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Overview

JobLeap provides comprehensive salary intelligence to help you understand market compensation, negotiate better offers, and make informed career decisions—all with cited, verifiable data sources. Get real-time compensation data for any role and location: 
"What's the average salary for a senior data scientist in Austin?"
"How much do machine learning engineers make in San Francisco?"
"Software engineer salaries in Seattle"

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Understanding Salary Data

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Compensation Components

What it is:
  • Fixed annual income
  • Guaranteed compensation
  • Basis for benefits calculations
Typical ranges by level:
  • Entry-level: 60k−60k-90k
  • Mid-level: 90k−90k-140k
  • Senior: 140k−140k-200k
  • Staff/Principal: 180k−180k-300k
Components:
  • Base salary
  • Annual bonus (10-30% typical)
  • Equity (stock options, RSUs)
  • Sign-on bonus
  • Other benefits value
Why it matters:
  • More accurate comparison
  • Reflects full package value
  • Critical for tech negotiations
Types:
  • RSUs: Restricted Stock Units (public companies)
  • Stock Options: Right to buy at strike price (startups)
  • ISO/NSO: Tax treatment variations
Vesting:
  • Typical: 4 years with 1-year cliff
  • Some: Front-loaded or back-loaded
  • Acceleration: Acquisition or performance
Valuation:
  • Public companies: Clear market value
  • Private startups: Speculative value
Types:
  • Annual/Performance: 10-30% of base
  • Sign-on: One-time, often 10k−10k-100k+
  • Retention: Keeps you from leaving
  • Referral: For successful hires
Considerations:
  • May be discretionary
  • Often tied to company/individual performance
  • Not guaranteed like base salary

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Salary by Location

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High-Compensation Markets

  • Bay Area
  • Seattle
  • New York City
  • Austin
  • Remote
San Francisco / Silicon ValleySoftware Engineer:
  • Entry-level: 120k−120k-160k base
  • Mid-level: 150k−150k-200k base
  • Senior: 180k−180k-280k base
  • Total comp can reach 300k−300k-600k+ at FAANG
Why higher:
  • Tech company concentration
  • High cost of living
  • Intense competition for talent

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Cost of Living Adjustments

Salary comparisons should account for cost of living:150kinSanFrancisco≈150k in San Francisco ≈ 105k in Austin (purchasing power)Use salary data in context with:
  • Housing costs (often 40-50% of income)
  • State/local taxes
  • Transportation expenses
  • General cost of living
JobLeap provides cited COL comparisons when discussing location-based salaries.

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Salary by Experience Level

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Years of Experience Bands

1

Entry-Level (0-2 years)

Software Engineer: 70k−70k-130k base
  • Recent grads, bootcamp grads
  • Junior positions
  • Learning and mentorship focused
2

Mid-Level (3-5 years)

Software Engineer: 100k−100k-170k base
  • Independent contributor
  • Owns features/projects
  • Mentors junior engineers
3

Senior (5-8 years)

Software Engineer: 140k−140k-240k base
  • System design ownership
  • Technical leadership
  • Cross-team influence
4

Staff/Principal (8+ years)

Software Engineer: 180k−180k-300k+ base
  • Company-wide impact
  • Technical strategy
  • Architectural decisions

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Salary by Role

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Technical Roles

RoleEntry-LevelMid-LevelSeniorLocation
Software Engineer80k−80k-120k120k−120k-170k160k−160k-250kNational avg
Data Scientist85k−85k-120k120k−120k-160k150k−150k-220kNational avg
ML Engineer95k−95k-140k140k−140k-190k180k−180k-280kNational avg
DevOps Engineer85k−85k-125k120k−120k-170k160k−160k-230kNational avg
Product Manager90k−90k-130k130k−130k-180k170k−170k-260kNational avg
Data Engineer85k−85k-125k120k−120k-170k160k−160k-240kNational avg
These are base salary ranges. Total compensation can be 20-100% higher when including bonuses and equity.

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How to Use Salary Data

1

Research Before Applying

Understand typical compensation for target roles and companies
2

Set Expectations

Know what’s realistic for your experience and location
3

Filter Opportunities

Focus on roles meeting your salary requirements
4

Evaluate Offers

Compare offers against market data

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For Negotiation

1

Know Your Market Value

Research comprehensive salary data with citations
2

Document Salary Ranges

Save multiple sources showing competitive compensation
3

Consider Total Comp

Don’t focus solely on base—evaluate full package
4

Prepare Justification

Use market data to support your ask
5

Be Ready to Walk

Know your minimum acceptable offer

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For Career Planning

1

Track Salary Trends

Monitor how compensation changes over time
2

Identify High-Value Skills

See which skills command premium salaries
3

Compare Career Paths

Evaluate earning potential of different roles
4

Plan Relocations

Understand salary differences across markets

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Factors Affecting Salary

High-value skills:
  • AI/ML expertise (+20-40%)
  • Cloud architecture (+15-30%)
  • Security specialization (+20-35%)
  • Niche technologies (+10-25%)
Example: “How do Python skills affect machine learning engineer salaries?”
Startups (< 50 employees):
  • Lower base, higher equity
  • More risk, more potential upside
Mid-size (50-500):
  • Balanced base and equity
  • Moderate risk/reward
Large Tech (500+):
  • Higher base, RSUs
  • Lower risk, proven value
FAANG/Big Tech:
  • Highest total compensation
  • Generous bonuses and equity
Highest paying:
  • Finance/Trading (+20-50%)
  • Big Tech (+15-40%)
  • Enterprise SaaS (+10-30%)
Moderate:
  • Healthcare tech
  • E-commerce
  • B2B startups
Lower (but meaningful work):
  • Non-profits (-20-40%)
  • Education (-15-30%)
  • Government (-10-25%)
PhD:
  • Research roles: +30-60%
  • Industry ML/AI: +20-40%
Master’s:
  • Data science: +15-25%
  • Engineering: +10-20%
Bootcamp/Self-taught:
  • Entry-level accessible
  • May start 10-20% lower
  • Skills matter more than credential

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Salary Negotiation Tips

Common Mistakes to Avoid:
  • Accepting first offer without negotiation (90%+ of offers are negotiable)
  • Focusing only on base salary (total comp matters more)
  • Not researching market rates (come prepared with data)
  • Negotiating too early (wait for offer)
  • Making demands vs. collaborative discussion
  • Forgetting about benefits, PTO, flexibility
Negotiation Best Practices:
  1. Always negotiate - Companies expect it
  2. Use market data - Cite JobLeap’s sourced salary information
  3. Know your worth - Quantify your impact and value
  4. Consider everything - Base, bonus, equity, PTO, remote work
  5. Be collaborative - “I’m excited about this role. Based on market data for [role] in [location], I was hoping we could discuss a range of X−X-Y”
  6. Get it in writing - Verbal offers aren’t final

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Salary Comparison Questions

Ask JobLeap to compare compensation: 
"Compare software engineer salaries: San Francisco vs. Seattle vs. Austin"
"How much less do remote positions pay than on-site?"
"Is it worth relocating to the Bay Area for the higher salary?"

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Next Steps

Interview Prep

Prepare to negotiate with confidence

Skills & Learning

Learn high-value skills to increase earning potential

Hot Jobs

Discover high-paying trending roles

Research Salaries

Start exploring salary data now
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