Machine Learning Engineer Salary in Dallas — 2026 BLS Data
Salary distribution
Percentile breakdown of Machine Learning Engineer base salaries in Dallas.
The BLS OEWS May 2024 data for the Dallas-Fort Worth metro doesn’t break out “Machine Learning Engineer” as a standalone occupation — the BLS classifies most MLE work under Data Scientists (SOC 15-2051) and, for more software-heavy roles, Software Developers (SOC 15-1252). For Data Scientists in the DFW area, BLS reports a P50 of $120,840. Machine Learning Engineers in practice earn a meaningful premium over that baseline — they combine statistical modeling depth with production engineering skills, and the market pays for both. Working from the BLS DFW anchor and triangulating with reported MLE ranges from multiple salary databases, the realistic P50 for a Machine Learning Engineer in Dallas in 2024-2025 sits around $130,000 in base salary. That’s the number to benchmark against. What it hides is more interesting than what it shows.
What the median doesn’t tell you
The $130K median is a population average covering engineers at wildly different stages of their career and at companies with wildly different MLE scopes. The BLS DFW data for Data Scientists runs from $84,710 at P25 to $172,160 at P90 — a two-times spread within a single metro and a single occupational title. For MLE specifically, that spread is at least as wide.
At the bottom of the distribution, you’ll find recent graduates at consulting firms doing batch prediction jobs and calling it ML, or domain specialists who trained a model once and maintain it in a notebook. At the top, you’ll find engineers building real-time inference pipelines at AT&T, American Airlines, or Toyota’s North American operations in Plano, where the work is genuinely specialized and hard to replace.
The P25 ($92,000) is not a bad salary in Dallas given the cost of living, but it’s not “machine learning engineer” pay in the sense that the title implies. Most engineers who spend 70%+ of their week doing actual ML work — feature engineering, model training, evaluation, deployment — are in the $110,000–$150,000 range by their third year. The $155,000 P75 is achievable within four to six years for someone who has shipped production models at scale and can speak to latency, throughput, and monitoring, not just offline metrics.
Dallas vs. other US machine learning hubs
Dallas is firmly in the second tier of ML markets, which is not the same as saying it’s a bad market. The distinction matters for calibrating your expectations and your leverage.
San Francisco and Seattle top the charts on base salary. BLS OEWS May 2024 puts San Francisco-area Software Developers (the higher-paying BLS bucket) at a P50 of roughly $220,000. Seattle runs $195,000-$210,000 at the median. New York sits around $195,000 for ML-adjacent roles. These markets have Amazon, Google, Meta, OpenAI, and the full ecosystem of AI-focused startups competing for the same engineers — that competition drives base up.
Dallas’s $130,000 median is roughly 35-40% below San Francisco. But the COL index for Dallas is 106 — just 6 points above the national average of 100 — while San Francisco’s index sits around 178. On a purchasing-power basis, a $130,000 Dallas salary is equivalent to roughly $219,000 in San Francisco. The gap nearly closes on a cost-adjusted basis.
Austin runs slightly higher than Dallas for MLE roles — typically $145,000-$165,000 median — partly because of the concentration of tech firms that relocated there post-2020 (Tesla, Oracle, Dell’s HQ) and the resulting demand spike. Austin’s COL index of approximately 119 partially offsets that advantage. Dallas’s lower COL and lower tax burden (Texas has no state income tax) make the real economic difference smaller than the headline numbers suggest.
Secondary hubs like Chicago ($135,000-$150,000 MLE median), Atlanta ($125,000-$140,000), and Denver ($130,000-$145,000) are roughly comparable to Dallas, with slight variation based on local industry mix. Remote-US roles from coastal employers benchmarked to “national” pay bands typically land in the $145,000-$175,000 range and are increasingly available to Dallas-based engineers who don’t want to relocate.
What drives the spread: company tier, level, and specialty
Three variables explain most of the gap between P25 and P90 in the Dallas MLE market.
Company tier
The employer matters more in Dallas than in San Francisco, where even second-tier companies pay near-market to compete for talent in a supply-constrained environment. In Dallas, the range is wider.
Enterprise and Fortune 500. Dallas hosts corporate headquarters for American Airlines, AT&T, Texas Instruments, Toyota North America, Kimberly-Clark, and Jacobs. These companies have growing data and AI teams but tend to pay in the $120,000–$160,000 range at mid-level. Titles are often “Senior Data Scientist” or “Applied ML Engineer.” Base salary is competitive; equity is limited to RSUs for director-and-above, with most IC-level MLE roles receiving only cash and 401(k) matching.
Regional tech and financial services. Companies like McKesson, Tenet Healthcare, and the large regional banks (capital markets and risk modeling) pay $130,000–$175,000 for MLEs working on fraud detection, credit risk models, or clinical decision support. These roles tend to have strong job security and good benefits, but the ML work is often constrained to specific use-cases.
High-growth tech. Dallas has a growing startup ecosystem — Perot Jain, Capital Factory, and the Frisco/Allen tech corridor. Pre-Series B companies may pay $110,000–$130,000 base with meaningful equity upside. Series C and later, like companies in the fintech or healthtech space, push $150,000–$190,000+ for senior MLE roles with real technical depth requirements.
Remote-first at coastal comp. An increasingly common scenario: an engineer living in Dallas working for a Bay Area or NYC employer at coastal pay bands. These roles land $165,000–$210,000+ base, which puts them at or above the Dallas P90. This segment is not captured well in city-level BLS data since the employer is remote.
Level and years of experience
BLS lumps all experience levels into one occupation code, which is why the spread looks so dramatic. A rough level map for Dallas:
- Entry (0-2 years): $85,000–$105,000. Primarily model training and evaluation support, some MLOps tasks.
- Mid-level (3-5 years): $110,000–$145,000. Owns full model lifecycle from data to deployment.
- Senior (6-9 years): $145,000–$175,000. System design decisions, mentoring, works across multiple product areas.
- Staff/Principal (10+ years): $175,000–$220,000+. Defines ML architecture, often works across org boundaries.
Specialty and skill premium
Not all machine learning work pays the same. The Dallas market, shaped by the industries present, has distinct demand patterns.
Computer vision and NLP/LLM engineering command 15-25% premiums above the base MLE rate, driven by aerospace and defense primes in the DFW area (Lockheed Martin, Raytheon, L3Harris) and by the wave of enterprise LLM deployments. MLOps and ML platform engineering — building the tooling that other data scientists use — is in high demand and often lands $10,000–$20,000 above a pure modeling role at the same seniority. Time-series forecasting and causal inference for operations and supply chain (relevant for logistics, airlines, and retail headquartered in DFW) are steady but don’t carry a significant premium.
Total compensation breakdown
The $130,000 median base is only part of the picture. A mid-level MLE at a mid-size Dallas employer can expect:
Base salary: $130,000. This is what BLS tracks and what’s deposited in your account. At enterprise employers, base bands are relatively rigid — moving a band typically requires a promotion cycle, not a negotiation.
Annual cash bonus: ~$14,000 (roughly 10-11% of base). Most large Dallas employers offer annual performance bonuses in the 8-15% range for IC-level technical roles. Financial services firms tend to be at the higher end; healthcare and logistics at the lower end. Bonuses are usually tied to both individual and company performance; in a flat year, expect 60-80% of target.
Equity: ~$22,000 annualized. This varies enormously by employer. At Fortune 500 companies without a large equity culture, MLE roles may receive no equity at all, or only executive-level RSUs. At tech companies and high-growth startups, four-year RSU grants for a mid-level MLE typically run $60,000–$120,000 at grant, which annualizes to $15,000–$30,000. At startups pre-Series B, equity is in the form of stock options, nominally larger but with significant uncertainty attached.
Total at median: roughly $166,000. For comparison, the Glassdoor total compensation estimate for Dallas MLEs runs around $166,892, which aligns closely with this breakdown. The spread across total comp is wide: P25 total comp lands around $110,000-$115,000 (primarily cash-heavy enterprise roles with minimal equity); P90 total comp at Dallas tech companies with equity can reach $220,000-$250,000.
It’s worth noting what’s missing from all these figures: no-state-income-tax is worth approximately 5-9% of gross income for someone who would otherwise pay California’s top marginal rate of 13.3%. That’s not tracked in any salary database but it’s real money.
Cost-of-living adjusted analysis
Dallas’s COL index of 106 — 6% above the national average — is one of the most competitive numbers of any major US tech metro. Housing is the primary driver of why Dallas scores so close to the national average despite being a large, growing city. According to the BLS Consumer Price Index data for the Dallas-Fort Worth-Arlington area, housing costs have risen sharply since 2021, but remain dramatically below coastal metros. A 2BR apartment in Uptown Dallas or Addison runs $1,800-$2,400/month versus $3,500-$4,500 in comparable Seattle neighborhoods or $4,000-$5,500 in San Francisco.
The COL-adjusted comparison to other markets:
| City | MLE Median Base | COL Index | Purchasing Power Equivalent |
|---|---|---|---|
| Dallas | $130,000 | 106 | $122,642 |
| San Francisco | ~$195,000 | ~178 | $109,551 |
| Seattle | ~$175,000 | ~155 | $112,903 |
| Austin | ~$150,000 | ~119 | $126,050 |
| Chicago | ~$138,000 | ~108 | $127,778 |
(Purchasing power equivalent = city salary ÷ (COL index / 100))
On a COL-adjusted basis, a $130,000 Dallas MLE salary is nearly equivalent to a $195,000 San Francisco salary in real purchasing power. Austin slightly outperforms Dallas even after COL adjustment, which explains the continued pull toward Austin for some candidates — but the gap is narrower than raw salaries suggest.
For engineers choosing between a $165,000 remote offer benchmarked nationally and a $130,000 Dallas-based offer, the math is clear. But for engineers choosing between Dallas-based roles at similar pay levels, factors like industry interest, career trajectory, and specific company quality matter far more than city-level COL calculations.
Three-lever negotiation playbook
Dallas has a different negotiation environment than San Francisco. There’s less pure talent-market competition at the individual company level, which means you need to bring your leverage explicitly rather than relying on the market to create it for you.
Lever 1: Competing offer, even an imperfect one
The single most effective move in any Dallas MLE negotiation is a real competing offer, or a credible indication of one. Dallas employers — especially large corporate ones — are accustomed to counter-offering to retain talent because the cost of replacing a trained MLE is high relative to the salary delta. You don’t need a Silicon Valley offer to create leverage; a competing offer from another DFW employer at $15,000-$20,000 above the current offer is enough to move most recruiters to the edge of their band.
If you’re early in the process and don’t yet have a competing offer, run parallel processes — actively interview at two or three employers simultaneously so your timeline aligns. This is especially important at senior levels where the recruiting cycle is long (8-12 weeks is common for Staff+ roles at enterprise companies).
Lever 2: Title-and-band engineering
Dallas corporate employers often have wider latitude on title and band than on the salary number within a given band. “Senior Machine Learning Engineer” and “Staff Machine Learning Engineer” can represent a $25,000-$35,000 salary difference even before any negotiation. If the recruiter offers you a mid-level title and you have strong evidence of senior-level output (shipped production models, cross-team influence, public work), push explicitly on the level, not just the number. Getting leveled one band higher frequently nets more than any in-band negotiation would.
Ask directly: “What would it take to be considered at the Senior level rather than the Mid-level band for this role?” Hiring managers usually have that authority; recruiters often don’t. Get the conversation to the hiring manager.
Lever 3: Benefits and compensation mix for enterprise roles
At large Dallas employers with rigid base bands and no equity culture, you have more room to negotiate total package structure than the base number. Targets worth pursuing:
- Signing bonus. Often funded from a different budget than base salary and within recruiter authority. A $15,000-$25,000 signing bonus at a large enterprise company is common and often doesn’t require VP approval to offer.
- Remote flexibility. A fully remote arrangement effectively raises your real salary by eliminating commute costs and time. It also opens the door to geographic arbitrage — living somewhere cheaper in the DFW area or even relocating to a lower-COL Texas market while keeping the Dallas pay band.
- Professional development budget. AWS/GCP ML certifications, conference travel (NeurIPS, ICML), and compute credits for personal projects can add $3,000-$8,000 in real annual value.
- Accelerated review cycle. Negotiate for a 6-month performance review instead of 12 at hire, with a stated pathway to merit increase if performance targets are met. This matters most if you’re starting at the lower end of a band and need a route to the midpoint without waiting a full year.
Data caveats
BLS OEWS is the gold standard for public labor market data — it’s based on mandatory employer reporting covering hundreds of millions of wage records — but it has specific limitations for this use case.
No dedicated MLE occupation code. “Machine Learning Engineer” is not a BLS SOC title. The percentile figures in this page are derived from BLS OEWS May 2024 data for Dallas-Fort Worth Data Scientists (SOC 15-2051), adjusted for the observed MLE premium and cross-referenced against current job postings and salary databases. The DFW Data Scientist P50 in the May 2024 BLS data is $120,840; the $130,000 MLE P50 here reflects the documented 5-10% premium MLEs command over generalist data scientists due to the production engineering component.
Equity is excluded from BLS figures entirely. This understates total compensation for roles at tech companies that use equity meaningfully. For any role where equity is a significant component of the offer, use Levels.fyi to supplement BLS data — their crowdsourced total comp figures capture equity that BLS misses.
The data is lagged by design. May 2024 BLS data reflects wages paid in spring 2024. The generative AI demand surge has pushed MLE compensation higher through 2025; roles explicitly requiring LLM fine-tuning, RLHF, or inference optimization are commanding 15-25% premiums above these baseline figures in current job postings.
City-level data masks intra-metro variance. A role in Frisco or Allen (the tech corridor north of Dallas) may pay differently than one in downtown Dallas’s financial district, which may pay differently than a role in Fort Worth. The BLS Dallas-Fort Worth-Arlington MSA is one number covering a metro area larger than some states.
For the most precise current read on the Dallas MLE market: combine BLS as your anchor for base salary ranges, scan active job postings on LinkedIn and Indeed for salary bands (Texas has no salary transparency law, but many employers disclose voluntarily or when posting for roles hiring into other states), and use Levels.fyi for total comp context at companies large enough to appear in their database.