How to Use Our Free Salary Estimator (and What the Numbers Really Mean)

The number in your head is probably wrong

Ask ten people what their role pays and you'll get ten confident answers, most of them traced back to a forum thread from three years ago. I've done this enough times to be suspicious of my own intuition. The figure I remember is usually what a friend earned at one company, in one city, at one point in the hiring cycle, and I then treat it like market data. What you actually need is a defensible range: a low you'd be right to turn down, a midpoint that reflects the market for your scope in your location, and a top end that's reachable with the right skills or the right employer.

That's exactly what the free salary estimator produces, in about ten seconds. It's deliberately a small tool. Sixteen role families, six experience bands, thirty-plus locations grouped by country, six optional skill bumps, and a switch between rupees (LPA) and dollars. Nothing to sign up for, no email capture, and it all runs in your browser — your inputs don't get posted anywhere or stored. This post walks through how to use it properly, how to read what comes back, and where it will mislead you.

What happens when you press "Estimate salary"

There's no black box here, and knowing the arithmetic makes the output far more useful. Four things get multiplied together.

1. A role benchmark. Each of the sixteen families carries a US-benchmarked median for your experience band, in gross annual cash. Software engineer at mid level sits at $105K, teaching at mid level at $56K, sales at $82K. That's the starting point, and it's why picking the right family matters more than anything else you touch.

2. A location factor. Your city scales that benchmark for local cost of living and how hard it is to hire there. San Francisco is 1.32 (above the US average), London 0.85, Berlin 0.72, Dubai 0.85, Singapore 0.62, Bengaluru 0.175, Hyderabad and Pune 0.15, tier-2 India 0.09. Divide San Francisco's 1.32 by Bengaluru's 0.175 and you get a little over seven times for nominally the same job. That gap is the whole reason a national figure is useless for a local decision.

3. A seniority multiplier. Entry 0.72, junior 0.88, mid 1.00, senior 1.18, lead or manager 1.35, executive or director 1.60. It compounds with the band-specific benchmark, so moving from mid to senior in the same city is a real jump, not a rounding error.

4. A skills premium. The optional chips add up: AI or machine learning 15%, cybersecurity 10%, cloud 8%, data engineering 8%, product management 7%, DevOps or platform 7%. The total is capped at 25%, because a person with six hot skills isn't 60% more valuable than one with three.

Then the midpoint gets wrapped in a range: low is 80% of it, high is 125%, and every figure is rounded to the nearest $500 so nothing looks falsely precise. Rupee output converts at a flat ₹84 to the dollar. The honest accuracy claim: expect the answer to land somewhere within 15–20% of a realistic market offer for your profile. Which means the range is the result, not the midpoint.

Using it step by step

1. Open the estimator. It's at /salary-estimator, and it loads straight to the form. Software Engineer is preselected, with Mid level and Bengaluru — so if that's not you, the first thing to do is change it.
2. Pick the role family, not the job title. The dropdown is grouped by what the work actually is. If your title says "Business Analyst" but you spend the day writing SQL and dashboards, compare the Data family against Operations and choose whichever description matches your responsibilities. This one input moves the number more than everything else combined.
3. Choose your band by scope, not by calendar. Someone with four years who owns a service end to end is not the junior median. Someone with nine years doing the same work they did at three is not the senior median either. Use the years label as a hint and the day-to-day ownership as the real answer. If your title and your years disagree, go with the responsibilities.
4. Set the location. The dropdown is grouped by country, and several options are deliberately paired (Hyderabad / Pune, Madrid / Barcelona, Lisbon / Athens) because they're one market for our purposes. If you're remote, pick the remote option for your country rather than your city — US Remote sits at 0.97, right on the national average, which is roughly how remote pay actually behaves.
5. Add only skills you'd defend in an interview. The chips are labelled with their boost, so tick the ones you can prove on a resume or in a technical round. Don't tick Cloud because you deployed one hobby project; the premium is meant to reflect scarce, hireable depth.
6. Switch currency. INR shows LPA (and a monthly gross figure), USD shows thousands. Pick the currency your offer will be written in, not the one you'd like to earn in.
7. Press "Estimate salary," then run two or three more scenarios. This is where the tool earns its keep. Change one input at a time — same role in another city, same city one band up — and watch how far the range moves. That comparison is more useful than any single answer. "Reset" clears your skill picks and the result if you want to start over.

Reading the result card

The header band gives you the big range, the midpoint, and the midpoint broken down monthly, so you can sanity-check it against your current payslip. Underneath, a gradient bar runs from conservative to top end with a marker at the middle — where you sit inside your own range is a negotiation question, not a data question. Three small cards explain how the number got built: the role benchmark band, your city's market factor as a multiplier, and the skills premium actually applied (or "None selected"). Read those three before you trust the headline.

At the bottom is a line in small grey text that says the estimate is directional, gross of tax, and based on published medians rather than your specific offer. Please read that line. It's the most important thing on the page.

Worked examples, with real numbers

Here's the same person — software engineer, Bengaluru, no skills selected — climbing the bands. These are midpoint figures the tool actually produces:

• Entry (0–2 yrs): about ₹6.30 LPA, roughly ₹52,500 a month gross.
• Junior (2–4): about ₹10.1 LPA.
• Mid (5–7): about ₹15.5 LPA, with the range running near ₹12.6 to ₹19.3 LPA.
• Senior (8–12): about ₹23.9 LPA.
• Lead / manager (12–15): about ₹33.2 LPA.
• Executive / director (15+): about ₹50.4 LPA.

Same mid-level engineer, same skills, different pin on the map: Hyderabad or Pune lands near ₹13.4 LPA, Delhi NCR or Mumbai near ₹12.6 LPA, and tier-2 India near ₹7.98 LPA. That's a 70% swing inside one country for one job description, which is why "what does a developer earn" is a question without an answer until a city is attached.

For a US comparison, a senior data or AI/ML engineer working remotely at the US national average with AI/ML and cloud ticked comes out around $204K, with a range of roughly $163K to $255K and about $17,000 a month gross. Add cybersecurity as a third chip and you'd expect another chunk of growth — except the premium caps at 25%, so the midpoint only edges up to about $207K. The cap is intentional. Three chips beat one; six chips don't beat three.

And a deliberately modest one, because most people aren't engineers: a fresher doing writing or content work in tier-2 India sees a midpoint near ₹2.10 LPA, a range of roughly ₹1.68 to ₹2.52 LPA, and about ₹17,500 a month gross. Uncomfortably accurate, and a useful anchor if that first offer comes in below it.

Six ways to put it to work

• Before you type a number in a form. "Expected CTC" is the earliest question that can cost you money. Run the estimator the night before you apply and quote from the range instead of from your mood. Our salary negotiation guide has the exact sentence for doing that.
• When you're screening, not just applying. Two minutes before a call, rerun it for that company's city and level so you can hear immediately when their band is nowhere near the market.
• Comparing two offers in different cities. Run each one as its own scenario. The same nominal title can be worth dramatically more in one location than another, and the raw rupee or dollar figure hides that.
• Deciding whether a move is worth it. Same role, same band, new city: that's your relocation number in one click. Then ask whether the difference survives rent.
• Building the case for a raise. Your side of a review conversation is a number plus a reason. The estimator gives you the number and the factor cards give you the reason, in language a manager can forward.
• Pricing a skill before you spend a year on it. Tick and untick a chip to see the delta. If AI/ML moves your midpoint by 15%, that's the strongest business case for the course you're hovering over — and if the delta is small, you learn that too.

Where it will be wrong

Every one of these is a reason to treat the output as an opening position rather than a verdict.

• It's gross, not take-home. Base plus typical bonus, before tax, with no PF, insurance or gratuity structure. Your in-hand could be a fifth lower.
• Equity and variable pay are invisible. A startup's lower cash offer with real options and a service company's higher cash with nothing attached aren't the same deal, and this tool can't tell them apart.
• Company size, stage and industry aren't inputs. Product companies pay differently from services, and both pay differently from a funded startup in the same city. You're getting a market median, not your shortlist's median.
• The rupee conversion is a flat ₹84. Fine for an Indian salary in dollars. Genuinely misleading for comparing Lisbon to Bengaluru in LPA, because it ignores what rent and groceries cost. Use the USD toggle for cross-border calls, and then go look up cost of living separately.
• Bands overlap. If you sit between junior and mid, run both. The gap between them is your negotiation room, and it's better to know it than to guess.
• Sector blind spots exist. Sixteen families covers a lot of careers and not all of them. Government, public-sector undertaking pay, defense, academia-with-tenure and clinical specialties follow their own rules, so use the nearest family and hold the answer loosely.

From a number to an outcome

An estimate only pays off when it changes what you say. So take the range into the three places it actually gets spent: the expected-salary field, the screening call, and the offer conversation. Quote the range, attach a reason ("for this scope in Pune the market looks like X to Y"), and hand the question back. When they ask how you know, you have three factor cards to point at instead of a feeling.

The other lever is your document, and it's the faster one. A resume that shows scope and measurable results is what puts you at the top of the range rather than the middle of it. If the estimate came back higher than what you earn, that's the sequence: import your current resume so your history is already in the builder, sharpen the wording against the posting, run it through the ATS checker, and export a clean PDF. Our guide to quantifying your resume without inventing numbers pairs well with that, because "I improved performance" doesn't support a premium ask — "cut the nightly job from 40 minutes to 6" does.

Try it on your own profile

Open the salary estimator, put in your real role family, the band that matches what you actually own, your city, and only the skills you could prove in a technical round. Then do the part almost everyone skips: run the same profile in one other city and one band up, and compare. You'll leave with a defensible range instead of a hunch, which is a better position to negotiate from than nine out of ten candidates in the pipeline — and if the number surprises you, that's useful information before you're the one holding the offer letter, not after.