Tech Salaries Ranked: Every Major Role, USA and Canada

Tech Salaries Ranked: Every Major Role, USA and Canada

Senior machine learning engineers and cybersecurity architects sit near the top of most tech salary tables, with indicative senior-level ranges commonly landing between $170,000 and $260,000+ in the United States, while entry-level QA and technical writing roles commonly start closer to $55,000–$80,000. Every figure below is a range, not a promise.

Updated for 2026. Salary figures are indicative ranges assembled from public aggregates and move quarterly — always cross-check against a current source before negotiating.

How This Ranking Was Built

This table orders roughly twenty tech roles by their typical senior-level range midpoint, using the pattern found across four source types: Levels.fyi (self-reported offers, strong at large tech companies), the U.S. Bureau of Labor Statistics (broad, lagging, but methodologically solid), the Stack Overflow Developer Survey (large sample, global, self-reported), and Glassdoor/Indeed aggregates (wide coverage, noisier).

None of these sources agree exactly. All four broadly agree on the order, which is what a ranking like this actually needs — the exact dollar figure in any single cell should be read as "somewhere in this neighborhood," not as a quote.

The Ranked Table

All figures are USD base salary, excluding bonus and equity, for the United States. Canada typically runs 15–25% lower in nominal CAD terms even before currency conversion — see the note below the table.

Canada note: apply roughly a 15–25% nominal reduction in CAD terms across every row above, before converting currency. A Canadian senior software engineer at C$140,000–C$185,000 is a typical range rather than an outlier, and remote roles paid by US employers are the main way Canadian engineers close the gap. See the country-by-country breakdown in the worldwide article linked below for the full picture.

Reading the Table Correctly by Level

Entry range assumes zero to two years of professional experience, usually including internships, and typically corresponds to a first full-time role after a bootcamp, degree, or self-taught portfolio. Mid range assumes two to five years with demonstrated independent ownership of features or projects, not just task completion. Senior range assumes five to eight years with a track record of leading design decisions, mentoring, and handling ambiguous problems without close supervision. Titles drift: a company might call someone "senior" at three years or "mid-level" at seven, so read the range by actual scope of responsibility rather than by whatever title sits on a specific job posting.

The gap between entry and senior within a single role is often larger than the gap between two adjacent roles at the same level. A senior backend engineer's range overlaps more with a senior data scientist's range than it does with an entry-level backend engineer's range. This is normal and reflects how much of tech compensation is driven by demonstrated scope rather than by which specific skill you apply that scope to.

How Company Type Moves You Within a Row

The table above assumes a typical mix of employer types. In practice, four employer categories pull the same role's pay in different directions:

This is why two engineers with an identical title and years of experience can see a forty to sixty percent difference in actual pay. The row in the ranked table tells you where the market sits broadly; the employer type tells you where you personally are likely to land inside that row.

A Note on Job Titles Across Employers

Titles are not standardized across the industry, which is one reason a ranking like this has to work at the role-description level rather than the exact title level. "Software Engineer II" at one company can mean the same scope as "Senior Software Engineer" at another, and a startup with a flat structure might call its most experienced individual contributor simply "Engineer" with no seniority qualifier at all. When comparing an offer against a table like this one, read the actual responsibilities listed in the job description — ownership of a system, people managed, cross-team scope — rather than matching titles literally. The tech company levels explained article in this series maps common internal leveling systems across several well known employers if you need a more precise translation.

Why the Order Shifts Year to Year

Rankings like this are not fixed physics. Three forces move rows up and down every twelve to twenty-four months:

• Hiring demand cycles. Machine learning hiring surged industry-wide as generative AI products scaled, pulling that row's range up faster than most others in the last two years.
• Supply of qualified candidates. Roles with a thin, hard-to-train talent pool (security architecture, senior SRE) hold premium pay even during broader slowdowns, because there simply are not enough experienced people.
• Company funding conditions. When venture funding tightens, senior and staff-level ranges compress first, because equity-heavy total compensation packages get renegotiated toward base salary or shrink outright.

Why Machine Learning and Security Roles Sit at the Top Right Now

Two forces explain why machine learning engineering and cybersecurity architecture currently sit near the top of most senior-level ranking tables. Machine learning demand grew faster than the supply of engineers who can both build a working model and productionize it reliably at scale, and that gap has not closed even as more engineers pick up applied ML skills through courses and on-the-job exposure. Companies racing to ship generative AI features have, for the last several years, been willing to pay a premium specifically for engineers who can bridge research-adjacent model work and production software engineering, a combination that remains genuinely scarce relative to demand.

Cybersecurity architecture commands premium pay for a different, more structural reason: the consequences of getting it wrong are severe and highly visible, regulatory requirements around security have tightened across many industries, and the talent pipeline for senior security roles is thinner than for general software engineering because the role requires years of exposure to real incidents that cannot be fully simulated in training. Both premiums are real but neither is permanent. Machine learning pay growth is already showing signs of moderating in the aggregate data as more engineers enter the field, and security pay premiums have historically narrowed somewhat once a talent pipeline matures, though that narrowing tends to happen slower than in most other tech specializations.

Why the Bottom Rows Still Pay a Living Wage

It is worth being honest about the bottom of this table too. Roles like technical writing, QA automation, and IT support sit at the lower end of typical tech compensation, but "lower" in a tech-industry ranking is still solidly middle-class or better pay in most of the United States and Canada, and these roles carry real advantages the higher-paying rows do not always offer: often gentler on-call expectations, frequently more predictable hours, and in several cases a shorter, less expensive path into the industry from a non-technical background. A technical writer or QA engineer who is genuinely good at the role and stays in it for a decade routinely earns more, with far less career volatility, than someone who chases the top of this table through a sequence of unstable, high-churn jobs.

The point of ranking roles is not to suggest everyone should aim for row one. It is to give an honest, source-backed picture so that whichever row you are considering, you are working from a realistic number rather than either an inflated social-media claim or an unnecessarily pessimistic guess.

What These Numbers Do Not Include

• Bonus and equity. Total compensation at large public tech companies frequently runs 20–60% above base salary once RSUs and annual bonus are included — sometimes far more at the staff-plus level.
• Benefits. Health insurance quality, 401(k)/RRSP matching, and paid leave vary enormously between employers and are not captured in any base salary figure.
• Cost of living. A $140,000 salary in a mid-size US metro buys a meaningfully different life than the same number in San Francisco or New York.
• Taxes. State/provincial income tax differences alone can shift take-home pay by 8–13 percentage points between locations, before any federal comparison.
• Company size and stage. A "senior software engineer" at a ten-person startup and one at a public company with 40,000 employees are not comparable roles despite the identical title.

How Negotiation Moves You Within a Row

Two candidates with identical experience, in the same role and location, can land at meaningfully different points within the same range purely based on how the negotiation was handled. Having a competing offer in hand is the single most reliable lever, since it gives a hiring manager a concrete number to justify matching or beating internally, rather than an abstract request to "pay more." Timing also matters: negotiating after a verbal offer but before signing carries far more leverage than trying to renegotiate salary a few months into a new job, when the employer has less structural flexibility and the conversation shifts from "attracting a candidate" to "adjusting an existing employee," a fundamentally different internal process at most companies.

Silence is also a legitimate negotiating tool that many candidates underuse. Asking for time to consider an offer, rather than accepting or countering immediately, signals that the number is being evaluated seriously and often prompts a hiring manager to proactively improve it before you even counter. None of this changes which row of the table your role sits in, but it reliably changes where within that row's range you personally land — and the difference between the bottom and top of a single row is often ten to twenty percent, which compounds meaningfully over a multi-year career through subsequent raises calculated as a percentage of a higher base.

How These Ranges Differ for Contractors and Freelancers

Every figure in the ranked table above assumes a standard full-time employee position with typical benefits. Contract and freelance arrangements for the same roles follow a different logic entirely: hourly or day rates for contractors are usually calculated to offset the lack of employer-provided benefits, paid leave, and job security, which means a contractor's effective hourly rate needs to run meaningfully higher than a full-time employee's equivalent hourly rate just to reach comparable total value. A contractor billing what looks like an impressive day rate may still be earning less, once gaps between contracts and self-funded benefits are accounted for, than the mid-point of the full-time range for the same role.

This distinction matters increasingly as more tech workers, particularly in software engineering and design, move between contract and full-time arrangements across a career rather than picking one path permanently. The contractor vs full-time pay article in this series works through the actual math of that comparison in detail, since a headline day rate and a headline annual salary are not directly comparable numbers without adjusting for the structural differences between the two arrangements.

The Five Mistakes People Make Reading a Table Like This

1. Treating the senior column as a floor for their own experience level. A senior range assumes years of demonstrated senior-level scope, not a job title alone. Titles inflate faster than actual scope does.

2. Ignoring that these are US-centric ranges. Applying this table directly to a local market outside North America, without adjusting for that country's own cost of living and hiring dynamics, produces a badly wrong expectation.

3. Anchoring to the single highest number they saw on social media. A viral compensation screenshot is one data point from one company at one moment, not the market.

4. Comparing base salary at one company to total compensation at another. This is the single most common way people talk themselves into a bad negotiating position or a bad job change.

5. Assuming the ranking is permanent. The row order shuffled meaningfully over the last five years and will shuffle again. Re-check before making a multi-year career bet on relative pay alone.

A Word on Benefits That Do Not Show Up in Any Salary Table

Two employers offering an identical base salary within the same row of this table can differ enormously in what surrounds that number, and these differences rarely make it into any public aggregate. Health insurance quality varies from plans with minimal deductibles and broad provider networks to high-deductible plans that shift real cost onto the employee, a difference that can be worth thousands of dollars a year depending on a family's actual healthcare usage. Retirement matching structures range from no match at all to a generous, immediately-vested employer contribution that meaningfully compounds over a career. Paid time off policies, parental leave length, and remote work flexibility all vary company to company in ways that a base salary figure cannot capture, yet materially affect the real value of accepting one offer over another at an identical headline number.

When two offers land at similar points within the same row, these surrounding benefits are frequently the deciding factor, and it is worth asking about them as directly and specifically during an offer conversation as you would ask about the base salary number itself.

How the Four Source Types Actually Differ

Understanding what each source type is good at, and where it is weak, makes the whole table more useful than treating "salary data" as one undifferentiated blob.

Levels.fyi collects self-reported offer and compensation data, concentrated heavily among large, well known technology companies where employees are more likely to know about and use the platform. Its strength is granular, near-real-time detail on total compensation, including equity, at the specific companies it covers best. Its weakness is coverage: smaller companies, non-tech industries, and less prominent employers are thin or absent, which skews any aggregate pulled from it toward the higher end of the market.

The U.S. Bureau of Labor Statistics surveys a broad, statistically representative sample across the entire economy, including small and non-tech employers that self-reported platforms miss entirely. Its strength is genuine representativeness at the national and metro level. Its weakness is a significant reporting lag, often six months to over a year, and job classifications that lump together roles a tech worker would consider quite different (a "software developer" category is broader than any single row in this table).

The Stack Overflow Developer Survey samples a large, global, self-selected group of developers who choose to respond, giving it breadth across countries and specializations that neither of the above sources matches. Its weakness is the same self-selection bias every voluntary survey has: respondents skew toward a particular kind of engaged, English-speaking, often more senior developer community member.

Glassdoor and Indeed aggregates pool self-reported salary data submitted by site users, offering wide coverage across employer size and industry. Their weakness is data quality control: submissions are unverified, sample sizes per specific company and role can be small, and the incentive to report is not evenly distributed (people are more likely to report either an unusually good or unusually bad number than an ordinary one).

None of these four sources is "the accurate one." Reading the pattern across all four, and noticing where they agree versus where they diverge, is the actual skill this article is trying to model.

How to Use This Ranking in an Actual Job Search

Start by finding your current or target role's row, then narrow using three of the more specific articles in this series before you set an expectation for a real negotiation. First, check the country or US city breakdown if location is a variable in your situation, since the difference between two cities in the same country can be larger than the difference between two adjacent rows in this table. Second, check the tech company levels article to translate a specific employer's internal title into the level bands used here. Third, before an actual negotiation conversation, read researching your market rate and the salary negotiation script, since knowing the range is only the first half of the work — knowing how to use it in a live conversation is the half that actually changes an offer.

A ranking like this is most useful as a sanity check on a specific offer you already have, not as a target you negotiate toward blind. If an offer sits meaningfully below the range for your role, level, and location, that is a reasonable, evidence-backed thing to raise in a conversation. If it sits meaningfully above, understand why before assuming it will hold — an unusually generous offer sometimes reflects an employer under hiring pressure rather than a durable new market rate.

How Layoffs and Hiring Freezes Affect a Ranking Like This

Layoff cycles and hiring freezes at large tech employers move a ranking like this in two ways that are worth separating. First, they can compress the top of a range temporarily, since a company under cost pressure typically slows senior and staff-level hiring and offers, the levels where equity and negotiation flexibility matter most, before touching entry-level hiring at the same intensity. Second, and less obviously, a layoff wave can increase the pool of experienced candidates available for a smaller number of open roles, which shifts negotiating leverage toward employers even for roles that are still actively hiring, independent of any change in the underlying nominal range.

This is why a ranking assembled from data spanning a full year, or several years, tends to smooth out short-term layoff-driven noise better than a snapshot taken during an acute hiring freeze. If you are evaluating this table during a period of visible industry layoffs, treat the senior and staff rows as somewhat more compressed toward the lower end of the stated range than they would be during a stronger hiring period, and expect that compression to ease as hiring conditions normalize, which they reliably have after every prior slowdown in this industry's history.

Every Focused Collection in This Series

This pillar ranks roles at a glance. The articles below go deep on one dimension each — country, city, specific role comparison, skill, or career decision.

By dimension:

• Software engineer salaries around the world
• Software engineer salary by US city, adjusted for cost of living
• AI engineer vs data scientist: salary and role comparison
• Cybersecurity salaries by role
• Cloud and DevOps salaries

By skill and career path:

• Highest paying tech skills
• Most in-demand tech skills
• Freelance skills that pay $50 an hour
• Fastest growing tech careers
• Tech careers without a CS degree

By stage and decision:

• Junior vs mid vs senior engineer: the real difference
• Tech company levels explained
• Contractor vs full-time pay
• Reading a tech job offer: equity and RSUs
• Career moves that actually raise your pay

By search and negotiation:

• Getting your first developer job
• Top remote tech jobs
• Salary negotiation script
• Researching your actual market rate
• Tech jobs most at risk from AI

🔗 Start with your role or country of interest — the ranking above is the map, not the destination.