsibilities. By the time you’re making architecture calls that affect multiple products or business lines, you’ve usually spent close to a decade inside real systems, learning from migrations, outages, and redesigns. If you’re in your own “Season 1” right now, the next step isn’t to jump straight to architecture diagrams; it’s to build and ship real APIs, get comfortable with SQL and cloud deployments, and use AI tools to deepen your understanding of why certain patterns work - so that later, when the title says “Architect,” the decisions behind it are grounded in hard-won experience.

Cloud Architect

What they do

Cloud Architects are the city planners of a company’s infrastructure. Instead of worrying about a single app or pipeline, they design how everything fits together in the cloud: how services talk to each other, where data lives, how users are authenticated, and how to keep costs from quietly exploding. Day to day, that looks like planning and overseeing cloud migrations, deciding between managed services and self-hosted options, defining networking and security architectures, and helping leadership translate “we’re going all-in on the cloud” into something engineers can actually build and operate.

Salary in 2026

On compensation charts, these roles consistently show up near the top. Typical ranges for Cloud Architects in US tech hubs land around a $140,000 - $195,000 base salary, with total compensation commonly in the $250,000 - $450,000 range once bonuses and stock are included. In fact, analyses of high-paying software roles like Nexford University’s breakdown of 2026 software engineering salaries highlight cloud and solutions architects as some of the best-compensated positions, largely because their decisions can save (or cost) enterprises millions in infrastructure and operations over time.

Skills, stack, and AI angle

To operate at this level, you need deep experience with at least one major cloud provider (AWS, Azure, or GCP), strong networking knowledge (VPCs, VPNs, load balancers, DNS), security fundamentals (IAM, KMS, encryption, compliance), and Infrastructure as Code tools like Terraform or CloudFormation. Cloud Architects also spend a lot of time on cost management, designing autoscaling strategies and right-sizing resources so the bill doesn’t balloon as usage grows. AI raises the stakes: more and more companies are running AI/ML workloads, LLM endpoints, and GPU-heavy training jobs in the cloud, which amplifies both cost and complexity. The architect is the one who decides how to host those workloads, how to secure training and inference data, and how to keep performance high without blowing the budget.

How to get there (runtime)

If this role were a series, you’d hit “Cloud Architect” around Season 5 or later. The usual path runs from 0-2 years as a cloud-aware DevOps, SRE, or backend engineer deploying real apps; 3-5 years as a Cloud or Platform Engineer managing infrastructure for multiple teams; and 6-9+ years as a senior cloud/DevOps engineer leading migrations and major infra projects before you’re truly ready to own an organization’s cloud strategy. If you’re still early in your own storyline, the next step is much simpler: get comfortable deploying a basic backend app to a major cloud provider, learn IaC from day one instead of relying only on web consoles, and treat AI tools as helpers for reading docs and sketching architectures - not as a substitute for understanding how networks, security, and costs actually work in the cloud.

Site Reliability Engineer

What they do

Site Reliability Engineers are the ones making sure the show doesn’t suddenly cut to static. Instead of building new product features, they focus on keeping existing systems fast, available, and predictable. That means defining and tracking SLIs/SLOs, building tooling for deployments and rollbacks, refining on-call practices, and running blameless post-mortems when something breaks. At staff/principal level, SREs don’t just fix outages; they shape reliability culture across teams so fewer outages happen in the first place.

Salary in 2026

Because downtime is literally “lost money,” SRE compensation reflects how critical the role is. Senior and staff/principal SREs in US tech hubs typically see base salaries around $140,000 - $175,000+, with total compensation roughly in the $250,000 - $400,000 range once bonuses and stock are added. Industry salary roundups, including SRE/DevOps bands summarized in sources like Built In’s DevOps and reliability salary data, show the upper end of those ranges most often in fintech, e-commerce, and large SaaS platforms where every minute of downtime has a clear revenue impact.

Skills, stack, and AI angle

To operate at this level, you need strong programming skills in languages like Python or Go, deep knowledge of Linux and networking, and fluency with containers and Kubernetes. You also live in observability tools: metrics, logs, traces, alerting, and dashboards are your everyday instruments. AI is showing up as AIOps platforms that can detect anomalies, summarize incidents, and suggest remediations, but it doesn’t replace the judgment required to design resilient systems, choose sane SLOs, or decide when to roll back versus roll forward. In practice, AI becomes another tool in the SRE toolbox, helping sift through noise so humans can focus on the highest-impact reliability work.

How to get there (runtime)

If this role were a show, its runtime would span several seasons. A common path looks like 0-2 years as a junior DevOps or systems engineer handling deployments and basic infra; 3-5 years as an SRE or DevOps engineer on call for a handful of services; 6-9 years as a Senior SRE leading incident programs and reliability-focused architecture work; and 8-12+ years before you’re operating at staff/principal scope across multiple teams. For career-switchers, early roles often have blended titles - DevOps Engineer, Cloud Engineer, or Backend Engineer with strong ops responsibilities - but the throughline is the same: get comfortable with Linux, networking basics, automation, CI/CD, containers, and cloud, then layer on reliability practices and incident management as you gain experience.

MLOps Engineer

What they do

MLOps Engineers live right at the crossroads of data science and DevOps. Instead of stopping at “the model works on my laptop,” they’re the ones who turn experiments in notebooks into reliable, monitored production services. Day to day, that means building and maintaining data and model pipelines (training, validation, deployment), wiring up ML-specific CI/CD/CT (continuous training), and creating monitoring for things like data drift, model performance, and fairness metrics. They also manage GPU-heavy infrastructure, feature stores, and model registries so data scientists and engineers can ship AI features without reinventing the wheel each time.

Salary in 2026

On the ranking screen, MLOps roles land firmly in the upper tier. Typical US ranges for this hybrid specialty put base salaries around $134,000 - $193,000, with total compensation commonly in the $220,000 - $380,000 band once bonuses and stock are included. That premium reflects how rare it still is to find people who understand both DevOps and machine learning well enough to keep production models healthy at scale. Broader AI salary analyses, like igmGuru’s list of highest-paying software engineering jobs, point out that AI/ML-focused roles routinely sit near the top of tech pay, with AI/ML engineers averaging around $187,000 in many US markets.

Skills, stack, and AI angle

To be effective in MLOps, you need strong Python skills, comfort with ML frameworks (PyTorch, TensorFlow, scikit-learn), and the usual DevOps toolkit: Docker, Kubernetes, CI/CD, and Infrastructure as Code. On top of that, you work with ML-specific tools like MLflow, Kubeflow, SageMaker, or Vertex AI for tracking experiments, registering models, and orchestrating training and deployment. AI isn’t just “part of the job” here - it is the product. That’s why roles with heavy AI exposure have seen faster wage growth; one analysis of AI’s impact on work from Baytech Consulting’s workforce study found AI-exposed jobs enjoying about a 3.8% real wage increase versus only 0.7% in less-exposed occupations. As an MLOps engineer, you’re squarely in that high-exposure category, but you still rely on classic engineering fundamentals to keep models reliable and cost-effective.

How to get there (runtime)

If this role were a series, you’d usually enter the MLOps storyline around Season 3 or 4. A common path looks like: 0-2 years as a data engineer, backend engineer, or DevOps engineer who occasionally supports ML projects; 3-5 years moving into a dedicated ML Engineer or MLOps Engineer role on a data/ML team; and 5-8+ years before you’re leading ML platform efforts for multiple products. For beginners and career-switchers, the most practical move isn’t to jump straight into deep learning frameworks, but to first get solid at backend and DevOps fundamentals with Python, SQL, CI/CD, Docker, and cloud deployments. Once you can reliably build and ship services, you can layer in ML-specific tools and start taking ownership of how models move from experiment to production.

Platform Engineer

What they do

Platform Engineers build the internal tools and paved roads that other developers use every day. Instead of focusing on one product feature, they create and maintain the internal developer platform (IDP): standardized ways to spin up new services, shared CI/CD pipelines, deployment templates, and observability defaults. The goal is simple but high impact: make it fast and safe for product teams to ship code by turning messy infrastructure steps into reusable building blocks that “just work” in the background.

Salary in 2026

Because their work multiplies the productivity of entire engineering organizations, senior Platform Engineers in US tech hubs typically see base salaries around $135,000 - $180,000, with total compensation commonly in the $220,000 - $350,000 range once bonuses and stock are included. These ranges align closely with senior DevOps/Platform bands reported in guides like Hakia’s DevOps engineer salary breakdown, which highlights how infrastructure and automation specialists often earn more than generalist developers at the same experience level.

Skills, stack, and AI angle

To thrive in this role, you need strong DevOps fundamentals: containers and orchestration (Docker, Kubernetes), CI/CD systems (GitHub Actions, GitLab CI, Jenkins, etc.), and Infrastructure as Code tools like Terraform. Scripting or programming in languages such as Python or Go is essential for building CLIs, operators, and platform automations. On top of that, great Platform Engineers think like product designers for developers - tracking friction points and smoothing them out. AI is quickly becoming part of the toolkit: teams are experimenting with internal code copilots, automated environment setup, and analytics on workflow bottlenecks. Lists of the highest-paying DevOps skills consistently feature Kubernetes, Terraform, and cloud expertise, all of which sit at the heart of platform work.

How to get there (runtime)

If this job were a show, you’d usually enter the Platform Engineer arc around Season 3 or 4. The common path starts with 0-2 years as a DevOps or backend engineer who enjoys building pipelines and deployment scripts; 3-5 years as a Platform or DevOps Engineer maintaining shared CI/CD, clusters, and internal tooling; and 5-8+ years before you’re leading platform initiatives that support many teams. For beginners and career-switchers, the next step isn’t “own the internal platform” - it’s getting comfortable with Git, Linux, Docker, CI/CD, and at least one major cloud, then volunteering for the “behind-the-scenes” tasks on your projects: improving build times, simplifying deployment configs, and documenting how services should be created. Those are the seeds of platform engineering, whether or not your job title says it yet.

DevSecOps Engineer

What they do

DevSecOps Engineers weave security into every step of the software delivery pipeline instead of tacking it on at the end. They integrate static and dynamic scanning into CI/CD, manage secrets and keys, define policies as code, and work with developers to fix vulnerabilities quickly. On top of that, they help design secure architectures for cloud and containerized applications so that identity, access, and network controls are built in from the start rather than bolted on after a breach or compliance audit.

Salary in 2026

Because they combine two high-value specialties - security and DevOps - DevSecOps Engineers sit in a premium pay band. Senior US roles typically see base salaries around $165,000 - $204,000+, with total compensation in the neighborhood of $200,000 - $350,000, based on security-focused DevOps analyses such as Hakia’s level-based salary guides and DevOps salary data from Motion Recruitment. Broader IT career overviews, like FinalRoundAI’s breakdown of IT career paths, regularly highlight security architect and cybersecurity engineer tracks alongside cloud and DevOps as some of the highest-paying roles, which is exactly the intersection where DevSecOps lives.

Skills, stack, and AI angle

To earn those numbers, you need solid DevOps fundamentals - CI/CD, Docker, Kubernetes, cloud (AWS, Azure, or GCP) - plus a strong security toolkit. That usually includes SAST/DAST and dependency scanners, container and image security tools, secrets management, and identity and access management (OAuth2/OIDC, IAM policies, role design). AI now shows up on both sides of the equation: attackers use automation and generative tools to discover and exploit weaknesses faster, while defenders lean on AI-powered analysis and anomaly detection to sift through logs and alerts. Industry roundups like Crossover’s list of high-paying engineering jobs point out that roles combining cloud, automation, and security are among the most resilient and best paid, precisely because they’re harder to automate safely.

How to get there (runtime)

If this role were a series, you’d usually enter the DevSecOps storyline around Season 3 or 4. A realistic path is 0-2 years as a DevOps or backend engineer learning pipelines, cloud, and containers; 3-5 years taking on more security responsibilities or moving from a security engineer role into CI/CD and infrastructure; and 5-8+ years before you’re the person owning security in the pipeline for multiple teams. For beginners, the next step isn’t memorizing every CVE - it’s getting comfortable with Linux, Git, CI/CD, Docker, and a major cloud provider, then layering in security: OWASP Top 10, basic cryptography, auth/authorization patterns, and hands-on experience adding scanners and policies to real build pipelines.

Infrastructure Engineer

What they do

Infrastructure Engineers are the foundation builders of tech teams. Instead of focusing on a single app, they design and maintain the core pieces everything else depends on: networks, storage, compute clusters, and the automation that ties it all together. In a cloud-native company, that usually means deep work with VPCs, subnets, load balancers, Kubernetes clusters, and shared databases; in hybrid or on-prem environments, it can also include physical servers, virtualization, and storage arrays.

Day to day, they implement and manage Infrastructure as Code (IaC), plan capacity, tune performance, and work closely with security and platform teams to ensure the underlying systems are fast, reliable, and secure. When something low-level breaks - DNS, routing, disks, or underlying nodes - Infrastructure Engineers are often the ones called in to untangle it.

Salary in 2026

Because their work is critical but often less visible than product features, Infrastructure Engineers tend to earn slightly below the flashiest titles, but still firmly in six-figure territory. Senior and staff-level roles in US tech hubs typically see base salaries around $140,000 - $180,000, with total compensation roughly in the $200,000 - $340,000 range once bonuses and stock are included. These bands line up with infrastructure-focused and DevOps ranges surfaced across multiple salary guides that group core infrastructure, cloud, and platform work near the top of engineering pay scales, especially in SaaS companies running large self-managed clusters.

Skills, stack, and AI angle

To be effective in this role, you need strong Linux skills, a solid grasp of networking (TCP/IP, DNS, routing, firewalls), and comfort with either cloud infrastructure (VPCs, security groups, gateways) or data center operations. Infrastructure as Code tools like Terraform or Ansible are non-negotiable at scale, and you’ll spend a lot of time in monitoring and logging systems looking at infra-level metrics such as CPU saturation, disk I/O, packet loss, and latency between services.

AI is increasingly another layer in your toolkit rather than a replacement for what you do. Modern teams use AI-assisted tools to generate configuration snippets, analyze huge log streams for anomalies, and spot early warning signs of failures. At the same time, AI workloads themselves - GPU clusters, high-throughput storage, and low-latency networking for model inference - introduce new infrastructure challenges. Overviews of DevOps trends, like Simplilearn’s look at DevOps patterns reshaping tech, consistently point to automation, observability, and cloud-native operations as core skills, all of which sit right at the heart of modern infrastructure engineering.

How to get there (runtime)

If this role were a series, you’d usually reach “Senior Infrastructure Engineer” somewhere around Seasons 4-6. A common path looks like: 0-2 years as a systems administrator, junior DevOps, or network engineer; 3-5 years as an Infrastructure or Cloud Engineer owning key components like clusters, networks, or storage; and 5-9+ years before you’re making staff-level decisions about how the entire foundation is designed and scaled. If you’re in your own Season 1, the next step is straightforward but not glamorous: get comfortable with Linux, learn networking basics, practice scripting in Python or Bash, and start using IaC and a major cloud provider on small projects. From there, AI tools can help you move faster and see patterns in complex systems - but they only become powerful once you understand the infrastructure they’re analyzing.

Senior Backend Developer Go/Rust Specialist

What they do

Senior Backend Developers who specialize in Go or Rust live where performance and reliability really matter. Instead of building just another CRUD endpoint, they’re the ones crafting low-latency, high-throughput services, API gateways, proxies, and real-time systems that have to stay fast under heavy load. They often work on infrastructure-adjacent components like messaging backbones, billing engines, or streaming pipelines, and they’re the people other teams call when concurrency bugs, memory leaks, or race conditions start causing mysterious failures in production.

Salary in 2026

On the salary rankings, this is a “quietly premium” niche. In US tech hubs, Senior Backend Developers with strong Go or Rust experience typically see base salaries around $110,000 - $193,000, with total compensation roughly in the $190,000 - $310,000 range. At large tech firms where stock plays a big role, top-end total comp for highly specialized backend engineers is often reported in the low $300,000+ band. Community discussions, like a widely shared Reddit thread on the highest-paying programming languages, frequently put Go and Rust near the top of the list, especially for backend and systems work, largely because there are fewer engineers who are truly fluent and comfortable using them in production.

Skills, stack, and AI angle

To earn this kind of pay, you need more than “I did a tutorial once.” You’re expected to have deep mastery of Go or Rust, including concurrency patterns, memory management, and performance profiling. You still work with the usual backend building blocks - REST or gRPC APIs, SQL and NoSQL databases, caching layers - but your edge is writing code that’s both safe and extremely fast. DevOps literacy (Docker, CI/CD, basic Kubernetes and cloud deployment) is assumed, not optional. Interestingly, AI code tools are still weaker at producing idiomatic, safe Rust or heavily concurrent Go than they are for more common stacks, which is one reason human expertise here continues to command a premium. Lists of top-paying remote coding roles, such as those highlighted by Metana’s remote coding jobs roundup, regularly call out Go- and systems-focused backend work as some of the best-compensated paths for experienced engineers.

How to get there (runtime)

If this role were a series, you usually don’t start in Go or Rust in Season 1. A realistic runtime looks like: 0-2 years as a backend developer in a more common language (often Python, JavaScript/Node, or Java) learning APIs, databases, debugging, and production basics; 3-5 years gradually specializing in Go or Rust on specific services and taking on more performance-critical work; and 5-8+ years before you’re a Senior Backend Developer trusted with the hardest concurrency and performance problems. For beginners and career-switchers, the most practical move is to first get solid on backend fundamentals - HTTP, REST, SQL, testing, logging, deployment - then layer Go or Rust on top once you’re comfortable. AI tools can absolutely help you explore these languages and read complex error messages, but they’re not a substitute for understanding what your code is doing with threads, memory, and CPU when the traffic really hits.

How to Use This Top 10 Without Letting It Use You

The Top 10 row is still glowing on the screen. You could just hit play on #1, but by now you know the trick: a ranking compresses everything into a single number and a glossy thumbnail. Salary lists and job boards do the same thing with careers, turning years of work and tradeoffs into a neat “median comp” line. The goal isn’t to ignore those numbers, but to read them the way you’d watch a trailer - enough to see if it’s your kind of story, not a contract you sign with your life.

Read rankings like trailers, not scripts

Every salary range you’ve seen here is real, but it’s also a highlight reel. That $300,000 - $600,000+ total compensation you keep noticing usually belongs to Senior → Staff → Principal engineers with 8-12+ years experience, working in tech hubs and often at companies that pay a lot of their compensation in stock. Tools like Levels.fyi’s compensation dashboards make it clear how wide the spread is by level and company, even at the same job title. Instead of asking “How do I jump straight to that number?”, a more useful question is “What skills and seasons of experience sit between where I am now and that kind of responsibility?”

Focus on the shared fundamentals, not just the fancy titles

Across all ten roles - whether it’s Principal DevOps, MLOps, or Go/Rust specialist backend - the same core foundations keep showing up: strong backend skills in languages like Python, Go, or Java, comfort with SQL and databases, fluency with Linux, Git, Docker, CI/CD, and at least one cloud provider, and enough security and networking knowledge to not be dangerous. That lines up with independent analyses like this in-depth guide to in-demand technical skills, which consistently ranks cloud, DevOps, data, and security fundamentals near the top of employer wishlists. AI tools slot in as your recommendation engine and assistant - helping you write code, read logs, or sketch designs faster - but they don’t replace the need to understand what your systems are actually doing.

Pick your next episode, not your final season

Here’s the honest summary: yes, those $300k-$600k+ packages exist; yes, AI is raising the bar for entry-level roles while creating new niches like MLOps and DevSecOps; and yes, you can start from scratch now and move toward any of these paths - but it’s a multi-season arc, not a one-month montage. If you’re in your own “Season 1,” the win is not landing a Staff title tomorrow; it’s getting solid enough at backend and DevOps basics to land that first role. Programs like Nucamp’s back-end and DevOps bootcamp are explicitly designed for that: 16 weeks, about 10-20 hours per week, focused on Python, PostgreSQL, CI/CD, Docker, and cloud deployment, plus 5 weeks of data structures, algorithms, and interview prep in small cohorts (max 15 students), with early-bird tuition around $2,124 instead of the $10,000+ price tags many bootcamps charge. However you choose to learn - bootcamp, self-study, college - the key is the same: stop endlessly scrolling the rankings, pick a realistic next episode that builds those fundamentals, and let the big titles and big numbers show up as the end credits of years of steady, focused work.

Frequently Asked Questions