Machine Learning Engineer Salary in Denver — 2026 BLS Data
Salary distribution
Percentile breakdown of Machine Learning Engineer base salaries in Denver.
Denver’s Machine Learning Engineer market sits in an interesting position: it’s well past the “emerging tech hub” phase — the metro has 27,010 software developers tracked by BLS OEWS May 2024 for SOC 15-1252, and ML engineers represent a growing and better-compensated slice of that pool — but it hasn’t been fully priced up to San Francisco or Seattle levels. The BLS places software developers in Denver at a median of $137,610 for May 2024. ML engineers, drawing on the same SOC code but consistently commanding a 12-20% market premium for AI/ML specialization, land closer to $155K-$160K at median base. The headline number is useful as a starting anchor. What it hides is more instructive.
What the median doesn’t tell you
The reported median of roughly $158,000 flattens a distribution that spans from $128,000 (P25) to $245,000 (P90). That’s a 91% gap between a quartile and a top-decile earner working the same job title, in the same city, at the same moment in time.
Several compression effects are baked into that single number. BLS OEWS groups ML engineers under Software Developers (SOC 15-1252) — a bucket that also includes junior web developers and mid-career full-stack engineers who have little ML on their resume. So the “Denver ML engineer median” you see from survey-based sources like Built In ($130,000 average base) or Indeed ($172,360) reflects different populations with different filtering criteria. The BLS number is comprehensive but blunt; the survey numbers are sharper but non-random.
The second compression issue is level. A brand-new ML engineer fresh from a graduate program, writing boilerplate training pipelines at a Denver-area insurance company, and a staff ML engineer building real-time recommendation systems at a Denver-headquartered fintech both show up as “machine learning engineers” in the data. The difference in pay is roughly 2x before equity. The BLS captures both and calls it a single number.
The practical takeaway: if you are a mid-level ML engineer with 3-5 years of production model deployment experience, the relevant benchmark is P75 ($198,000), not the median. The median describes an average of a population that includes a lot of people who aren’t your competition for the same roles.
Denver vs. the major ML hubs
Denver occupies what Signify Technology’s 2026 US Market Report categorizes as a Zone 3 tech market — alongside Austin and the Research Triangle — paying competitively but below the Zone 1 (San Francisco, New York) and Zone 2 (Seattle, Los Angeles) ceilings.
Here’s how mid-level (3-5 year) ML engineer base salary compares across hubs:
- San Francisco Bay Area: $187,000–$220,000
- Seattle: $160,000–$207,000
- New York City: $165,000–$200,000
- Austin: $145,000–$185,000
- Denver: $145,000–$185,000 (effectively tied with Austin at mid-level)
- Remote US: $145,000–$198,000
Denver tracks closely with Austin on base salary. The meaningful difference shows up at senior and staff levels: Denver has fewer seats at that tier because it has a smaller concentration of hyperscaler engineering offices. Senior ML engineers (6-9 years) in SF land $220,000-$275,000 base; in Denver the comparable range is roughly $175,000-$225,000. That’s still an attractive market, especially once cost of living enters the calculation.
One Denver-specific advantage: companies like Arrow Electronics, DaVita, Dish Network (now EchoStar), and a growing number of AI-native startups along the Boulder-Denver corridor have increased local ML hiring meaningfully since 2023. The presence of National Renewable Energy Laboratory (NREL) and several aerospace contractors (Lockheed Martin Space, Raytheon) also creates demand for ML engineers in non-consumer-tech verticals where pay bands are sometimes narrower but long-term compensation is more predictable.
What drives the spread: company tier, level, and specialty
Three variables explain almost all of the P25-to-P90 variance in Denver ML engineer pay.
Company tier is the dominant factor. A mid-level ML engineer at a public Fortune 500 company with a Denver engineering office — a role that’s been posted, leveled, and benchmarked against peer surveys — will land $155,000-$175,000 base with a 10-15% cash bonus and a modest equity package. The same engineer at a well-funded Series B startup might see $140,000-$160,000 base but receive equity that represents real upside if the company exits. At a government contractor or national lab, base lands $130,000-$155,000 with strong benefits and stability but essentially no equity. The differences are large enough that “where you work” matters more than almost any negotiation tactic.
Level compounds the effect. BLS doesn’t segment by level, but the market does:
- Entry-level / IC1 (0-2 years): $105,000-$140,000 base
- Mid-level / IC2-IC3 (3-5 years): $145,000-$185,000 base
- Senior / IC4 (6-9 years): $175,000-$225,000 base
- Staff / IC5 (10+ years): $208,000-$280,000 base
Each step is a meaningful jump, not a marginal increment. A mid-career engineer misclassified as junior by a company with poor leveling calibration is likely leaving $25,000-$40,000 per year on the table before the negotiation even begins.
Specialty is the third lever, and it’s become more powerful as the ML field has fragmented. In Denver’s market, the clearest premium areas are:
- LLM/GenAI engineering: fine-tuning, RAG pipeline architecture, model evaluation — currently commanding a 15-25% premium over traditional MLOps or classical ML roles
- Computer vision for aerospace/defense clients: clearance-eligible CV engineers can see 10-20% premiums reflecting the thin candidate supply
- MLOps and ML platform: senior engineers who can own the entire model-to-production lifecycle — not just training code but serving infrastructure, monitoring, and retraining pipelines — are in consistent demand and compensated above the generalist median
Traditional data science roles that involve more analysis and less production ML have drifted toward the $110,000-$135,000 range in Denver, converging with data engineer pay and below the ML engineering median.
Total compensation breakdown
For a typical mid-level (IC3) ML engineer at a Denver public company or well-funded private company, the breakdown looks like this:
- Base salary: $158,000. This is what BLS OEWS tracks and what appears on your W-2. Recruiters typically have 5-8% discretion on base within a published band; moving outside the band requires VP or compensation committee approval.
- Annual cash bonus: ~$18,000. Most Denver tech employers pay 10-15% of base as an annual performance bonus. At public companies with strong years this gets paid reliably; at Series B-C startups the payout is sometimes discretionary or tied to company-level milestones.
- Annualized equity: ~$28,000. This is the four-year RSU grant divided by four. At a public company, a typical IC3 ML engineer grant is $80,000-$130,000 over four years — so roughly $20,000-$32,000 per year. Pre-IPO startups offer larger nominal grants with higher risk; the equity may vest on the same schedule but the realized value at any point before exit is zero.
That sums to roughly $204,000 total annual compensation at the median. At P75, the picture shifts: base $198,000, bonus $22,000-$28,000, equity $45,000-$65,000 annualized — total in the $265,000-$290,000 range.
At staff level in Denver, total comp can approach $380,000-$450,000 at companies with strong equity programs, though the stock component makes this figure volatile year-to-year. Signing bonuses — typically $15,000-$30,000 at mid-level, $40,000-$70,000 at senior/staff — are paid once in year one and usually have a 12-month clawback.
Cost-of-living adjusted picture
Denver’s cost-of-living index of 113 (US average = 100, per ACCRA/C2ER data from 2024) means you’re paying about 13% above the national average for housing, goods, and services. Housing drives most of that premium — median home prices in Denver proper were hovering near $600,000 in 2024, and average one-bedroom rents ran $1,800-$2,100 per month.
How does that compare to other markets where ML engineers work?
| City | COL Index | $158K Denver equivalent |
|---|---|---|
| San Francisco | ~178.6 | $268,000 |
| Seattle | ~152.4 | $241,000 |
| New York City | ~168.0 | $266,000 |
| Austin | ~119.3 | $187,000 |
| Denver | 113.0 | $158,000 |
| US National Average | 100.0 | $140,000 |
A $158,000 Denver salary has the same real purchasing power as $140,000 in an average US city — or as $268,000 in San Francisco. That gap is substantial. An ML engineer choosing between a $185,000 Denver offer and a $220,000 SF offer should run this calculation explicitly: the SF premium of $62,000 in gross pay turns into a modest real advantage after COL adjustment, and that’s before accounting for state income tax (Colorado is 4.4% flat; California is up to 13.3%).
One frequent mistake: confusing nominal salary comparisons with real compensation. A lot of “I took a pay cut to move to Denver and don’t regret it” careers are actually flat or slightly ahead in purchasing power, not behind.
Three-lever negotiation playbook
Lever 1: Anchor to your specialty, not your last title. If you’re a generalist ML engineer pivoting into GenAI or MLOps work, the employer’s initial offer will often be benchmarked to the generalist ML median (around $145,000-$155,000 in Denver) rather than to the specialty premium (15-25% higher). Before negotiations start, inventory your concrete production experience — “I’ve deployed five models to production with latency SLAs under 100ms” or “I own our LLM evaluation pipeline” — and connect those directly to the role requirements. Specificity gives the hiring manager cover to go back to comp with a justification.
Lever 2: Push hardest on signing bonus. Denver companies recruiting from competitive tech markets — or trying to match competing offers from remote-first FAANG shops — have more flexibility on signing bonuses than on base or equity bands. Base requires HR band approval; signing is often within recruiting manager discretion up to a stated cap. If you have a competing offer with a meaningful signing component, or if joining requires breaking a vesting cliff at a current employer, the signing ask is the cleanest path to closing the gap without forcing a formal leveling review.
Lever 3: Negotiate a 12-month review clause. Denver companies are more likely than SF-based companies to give this. A clause guaranteeing a formal compensation review at 12 months (not just a standard performance review) creates a defined window to address any initial underleveling. If you join at the lower end of a band because the company couldn’t verify your production experience pre-hire, the 12-month review is the mechanism to correct that — but only if you negotiated it into the offer letter. Without it, the timeline for a real raise often stretches to 18-24 months in practice.
Data caveats
The BLS OEWS is the most methodologically rigorous US salary dataset available — it’s a mandatory survey covering tens of millions of workers, not a self-reported convenience sample. But it has specific limitations for ML engineers:
- No occupation-specific code. ML engineers are reported under SOC 15-1252 (Software Developers), which dilutes the signal by including non-ML roles. The Colorado-specific percentiles above are derived by applying published ML engineer market premiums to the BLS Denver software developer distribution (May 2024 data released July 23, 2025), then cross-validating against Glassdoor, Built In, and Signify Technology survey data. They are best estimates, not direct BLS reads.
- Equity is excluded entirely. BLS tracks wages and salaries but not equity compensation. For any role at a company with an active equity program, total comp is materially higher than the BLS-derived base numbers suggest.
- The data is lagged. May 2024 data describes wages paid in May 2024. The AI/ML labor market moved meaningfully in the 12-18 months since that snapshot — GenAI role premiums emerged sharply in 2024-2025 and are not yet captured in BLS OEWS headline figures.
- Colorado data had a delayed release. Colorado’s May 2024 OEWS estimates were released on July 23, 2025 — roughly three months after the national release — due to issues with the state’s unemployment insurance system modernization. This doesn’t affect the underlying data quality, but it’s worth knowing when citing the source.
For current spot-checks on ML engineer comp in Denver, supplement BLS with Colorado’s Equal Pay for Equal Work Act postings: since January 2021, Colorado employers must post salary ranges on job listings, making it one of the more transparent markets in the country for real-time compensation benchmarking. Filtering recent ML engineer postings on LinkedIn or Indeed for Colorado-based roles gives you a live sample of what employers are currently willing to pay — a useful cross-check against any survey source.