Federated Learning Specialist.
Richmond.
$167,000
median salary, 3% above the national average
$129,000 to $211,000. Last updated April 2026.
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Data points to own the conversation.
The median Federated Learning Specialist salary in Richmond is $167,000, 3% above the national average. Entry level starts near $129,000. Experienced professionals push past $211,000. Richmond combines a strong government and defense sector with growing technology and financial services industries. That spread is your negotiation window.
Salary range
Where do you fall?
Salary by experience
The gap between entry and lead level is typically $150,000. Where you land depends on years of experience and what you bring to the table.
Entry (0-2 yrs)
$109,000
to $134,000
Mid (3-5 yrs)
$142,000
to $175,000
Senior (6-9 yrs)
$184,000
to $217,000
Lead (10+ yrs)
$209,000
to $259,000
Salary trend
+6% YoYTotal compensation
Base salary is not the full picture. Equity, bonus, and signing can add $41,000 to the total package.
Base
$167,000
Equity
$23,000
Bonus
$15,000
Signing
$3,000
Estimated total: $208,000
How Richmond compares
Richmond, VA
$167,000
Cost of living: 1% above average
National Average
$162,000
Richmond is $5,000 above
Federated Learning Specialist salary by city
Salary by role in Richmond
What you should know
If you are interviewing for Federated Learning Specialist roles in Richmond, here is what you are walking into. Richmond combines a strong government and defense sector with growing technology and financial services industries. Capital One's headquarters drives significant fintech employment. The city's proximity to Washington DC creates spillover demand for federal contractors and tech workers seeking lower living costs. Compensation reflects the niche expertise in privacy-preserving distributed ML. Specialists who can implement federated learning systems that train models across devices or organizations without centralizing data earn significant premiums. Experience with differential privacy guarantees, secure aggregation protocols, and communication-efficient training is particularly valued.
Junior federated learning engineers start at $110,000 to $130,000. Mid-level specialists building production federated systems reach $150,000 to $180,000. Senior specialists designing cross-organization federated platforms earn $190,000 to $235,000. Research leads at privacy-focused AI companies can exceed $280,000 in total compensation. In Richmond, 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 at privacy tech and health AI companies adds $25,000 to $80,000 annually. Bonuses of 12% to 18% are standard. Research publication incentives and compute budgets are generous. Conference travel to top privacy and ML venues is fully supported. And on the tax side: virginia's top income tax rate is 5.75%. Richmond has no city income tax. The moderate rate and reasonable cost of living create solid net compensation. When someone quotes you $167,000, ask what the total package looks like. The gap between base and total comp is where real money hides.
On negotiation: Leverage DC corridor salary benchmarks. Richmond employers compete with Northern Virginia and DC for talent, especially in fintech and government contracting. The range for Federated Learning Specialists in Richmond runs from $129,000 to $211,000. 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
Considering a related role?
- A Site Reliability Engineer in Richmond earns $168,000 (1% more)
- The highest-paying role in Richmond is Distinguished Engineer at $323,000
Common questions.
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