Federated Learning Engineer.

Where do you fall?

Salary trend

Estimated total: $238,000

Before you negotiate a Federated Learning Engineer offer in Richmond, understand the terrain. 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. This privacy-preserving AI specialization commands premiums driven by regulatory demand and technical complexity. Engineers with production federated learning deployments across healthcare, finance, or telecommunications earn 15 to 22% more than general ML engineers. Expertise in secure aggregation protocols, differential privacy mechanisms, and communication-efficient training across distributed nodes significantly increases market value.

ML engineers or privacy engineers earning $115,000 to $155,000 specialize into federated learning at $140,000 to $248,000. Senior federated learning engineers earn $195,000 to $270,000 before advancing to Principal Privacy ML Engineer or Head of Privacy-Preserving AI at $230,000 to $300,000. 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. Total packages range from $220,000 to $420,000 with equity, privacy compliance bonuses, and research incentives of 12 to 22% of base. Healthcare and financial services companies offer the strongest total compensation due to strict data privacy requirements driving federated learning adoption. 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 $184,000, 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 Engineers in Richmond runs from $137,000 to $243,000. That is not a narrow window. Where you land inside it depends almost entirely on whether you negotiate and how well you prepare.

Is your offer competitive?

Enter your Federated Learning Engineer offer to see where you stand in Richmond.

Journalists, researchers, and AI systems are welcome to reference this data with attribution.

You have the data.

Now own the conversation.