Deep Learning Engineer.
Phoenix.
$198,000
median salary, 1% below the national average
$153,000 to $252,000. Last updated July 2026.
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Data points to own the conversation.
The median Deep Learning Engineer salary in Phoenix is $198,000, 1% below the national average. Entry level starts near $153,000. Experienced professionals push past $252,000. Phoenix is one of the fastest growing metros in the U. That spread is your negotiation window.
Salary range.
Where do you fall?
Salary by experience.
The gap between entry and lead level is typically $178,000. Where you land depends on years of experience and what you bring to the table.
Entry (0-2 yrs)
$129,000
to $158,000
Mid (3-5 yrs)
$168,000
to $208,000
Senior (6-9 yrs)
$218,000
to $257,000
Lead (10+ yrs)
$248,000
to $307,000
Salary trend.
+8% YoYTotal compensation.
Base salary is not the full picture. Equity, bonus, and signing can add $100,000 to the total package.
Base
$198,000
Equity
$61,000
Bonus
$27,000
Signing
$12,000
Estimated total: $298,000
Deep Learning 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, state pay-transparency job postings, and community-submitted compensation data.
How Phoenix compares.
Phoenix, AZ
$198,000
Cost of living: 4% below average
National Average
$200,000
Phoenix is $2,000 below
Deep Learning Engineer salary by city.
Salary by role in Phoenix.
What you should know.
If you are interviewing for Deep Learning Engineer roles in Phoenix, here is what you are walking into. Phoenix is one of the fastest growing metros in the U.S., fueled by semiconductor manufacturing, financial services, and tech company expansions. Intel, TSMC, and other chipmakers have made massive investments in the area. The combination of affordable housing and rising salaries has made Phoenix increasingly attractive to relocating professionals. Pay hinges on expertise with transformer architectures, large-scale training pipelines, and GPU optimization. Engineers who have shipped production models at scale earn top-band salaries. Publications at NeurIPS or ICML and familiarity with distributed training frameworks like DeepSpeed further boost compensation.
Junior deep learning roles start at $120,000 to $145,000. Mid-level engineers earn $155,000 to $200,000. Senior and staff-level positions reach $220,000 to $300,000 base, with principal engineers at top labs exceeding $400,000 in total compensation. In Phoenix, cost of living sits near the national average, so the numbers you see are roughly what you keep.
Base salary is not the full picture. Equity is a major component at AI-focused companies, often representing 30 to 50% of total compensation. Annual bonuses range from 10 to 20% of base, with signing bonuses of $30,000 to $75,000 common for experienced hires. And on the tax side: arizona has a flat 2.5% income tax rate, one of the lowest in the nation. Phoenix has no additional city income tax, making overall take home pay very competitive. When someone quotes you $198,000, ask what the total package looks like. The gap between base and total comp is where real money hides.
On negotiation: Reference the influx of semiconductor and tech employers to justify higher offers. Phoenix salaries are rising fast, and employers know they must compete with coastal recruiters. The range for Deep Learning Engineers in Phoenix runs from $153,000 to $252,000. That is not a narrow window. Where you land inside it depends almost entirely on whether you negotiate and how well you prepare.
Sources: BLS OES, H-1B LCA (DOL), state pay-transparency job postings, and community submissions. COL: BEA Regional Price Parities (2025). Data verified by Justin Bartak, Founder & Chief AI Officer. Last verified July 1, 2026. Full methodology
Considering a related role?
- A Reinforcement Learning Engineer in Phoenix earns $200,000 (1% more)
- The highest-paying role in Phoenix is Principal Quantitative Researcher at $339,000
- See all ai engineering roles tracked in Orbyt Intelligence
Common questions.
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