GovTech ML Engineer.
San Francisco.
$205,000
median salary, 35% above the national average
$161,000 to $267,000. Last updated April 2026.
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Data points to own the conversation.
A GovTech ML Engineer in San Francisco earns a median of $205,000 in 2026. That is 35% above the national average. The range runs from $161,000 to $267,000, and where you land depends on your experience, your skills, and how well you negotiate. Compensation for GovTech ML Engineers is driven by depth of technical specialization, open-source or published work, and the specific technology stack.
Salary range
Where do you fall?
Salary by experience
The gap between entry and lead level is typically $185,000. Where you land depends on years of experience and what you bring to the table.
Entry (0-2 yrs)
$133,000
to $164,000
Mid (3-5 yrs)
$174,000
to $215,000
Senior (6-9 yrs)
$226,000
to $267,000
Lead (10+ yrs)
$256,000
to $318,000
Salary trend
+7% YoYTotal compensation
Base salary is not the full picture. Equity, bonus, and signing can add $42,000 to the total package.
Base
$205,000
Equity
$23,000
Bonus
$15,000
Signing
$4,000
Estimated total: $247,000
How San Francisco compares
San Francisco, CA
$205,000
Cost of living: 35% above average
National Average
$152,000
San Francisco is $53,000 above
GovTech ML Engineer salary by city
Salary by role in San Francisco
What you should know
The GovTech ML Engineer landscape in San Francisco is not what most salary sites will tell you. San Francisco is the epicenter of venture capital and startup innovation, consistently producing the highest tech salaries in the nation. The city's concentration of AI labs, SaaS companies, and fintech firms creates intense competition for talent. Despite remote work trends, SF still commands the steepest salary premiums for engineering and product roles. Compensation for GovTech ML Engineers is driven by depth of technical specialization, open-source or published work, and the specific technology stack. Equity is a major component at roughly 25% of base — candidates should weight stock grants as heavily as salary when comparing offers. Within tech-sector GovTech ML Engineers specifically, employer tier (FAANG and frontier-AI labs vs mid-stage startups vs traditional enterprise) drives 66%+ variance across the compensation band.
GovTech ML Engineers typically progress Junior → Mid → Senior → Staff → Principal over 8 to 12 years, with the Staff+ levels carrying significant technical scope and cross-team influence. The director/VP track diverges around year 8 for those who choose management; IC staff-plus roles keep building technical depth. In San Francisco, those numbers run higher. The cost of living here is 35% above average, and employers adjust to compete.
Base salary is not the full picture. Total compensation for GovTech ML Engineers runs roughly $219K at median when factoring base + equity (25% of base annually) + bonus (15% of base). Equity is the single largest non-base component — candidates should model vesting schedules (typically 4-year with 1-year cliff) and compare grant values across offers carefully. At tech companies specifically, equity and sign-on are often the largest delta between offers — two roles with matching base can differ by $100K+ at total when equity is included. And on the tax side: california's top marginal state income tax rate is 13.3%, the highest in the U.S. San Francisco has no additional city income tax, but overall tax burden remains steep. When someone quotes you $205,000, ask what the total package looks like. The gap between base and total comp is where real money hides.
On negotiation: Leverage competing offers aggressively. SF employers expect candidates to shop around, and matching or beating a rival offer is standard practice here. The range for GovTech ML Engineers in San Francisco runs from $161,000 to $267,000. That is not a narrow window. Where you land inside it depends almost entirely on whether you negotiate and how well you prepare.
Sources: SEC filings, H-1B LCA (DOL), BLS OES, 50+ job posting platforms. COL: BEA Regional Price Parities (2025). Data verified by Justin Bartak, Founder & Chief AI Officer. Last verified April 8, 2026. Full methodology
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