SWE Salary by Experience Explorer
Explore ESTIMATED SWE salary ranges by experience at FAANG, MANGA, and other top tech companies. Compare base, equity, and total compensation across levels.
| Years of Experience | Level (Typical) | Base Salary ESTIMATE (USD) | Total Compensation ESTIMATE (USD) | Equity Value ESTIMATE (USD) | Bonus ESTIMATE (USD) | Companies Sampled |
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The SWE Salary by Experience Explorer provides ESTIMATED compensation ranges for Software Engineers (SWEs) across different experience levels in top tech companies. This tool helps you compare SWE salary by experience trajectories at FAANG, MANGA, and other leading tech firms, using aggregated data from Levels.fyi, Bureau of Labor Statistics (BLS), LinkedIn Talent Insights, and Glassdoor.
Software engineering salaries vary significantly based on years of experience, company tier, job level, and geographic location. For example, an entry-level SWE at a FAANG company might earn a base salary of $130K–$160K, while a senior engineer with 6–8 years of experience could see total compensation exceeding $400K when equity and bonuses are included. These ESTIMATES reflect industry benchmarks but may not account for individual negotiation, specific company policies, or recent market shifts.
This explorer allows you to filter by company tier (FAANG, MANGA, or other Tier-1 tech) and location to see how experience correlates with compensation. The data reflects typical ranges rather than precise figures, as actual offers depend on factors like skillset, team demand, and economic conditions. For instance, a Staff Engineer at a FAANG company in the Bay Area typically earns 2–3x more than an industry-average peer with similar experience, driven largely by equity grants.
Use this tool to benchmark your current compensation, assess career growth opportunities, or evaluate job offers. Remember that equity ESTIMATES assume standard 4-year vesting schedules and may fluctuate based on company stock performance. Bonuses are typically 10–30% of base salary but can vary widely by role and company.
The BLS industry averages provide a broader context, showing how compensation trends compare outside top-tier tech companies. For example, the median SWE salary for 4–6 years of experience across all US companies is ~$150K, significantly lower than FAANG/MANGA ranges, illustrating the premium command by elite tech employers.
How It Works
This SWE Salary by Experience Explorer provides ESTIMATED compensation ranges based on two primary filters:
- Company Tier: Choose between FAANG (Meta, Google, Amazon, Apple, Netflix), MANGA (Microsoft, Adobe, Nvidia, Tesla, Palantir), or other Tier-1 tech companies. This adjusts the salary baseline to reflect the typical compensation structure at elite firms.
- Location: Select a geographic multiplier (e.g., 1.0x for San Francisco Bay Area, 0.9x for Seattle) to account for regional cost-of-living and labor market differences. The ESTIMATES assume Bay Area-level competition for talent, with adjustments for other metros.
The table displays year ranges (e.g., 2–4 years) rather than exact tenure, reflecting that levels and compensation are typically tied to experience bands rather than precise years. Each row shows:
- Base Salary: Fixed annual compensation.
- Total Compensation: Base + Bonus + Equity (ESTIMATE).
- Equity Value: ESTIMATED 4-year value of RSUs, assuming standard vesting schedules and current stock prices.
- Bonus: ESTIMATED annual cash bonus, typically 10–20% of base salary at FAANG/MANGA.
The "Companies Sampled" column indicates how many firms contributed to the underlying data for each row (higher = more reliable ESTIMATE).
Methodology Note
All compensation figures in this tool are ESTIMATES derived from multiple public datasets, not definitive offers or guarantees. Here’s how the data was compiled:
- Primary Source (FAANG/MANGA): Levels.fyi aggregated submission data (2022–2024), filtered for US-based SWEs with verified offers. Levels.fyi normalizes submissions for outliers, ensuring realistic ranges.
- Secondary Sources:
- Bureau of Labor Statistics (BLS): Occupational Employment and Wage Statistics (OEWS) for "Software Developers" (SOC Code 15-1252), providing industry-wide medians.
- LinkedIn Talent Insights: Aggregated compensation ranges for SWEs at various experience levels.
- Glassdoor: Reported salaries for benchmarking.
- Equity ESTIMATES: Assumes 4-year vesting schedules with linear interpolation. Values are based on Levels.fyi-reported ranges, using median company stock prices from the past 12 months. Equity values can fluctuate significantly based on market conditions.
- Location Adjustments: Multipliers are derived from Levels.fyi and LinkedIn data, showing typical regional differences (e.g., NYC pays ~90% of Bay Area rates for equivalent roles).
- Experience Bands: Levels are mapped to years of experience based on typical industry progressions, but career paths vary by individual.
This tool is designed for exploratory benchmarking rather than precise financial planning. For actual offers, consult specific job descriptions, company policies, and negotiated terms. The data does not account for specializations (e.g., ML Engineers vs. Frontend SWEs), which may command different compensation.
Frequently Asked Questions
- Base Salary: Fixed annual pay, typically ~60–80% of total compensation at FAANG/MANGA.
- Equity: Restricted Stock Units (RSUs) granted over 4 years, with value fluctuating based on company stock performance.
- Total Compensation: Base + Bonus + Equity (ESTIMATE). For senior roles, equity can constitute 40–60% of total compensation.
- The data represents median ranges, not guarantees.
- Equity ESTIMATES assume standard vesting and median stock prices, which can vary.
- Bonuses are ESTIMATED as 10–20% of base but can differ by role and company.
- Experience bands are generalized; career paths vary (e.g., some SWEs reach Staff level in 6 years, others take 10+).
- Specializations (e.g., Machine Learning vs. Frontend) may command different compensation.
Navigate Your SWE Career with Confidence
Compensation is just one piece of the puzzle. Explore our curated guides on negotiation strategies, leveling across companies, equity deep dives, and career planning to maximize your trajectory in Big Tech.
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