AI Engineer Salary in Dallas — 2026 BLS Data
Salary distribution
Percentile breakdown of AI Engineer base salaries in Dallas.
The $152K median base for an AI Engineer in Dallas is a competitive number for a major US metro — and it becomes even more interesting when you factor in that Texas collects zero state income tax. BLS OEWS May 2024 data for SOC 15-1252 (Software Developers — the closest federal proxy, since BLS has not yet defined a standalone AI Engineer classification) puts the Dallas-Fort Worth-Arlington MSA median for software occupations at roughly $138K–$143K. AI Engineer roles carry a demonstrated 10–20% premium above that general software developer baseline, consistent with Glassdoor and Kore1 market survey data that puts Dallas AI Engineer median base at $152K–$158K. The $152K figure used in this page reflects a conservative estimate within that range.
BLS OEWS May 2024 also shows that computer and mathematical occupations in the Dallas-Fort Worth MSA had a mean hourly wage of $55.92 — equivalent to approximately $116,314 annualized — compared to the US national mean of $32.66 for all workers. The tech-specific figure is substantially higher, confirming that Dallas rewards software and AI talent materially above its own cost-of-living baseline.
The bigger story is not the median number itself. It is the $117K spread from P25 to P90, the way employer type fractures the market, and the hidden value of Texas’s tax structure that most out-of-state comparisons fail to account for properly.
What the median hides
The P25-to-P90 range runs from $115K to $232K — a 2x spread inside a single metro for the same job title. That gap is not noise. Dallas hosts at least four distinct employer segments that all hire “AI Engineer” but pay structurally differently.
Corporate HQ tech and financial services. AT&T (global HQ in downtown Dallas), Goldman Sachs (Irving campus with one of the firm’s largest US headcounts), JPMorgan Chase, Bank of America, and American Airlines each run large AI and data science organizations in the DFW area. These employers tend to pay above the local median — base salaries in the $155K–$195K range for mid-to-senior AI Engineers — with strong cash compensation but conservative equity packages relative to pure-tech firms. AT&T’s Senior Machine Learning Engineer roles, for example, have been posted at $150K–$195K base, which anchors well above the market P50.
Enterprise SaaS and cloud tech. McKesson, Jacobs Engineering, Kimberly-Clark, and a growing cluster of enterprise SaaS companies have significant technical headcount in DFW. They hire AI Engineers for applied work — demand forecasting, supply chain optimization, NLP-based document processing — at $135K–$175K base. These roles skew toward practical ML deployment over foundational research, which narrows the top end but compresses the floor.
Startups and AI-native companies. The Dallas-Fort Worth AI startup ecosystem raised over $800 million in venture funding in 2024 and 2025, with concentration in fintech AI, healthcare AI, and logistics automation. Series A–C companies typically offer $130K–$172K base with equity packages that are illiquid but potentially significant. The equity risk-reward calculus here is different from a public company’s predictable RSU schedule.
Defense and aerospace. Lockheed Martin (Ft. Worth), L3Harris, and Raytheon have substantial AI and applied ML operations in the metro. Defense contractors pay $115K–$160K base — below what comparable software companies offer — with lower equity but mission-specific roles, more predictable hours, and clearance pathways that open a separate, higher-paying market once active.
Hub comparison: how Dallas sits relative to other AI markets
Dallas at $152K median base sits in a distinct tier of US AI engineering markets — meaningfully above cities like Atlanta ($135K–$145K), Phoenix ($130K–$148K), and Chicago ($140K–$155K), but trailing the coastal concentration centers.
San Francisco remains the US leader on AI Engineer base salary at approximately $220K+ median for the same SOC classification, driven by OpenAI, Anthropic, Google DeepMind, and FAANG AI divisions that have no comparable presence in Dallas. Seattle runs $185K–$205K, anchored by Microsoft AI, Amazon AI research, and Meta’s Bellevue engineering hub. New York City sits at $175K–$195K, pulled up by finance-sector AI demand at Citadel, Two Sigma, and JPMorgan AI Research.
Austin is the most relevant peer market: roughly $145K–$160K median AI Engineer base, COL index around 119, and a more startup-heavy employer mix. Dallas nominally outpays Austin by 5–8% in base salary while offering lower housing costs in most neighborhoods. The Dallas-Austin corridor is increasingly a single talent market — engineers sometimes commute or work hybrid across both cities — which puts upward pressure on Dallas compensation as Austin-based employers compete for the same candidates.
Where Dallas has a structural advantage is the tax math. A $152K Dallas base, with zero state income tax, produces more take-home pay than a $162K Chicago base (Illinois taxes income at 4.95%) or a $156K Pennsylvania base (Philadelphia adds a local 3.75% wage tax). The gap widens against California ($165K base after 9.3% marginal state tax starts at ~$90K) and New York ($170K base in NYC after 6.85% state plus 3.9% city rate). For most AI engineers, moving from California to Dallas at the same nominal salary is worth $12K–$18K/year in take-home pay — roughly equivalent to a mid-cycle merit raise without changing employers.
What drives the spread: company tier, level, and specialty
Three variables account for the majority of the P25-to-P90 range.
Company tier
A mid-level AI Engineer at Goldman Sachs or AT&T clearing $172K–$185K base is in a structurally different market from the same title at a Series B AI startup at $140K base plus illiquid equity. Public companies have internal compensation bands updated annually to external benchmarks, HR-enforced level criteria, and predictable total-comp trajectories. The trade-off is that base salary is unlikely to move more than 5–8% from the band midpoint in a single negotiation cycle. Private-company roles have more flexibility at offer stage but less predictability going forward.
Level
Junior AI Engineers (0–2 years, typically working on prompt engineering, ML-assisted feature integration, or data pipeline work): $85K–$115K base. Mid-level (2–5 years, owning model deployment, fine-tuning, or applied research in a specific domain): $120K–$165K base. Senior (5–8 years, leading system design for ML infrastructure or multi-model production systems): $165K–$205K base. Staff and principal (8+ years, cross-functional technical leadership): $205K–$240K base before equity. BLS aggregates all of these into one occupation code — which is the primary mechanical reason the P25-to-P90 spread looks so wide.
Specialty premium
Three specializations command consistent premiums above the Dallas baseline in 2026:
- Generative AI and LLM engineering (RAG systems, fine-tuning, production LLM deployment, agentic pipelines): $15K–$30K above baseline. Demand is highest at financial services firms automating compliance, document review, and customer-facing AI, and at healthcare companies deploying clinical NLP.
- MLOps and AI infrastructure (feature stores, model serving at scale, GPU cluster management, CI/CD for ML): $18K–$32K above baseline. The largest corporate HQs in DFW — AT&T, McKesson, American Airlines — all have material needs for this skill set as their AI deployments move from pilot to production.
- Computer vision and multimodal systems: $12K–$22K above baseline. Most relevant to logistics and supply chain applications, which are heavily represented in DFW given its position as a freight and distribution hub.
A generalist AI Engineer who integrates third-party models but does not own the training or infrastructure layer typically lands near the median. An engineer with demonstrable experience building and maintaining production ML systems — particularly with LLMs or MLOps tooling — consistently lands in the top quartile.
Total compensation breakdown
For a mid-level AI Engineer at a public company or established private employer in Dallas, total comp breaks down roughly as follows:
- Base salary: $152K. This is the number BLS tracks and what your W-2 reflects. At AT&T or Goldman Sachs, equivalent-level base would be $162K–$182K. At a mid-sized enterprise AI company, $142K–$158K. At a startup: often $130K–$150K with an equity kicker.
- Annual cash bonus: ~$19K. Large corporate employers in DFW typically pay 10–15% of base as a discretionary annual cash bonus tied to company and individual performance metrics. Goldman Sachs AI roles skew higher — bonuses of 15–25% of base are common at mid-to-senior levels in their Dallas operations. Startups frequently replace cash bonuses with equity grants.
- Annualized equity: ~$22K. At public tech companies and larger private employers, AI Engineers at the P50 level receive initial RSU grants of $72K–$100K vesting over four years, yielding roughly $18K–$25K annualized. At AT&T, equity grants tend to be smaller relative to San Francisco-based tech firms but more predictable. At startups, equity is typically common stock or options valued at stated strike price rather than vest-tracked RSUs — the paper value can be much higher but requires a liquidity event.
That totals approximately $193K in expected total comp at the median. Signing bonuses at Dallas AI Engineering roles run $10K–$30K at the mid-level, usually with a 12-month clawback. They are common at corporate HQ roles and less standard at early-stage startups.
For a senior AI Engineer at a Tier 1 Dallas employer (Goldman Sachs, AT&T, JPMorgan Chase), the numbers shift materially: $175K–$205K base, $25K–$45K bonus, $35K–$60K annualized equity. Total comp of $235K–$310K is achievable at senior levels without relocating to San Francisco — and that $235K–$310K in Texas take-home stretches further than the same number in California by $15K–$25K after state tax.
Cost-of-living adjusted value
Dallas carries a COL index of approximately 106 (US national average = 100), according to C2ER data through 2025, meaning overall living costs run roughly 6% above the national baseline. That is an unusually modest premium for a major US tech market. Housing is above average but not dramatically so: median one-bedroom apartment rent in Dallas proper runs $1,600–$2,000/month, versus $2,200–$2,700 in Los Angeles, $3,200–$4,000 in San Francisco, and $2,800–$3,500 in New York City. Groceries, transportation, and healthcare track closely to the national average.
The purchasing-power arithmetic: a $152K Dallas base, COL-adjusted, is equivalent to approximately $143K at the national average purchasing power. That is notably better than Los Angeles ($163K nominal = $109K COL-adjusted) and far better than San Francisco ($220K nominal = $123K COL-adjusted). In pure purchasing power terms, a $152K Dallas AI Engineer salary goes further than a $163K Los Angeles salary.
Compared to Austin (COL ~119): Austin runs a meaningfully higher cost of living than Dallas despite similar tech salary ranges, driven by a hotter housing market. A Dallas AI Engineer at $152K median has approximately $143K in purchasing power. An Austin AI Engineer at $150K median has approximately $126K in purchasing power. Dallas delivers a real COL-adjusted advantage over Austin even with a nominally similar headline salary.
The comparison to San Francisco is stark in purchasing power but less compelling once you factor in what the SF market actually pays. A $152K Dallas base has $143K in purchasing power; a $220K SF base has $123K. The Dallas engineer has more purchasing power — but the SF median is not the top of that market. Senior AI Engineers at Bay Area AI-native companies earn $300K–$500K total comp, a ceiling that Dallas’s employer mix simply does not reach today. If your goal is maximum absolute compensation and you are targeting research or principal-level roles at frontier AI companies, that requires being in San Francisco. If your goal is comfortable purchasing power, career stability, and a strong total comp package without the Bay Area rent premium, Dallas makes a compelling case.
Three-lever negotiation playbook
Lever 1: Use the Texas tax advantage as an explicit negotiating argument
Most Dallas-based recruiters at companies with national or global pay bands (Goldman Sachs, AT&T, JPMorgan, McKesson) are accustomed to candidates from Chicago, New York, or California who have done the tax math. You do not need to be subtle about it. If you are evaluating a Dallas offer alongside a competing offer from a higher-tax state, the net take-home gap is a legitimate business argument for negotiating base: “This offer is at $152K, but my alternative is $165K in Chicago. After Illinois’s 4.95% flat tax, the after-tax gap is about $4K, not $13K. I’d like to close that gap with a $158K base in Dallas.” This framing is factually correct, professional, and specific enough to move a conversation without sounding like you are guessing.
Lever 2: Anchor to the specialty premium, not the general title band
“AI Engineer” is an umbrella title that can resolve to a software engineer band (lower ceiling) or an ML engineer band (higher ceiling) depending on how a company’s internal job family structure works. Before accepting any offer, establish which classification the role maps to internally. If your work involves model training, fine-tuning, production MLOps, or generative AI system design — as opposed to integrating a third-party API wrapper — push to have the role placed in the ML or AI specialist track, which typically carries a 10–20% higher base band than the generalist software engineer family. This ask is most effective at offer stage before the paperwork is finalized, and it succeeds more often than candidates expect. A hiring manager who has already decided they want you has limited incentive to fight an internal reclassification request over a $10K–$15K band difference.
Lever 3: Create timing leverage with a competing offer
The Dallas AI engineering market is active enough that real competing offers are achievable within a normal 4–6 week interview process. If you have two simultaneous final-round processes, the second offer — even if from a company you prefer less — creates the single most reliable negotiation lever available. Dallas corporate HQ employers are particularly responsive to competitive offers because they are not at the center of a high-velocity bidding market (unlike San Francisco, where everyone expects the process). A competing offer letter from Goldman Sachs helps you negotiate with AT&T and vice versa; an offer from a Dallas startup can be used to accelerate a timeline or push base at a larger employer that tends to move slowly. The mechanics: once you have a written offer, give the preferred employer 72 hours to respond — not 24 (too aggressive for corporate HR), not a week (gives them time to slow-walk). That window converts a competing offer into measurable base salary in a majority of cases.
Data caveats
BLS OEWS is the most carefully constructed public salary dataset in the US — mandatory employer reporting rather than voluntary self-reporting — but it has real limitations specific to AI engineering.
No dedicated AI Engineer SOC code. BLS uses SOC 15-1252 (Software Developers) as the primary container for this occupation. That bucket also includes mobile developers, embedded systems engineers, and full-stack generalists whose compensation is structurally lower than specialized AI/ML engineers. The P25–P90 figures in this page apply an estimated 10–20% AI Engineer premium above the BLS software developer baseline for the Dallas MSA, validated against Glassdoor, Built In DFW, and Kore1 survey data. Treat these percentiles as calibrated ranges, not precise measurements.
Equity is excluded. BLS tracks base wages only. For AI Engineers at public companies, equity is a meaningful but secondary component at the median level in Dallas (unlike San Francisco, where equity often exceeds base at senior levels). For pre-IPO startup roles in DFW, equity is the primary argument for accepting below-median cash — but BLS provides no way to value it. The total comp figures in this page are estimates built from market survey data, not BLS outputs.
Defense sector distortion. Dallas’s substantial defense and aerospace presence (Lockheed Martin in Ft. Worth, Raytheon, L3Harris) pulls the lower percentiles down compared to a pure-software market. If you are interviewing exclusively at tech companies and financial services firms, your realistic P25 is closer to $128K–$132K rather than the market-wide $115K. The BLS metro figure reflects all employers across all industries.
The data is lagged. May 2024 reflects wages paid in May 2024. AI engineer compensation continued to move upward through 2025 and into 2026, particularly for generative AI and LLM-specialist roles. Treat the BLS baseline as a floor for current negotiations, not a ceiling.
For triangulation, supplement the BLS numbers with levels.fyi (for company-specific total comp at any employer you are actively evaluating), Built In DFW (for Dallas startup and tech-company pay ranges), and posted salary bands on job listings, which Texas employers are increasingly disclosing even without a legal requirement to do so. Cross-referencing three data sources gets you within 8–12% of what any specific offer should look like before your first recruiter call.