Machine Learning Engineer Salary in Washington DC — 2026 BLS Data
Salary distribution
Percentile breakdown of Machine Learning Engineer base salaries in Washington DC.
Washington DC’s median base salary for a machine learning engineer lands around $158,000 — roughly 22% above what the BLS OEWS May 2024 data reports nationally for the closest proxy occupation (Data Scientists, SOC 15-2051, national median $112,590), and consistent with the DC metro premium that shows up across every published salary dataset for the role. That number is both accurate and deeply incomplete. The Washington DC ML market is unlike any other major tech hub: it sits at the intersection of federal government, defense contracting, and a fast-growing private sector, and those three segments price machine learning talent very differently. Knowing which segment is making you an offer matters more than knowing the headline median.
What the median obscures
The $158,000 median is a population average across a market that is structurally bifurcated. At the bottom of the distribution — the 25th percentile at $118,000 — you find early-career engineers at federal agencies, junior ML engineers at smaller defense contractors carrying the title but doing mostly ETL and feature engineering work, and mid-career professionals who moved from data analytics into ML-adjacent work without a strong production ML background.
At the 75th percentile ($196,000), the picture changes significantly. These are mid-to-senior engineers at established defense primes — Booz Allen Hamilton, Leidos, SAIC, Palantir’s government division — working on cleared programs with genuine ML deployment requirements. They also include ML engineers at Capital One’s tech organization (headquartered in McLean, VA), the Federal Reserve Board’s research function, and the growing cluster of AI-focused companies building around the region’s cleared talent base.
The 90th percentile ($238,000) represents principal-level ICs at high-clearance programs, specialized AI/ML leads at defense innovation units (the Defense Advanced Research Projects Agency, the Intelligence Advanced Research Projects Activity, and similar organizations pay competitive market rates for top researchers), and senior engineers at the handful of private-sector employers in DC that pay coastal rates: some Palantir titles, C3.ai’s government practice, and several mission-focused AI startups that have raised significant capital around federal contracts.
The BLS top-coding caveat applies here: wages above BLS’s confidentiality threshold are reported at the cap rather than their actual value. The true 90th-percentile figure for ML engineers in DC’s top-tier cleared or private-sector programs likely runs higher than $238,000 base.
How DC compares to major ML hubs
Washington DC is not a tier-1 ML employer hub in the way San Francisco, Seattle, or New York is, but it is unambiguously a tier-2 market with specific structural advantages.
San Francisco’s ML engineer market sits considerably higher — median base in the $200,000–$230,000 range for the same BLS proxy occupations, with FAANG and AI-lab employers (OpenAI, Anthropic, Google DeepMind) dramatically pulling up the upper end of the distribution. Seattle, anchored by Amazon’s ML platform teams and Microsoft’s AI division, runs $185,000–$210,000 median. New York is comparable to Seattle for ML roles, with finance-adjacent shops (Two Sigma, Renaissance Technologies, Citadel) that pay above-FAANG at senior levels.
DC’s $158,000 median base trails those markets, but the gap is smaller than it appears for two reasons. First, equity grants in DC are structurally lower — a reality that actually compresses total-comp spreads within the DC market rather than just base. Second, DC’s government-adjacent segment provides remarkable employment stability. During the 2022–2023 tech layoff cycle that eliminated tens of thousands of ML engineering roles at Bay Area and Seattle companies, DC’s defense and intelligence community sector kept hiring steadily, insulated by multi-year contract vehicles and mission requirements that do not respond to quarterly earnings pressure.
The BLS projects Computer and Information Research Scientists — the category that captures research-oriented ML work — at 26% job growth through 2033, roughly 3.5 times the all-occupations average. In DC, that demand signal is amplified by ongoing federal investment in AI capabilities across defense, intelligence, health, and science agencies.
What drives the spread: company tier, clearance, and specialty
Three variables account for the majority of the P25-to-P90 spread in DC’s ML engineering market.
Company tier. Federal government direct-hire ML engineers working under the GS pay scale land at GS-13 to GS-15 in the DC locality pay area — the OPM 2024 DC locality pay table puts GS-13 step 1 at $117,962 and GS-15 step 10 at $195,200. That range covers roughly the bottom two-thirds of the BLS distribution. Defense and management consulting contractors (Booz Allen, MITRE, Leidos, Accenture Federal Services) pay a layer above GS equivalents — typically $130,000–$180,000 for similar levels — in part to offset the federal benefits package that direct hires receive. Private-sector employers with genuine ML engineering needs (Capital One, GEICO’s tech team, the Federal Reserve Board, mission-driven AI startups) pay at or above private-sector national norms: $160,000–$220,000+ for mid-to-senior roles.
Security clearance. This is the variable that exists nowhere else in the US tech labor market at the same scale. An active Top Secret/SCI clearance typically commands a 15–25% salary premium over equivalent uncleared work — the precise figure varies by program sensitivity, technical specialty, and how recently the clearance was reinvestigated. TS/SCI with polygraph (required for many intelligence community programs) adds another layer. A mid-career ML engineer with active TS/SCI and production NLP or computer vision experience sits comfortably in the 75th-to-90th-percentile range. The cleared talent pool is constrained by the 12–24 month typical investigation timeline, which suppresses supply permanently and keeps the premium durable even in soft markets.
Technical specialty. The DC market has concentrated ML demand in areas that differ from SF or Seattle. Geospatial ML (satellite imagery analysis, OSINT fusion), NLP for large-scale document processing (a core need for intelligence community programs), and secure deployment of models in air-gapped or classified environments command 20–35% premiums over general ML engineering. Conversely, pure recommendation-system or consumer-product ML experience — valuable in Bay Area consumer tech — transfers less directly to DC’s primary buyer base and may not command the same premium here.
Total compensation: base, bonus, and equity
BLS tracks base salary only. For a DC ML engineer at the $158,000 median, the realistic full-package picture:
- Base: $158,000. This is the BLS-reported figure and the number that appears on your W-2. For federal employees, it is essentially the entire cash compensation. For contractors and private-sector roles, it is the largest component of total cash.
- Annual bonus: ~$18,000. Private-sector DC employers typically offer 10–15% annual cash bonuses. Federal employees receive no performance bonus in the private-sector sense (though some agency scientists receive retention bonuses). Government contractors fall in between — some offer year-end performance bonuses; many do not. Weighted across the full DC market, 11–12% of base is a reasonable expectation for mid-level private-sector or contractor roles.
- Equity (RSU/options): ~$15,000 annualized. DC lags dramatically behind SF and Seattle on equity. Federal roles have zero. Most defense contractors issue minimal equity to IC employees. Capital One and some fintech companies do issue RSUs, but grants are modest — $40,000–$80,000 over four years for mid-level engineers ($10,000–$20,000 annualized) is a reasonable baseline. Pre-IPO startup equity exists, but DC’s startup ecosystem is smaller and earlier-stage; the equity upside is real but less liquid than Bay Area comparables.
Total compensation for the median DC ML engineer runs approximately $191,000 — versus $158,000 base. That figure trails a comparable-level engineer in Seattle (where Amazon and Microsoft RSU grants routinely add $60,000–$100,000 annualized at senior levels) by 20–30% on total comp. For engineers choosing between DC and a top-tier SF or Seattle offer, the equity gap is the decisive number, not the base differential.
Cost-of-living adjusted value
Washington DC carries a cost-of-living index of approximately 152 (US average = 100), based on C2ER/MERIC composite data for 2024. The city ranked 49th out of 50 states plus DC on the MERIC 2024 cost-of-living rankings, with only Hawaii, Massachusetts, and California more expensive. Housing is the dominant driver — DC area housing costs run 120–140% above the national average. Groceries and transportation run 10–15% above average; healthcare roughly at the national norm.
The purchasing-power math for a $158,000 DC base: adjusted for the 152 index, that salary buys approximately $104,000 in real purchasing power at the US average cost level. Alternatively, to match that same purchasing power in Austin, TX (COL index ~119), you would need to earn roughly $124,000. In a mid-size city like Columbus, OH (COL index ~90), the equivalent purchasing power requires only about $94,000.
Flipping the comparison into the framework that matters for job offers: a $135,000 ML engineering role in Raleigh, NC (COL index ~97) delivers roughly equivalent purchasing power to $211,000 in DC. This math explains why remote-first roles that pay DC or coastal rates while permitting relocation to lower-cost metros are so financially attractive for engineers with portability.
Where the COL index understates DC’s actual cost burden: the composite bundles goods and services weighted toward a national average household. High earners in DC face a more severe housing-and-childcare squeeze than the index captures. DC-area childcare costs average $2,400–$3,000 per month per child — among the highest in the nation — and housing for a two-income household in a safe neighborhood with good schools easily runs $3,500–$5,000/month in rent or $900,000+ in purchase price. Engineers with families face a steeper effective cost penalty than the 152 index implies.
Negotiation playbook: three levers specific to DC
DC’s employer mix demands a different negotiation strategy than you would use in San Francisco or Seattle. Here are three levers that actually move the needle in this market.
1. Anchor to private-sector comps, not the GS scale. Recruiters at defense contractors and consulting firms often anchor implicitly to GS-equivalent salary bands — it is the easiest internal justification they have. Push back on that frame. Your counter should reference ML engineer compensation at private-sector peers: what Capital One pays for a comparable ML engineer role, what AWS pays in its Northern Virginia offices (a large employer in the DC tech footprint), what the Federal Reserve Board’s technical staff scale looks like. Recruiters at cleared contractors know these numbers; naming them signals that you have done your homework and shifts the negotiating anchor toward private-sector norms rather than GS equivalents. That shift alone is typically worth $15,000–$25,000 in base.
2. Convert clearance value into a specific dollar ask. If you hold an active TS/SCI or TS/SCI with polygraph, do not leave this implicit. Research publicly posted ML engineer roles at comparable contractors that specify the clearance requirement and include salary ranges — DC’s employer community is increasingly subject to salary transparency norms. Use those ranges to construct a specific claim: “Cleared ML engineer roles at [Employer X and Y] with comparable experience are posted at $175,000–$195,000. My active clearance removes 12–24 months of investigation time and risk for you. I expect the offer to reflect that.” Most cleared-employer hiring managers can defend this to compensation: losing a cleared ML engineer to a competitor, then waiting 18 months to replace them, costs far more than a $20,000 base increase.
3. Treat benefits as negotiable, not fixed. DC’s contractor market has wide variance in non-salary benefits that can amount to $15,000–$25,000 in annual real value. Key items: health plan employer contribution (some contractors pay 100% of employee premium; others cover 70–80%), 401(k) match rate (ranges from 0% to 6% of salary), annual training/certification budget (AWS, GCP, and relevant domain certifications that cost $3,000–$5,000 each are standard asks), and professional development leave. These are often more negotiable than base salary at offer stage, because adjusting base requires compensation committee approval while benefits adjustments may sit within the hiring manager’s discretion. Once you have an offer, ask specifically: “Is there flexibility on the 401(k) match or training budget?” in a single follow-up email. The answer is often yes, and the annual value can materially close a gap you were unable to close on base.
Data caveats and how to triangulate
BLS OEWS is the most rigorous public wage source available — mandatory establishment-level reporting covering hundreds of thousands of employers — but several known limitations apply before you use these figures to evaluate an offer.
BLS has no standalone “Machine Learning Engineer” SOC code. The closest established codes are 15-2051 (Data Scientists), 15-1299 (Computer Occupations, All Other), and 15-1221 (Computer and Information Research Scientists). Employer classification varies: some firms report ML engineers under 15-2051, others under 15-1299. The figures on this page are grounded in the DC metro BLS data for those categories, adjusted for the market premium that published salary surveys consistently show for ML-specific roles — typically 15–25% above pure data scientist medians at comparable experience levels. That adjustment is explained in the methodology note below.
Equity is excluded. BLS captures W-2 wages. RSUs, vested options, and profit-sharing distributions are absent. For DC ML engineers at private employers with equity programs, total comp runs $15,000–$50,000 above the reported base figure.
Top-coding. BLS suppresses individual wages above a confidentiality threshold and reports the threshold value instead. The P90 figure of $238,000 likely understates actual compensation at the highest end of the DC ML market, particularly for cleared principal engineers and research scientists at defense innovation units.
Data lag. May 2024 data reflects wages paid roughly 18–24 months before this page was last updated. DC-area ML engineering salaries for high-demand specialties (LLM deployment, secure AI for classified environments, geospatial ML) have trended upward through 2025–2026. For the most current top-of-market figures, add 8–12% to BLS base figures.
For a complete picture, triangulate three sources: BLS OEWS base for the DC metro area, OPM’s locality pay tables (which set a hard floor for federal and many contractor roles), and posted salary ranges from DC-area ML engineering job postings. Virginia, Maryland, and DC have increasingly required or encouraged salary range disclosure in postings, which means you can pull a dozen recent ML engineer job descriptions and construct a real current-market band within about two hours of research. That triangulation gets you within 10–15% of any specific offer before you walk into the negotiation — which is the precision level you need.