Computer Vision Engineer.
San Jose.
$241,000
median salary, 34% above the national average
$181,000 to $315,000. Last updated April 2026.
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Data points to own the conversation.
San Jose is 38% more expensive than the national average. For Computer Vision Engineers, that shakes out to a median of $241,000, with the full range spanning $181,000 to $315,000. Expertise in specific application domains (autonomous vehicles, medical imaging, manufacturing inspection) drives the largest salary variation. Know the range before you walk in.
Salary range
Where do you fall?
Salary by experience
The gap between entry and lead level is typically $217,000. Where you land depends on years of experience and what you bring to the table.
Entry (0-2 yrs)
$157,000
to $193,000
Mid (3-5 yrs)
$205,000
to $253,000
Senior (6-9 yrs)
$265,000
to $313,000
Lead (10+ yrs)
$301,000
to $374,000
Salary trend
+9% YoYTotal compensation
Base salary is not the full picture. Equity, bonus, and signing can add $99,000 to the total package.
Base
$241,000
Equity
$60,000
Bonus
$25,000
Signing
$14,000
Estimated total: $340,000
Computer Vision Engineer salary by company
Total compensation estimates include base, equity, bonus, and signing. Ranges reflect mid to senior level. Derived from public H-1B LCA filings, SEC proxy statements, and aggregated self-reported data.
How San Jose compares
San Jose, CA
$241,000
Cost of living: 38% above average
National Average
$180,000
San Jose is $61,000 above
Computer Vision Engineer salary by city
Salary by role in San Jose
What you should know
The Computer Vision Engineer landscape in San Jose is not what most salary sites will tell you. San Jose is the capital of Silicon Valley, home to Apple, Google, Adobe, and Cisco. The city consistently ranks among the highest paying metros in the country for technology roles. Competition for talent is fierce, with employers offering aggressive compensation packages including equity, signing bonuses, and premium benefits to attract and retain engineers. Expertise in specific application domains (autonomous vehicles, medical imaging, manufacturing inspection) drives the largest salary variation. Engineers who can deploy real time inference systems with strict latency requirements earn more. Experience with 3D vision, video understanding, and edge device optimization commands significant premiums over basic image classification skills.
Junior CV engineers start at $120,000 to $150,000, reaching mid level at $160,000 to $205,000 in two to three years. Senior CV engineers earn $205,000 to $275,000. Staff engineers and research leads at companies like Waymo or in medical imaging can exceed $400,000 in total compensation. In San Jose, those numbers run higher. The cost of living here is 38% above average, and employers adjust to compete.
Base salary is not the full picture. Equity at autonomous vehicle and robotics companies can add $40,000 to $150,000+ annually. Bonuses of 15 to 25% are standard. Signing bonuses of $30,000 to $80,000 are common. Benefits frequently include access to specialized hardware, conference sponsorship, and patent filing bonuses. And on the tax side: california's top 13.3% state rate applies fully here. While gross salaries in San Jose are among the nation's highest, the tax burden means net pay may not stretch as far as expected. When someone quotes you $241,000, ask what the total package looks like. The gap between base and total comp is where real money hides.
On negotiation: Always negotiate equity alongside base salary. San Jose employers expect candidates to evaluate total compensation including RSUs, and leaving equity on the table is leaving money behind. The range for Computer Vision Engineers in San Jose runs from $181,000 to $315,000. 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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Common questions.
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