In [17]: # texas_state.ipynb
Abstract
On the official wage series Texas sits mid-table in nominal terms and higher on purchasing power. The May 2025 OEWS median for SOC 15-2051 Data Scientists is $122,090 (rank 13) and for SOC 15-1252 Software Developers $132,150 (rank 15) [1]. Texas has no state income tax and substantially lower housing costs than coastal metros. On market-comp self-report, Austin leads the state with $165,000 L5 base after sustained West Coast tech relocation since 2018, plus xAI's 2024 HQ relocation to the metro; Dallas and Houston anchor strong enterprise-AI, energy-ML, and defence-ML clusters with lower comp ceilings but distinct domain-depth opportunities [2].
1 BLS Occupational Employment and Wage Statistics, May 2025, SOC 15-2051. Snapshot taken 6 September 2026. BLS Occupational Employment and Wage Statistics, May 2025, SOC 15-1252. Snapshot taken 6 September 2026. Public domain; attribute to the Bureau of Labor Statistics. There is no BLS occupation called machine learning engineer, so these two SOCs are the closest published series, not a survey of the job title.
2 Metro bands and total-comp figures are market-comp self-report, not BLS, from Levels.fyi Austin metro captured May 2026. Texas Workforce Commission 2026 wage data is a separate state source.
Occupation mapping
"Machine learning engineer" is not a Standard Occupational Classification title, so the BLS Occupational Employment and Wage Statistics survey publishes no wage series under that name. The figures on this page come from SOC 15-2051 Data Scientists, the closest published occupation. Read them as a floor and a shape for the market, not as a Machine learning engineer salary survey.
O*NET OnLine's occupation keyword search returns 15-2051.00 Data Scientists as the first match for the job title "machine learning engineer" (onetonline.org/find/quick?s=machine%20learning%20engineer, checked 6 September 2026).
Software Developers is the other SOC that absorbs machine learning engineering work. It is shown alongside Data Scientists so the bracket is visible rather than hidden behind a single choice.
table tx-1 : base by metro and level
Texas is the second most populous state in the United States and has the largest land area of any contiguous state. Its ML labour market is geographically dispersed across four major metros, each with a distinct industry concentration. Austin is the tech-forward metro with the strongest concentration of T2 hyperscaler outposts and AI-startup activity. Dallas and Fort Worth anchor enterprise IT and fintech, with multiple Fortune 500 headquarters and a strong defence-tech cluster. Houston is dominated by energy-sector ML and the world's largest medical centre. San Antonio supports a defence-ML cluster anchored by USAA, the city's cybersecurity industry, and military presence.
| Metro | L3 base | L5 base | L6 base | L5 total comp |
|---|---|---|---|---|
| AustinTech corridor, T2 outposts, AI-startup growth, remote-friendly | $120k | $165k | $210k | $180k - $250k |
| Dallas / Fort WorthEnterprise AI, fintech ML, telecom AI, defense contracting | $110k | $148k | $190k | $165k - $225k |
| HoustonEnergy-sector ML, oil-and-gas AI, healthcare ML | $105k | $140k | $180k | $155k - $215k |
| San AntonioDefense ML, cybersecurity ML, smaller market | $95k | $125k | $160k | $140k - $190k |
Figure tx-1. Texas metro base salary bands and L5 total compensation, May 2026. These are illustrative market-comp bands from Levels.fyi regional filters. They are NOT BLS figures and contain no BLS component; the published Texas wages are in the derived table below, separately sourced. Austin numbers reflect the post-2024 xAI HQ relocation effect on the local senior-IC market.
| Texas, May 2025 | 15-2051 | 15-1252 |
|---|---|---|
| Median annual | $122,090 | $132,150 |
| Mean annual | $119,710 | $136,450 |
| 25th percentile | $83,880 | $103,350 |
| 75th percentile | $146,030 | $162,530 |
| 90th percentile | $170,780 | $183,680 |
| Employment | 25,860 | 163,880 |
Table tx-1b. Official Texas wages for the two SOCs closest to machine learning engineer. BLS Occupational Employment and Wage Statistics, May 2025, SOC 15-2051. Snapshot taken 6 September 2026. BLS Occupational Employment and Wage Statistics, May 2025, SOC 15-1252. Snapshot taken 6 September 2026. Public domain; attribute to the Bureau of Labor Statistics.
section tx-2 : take-home math
Texas, like Washington and Florida, has no state income tax on W2 wages. For an ML engineer comparing a Texas offer to a California offer of the same nominal base salary, the after-tax delta is substantial. On a $200,000 base, the Texas resident takes home approximately $23,000 more per year than the California resident, purely from the absence of state income tax. On a $300,000 base, the gap widens to approximately $34,000. Over a 10-year career stage, the cumulative after-tax advantage compounds into approximately $300,000 to $500,000 of additional wealth, assuming the additional take-home is invested at typical equity returns.
Texas does levy property tax (one of the highest in the United States at the local level, typically 2.0 to 2.7 percent of home value in major metros), which partially offsets the income-tax advantage for homeowners with mortgages above approximately $700,000. For ML engineers planning to rent or buy modest housing, the income-tax advantage is the dominant factor. For high earners buying expensive Austin or Dallas single-family homes, the property tax math reduces the gap to coastal-state-resident equivalents.
Texas also has no income tax on RSU vesting, on long-term capital gains, or on dividends. This makes Texas particularly favourable for equity-heavy compensation packages relative to California or NY. A $200,000 base plus $100,000 in annual RSU vesting at a T2 hyperscaler Austin office returns approximately the same total take-home as $250,000 base plus $125,000 RSU at a Bay Area office, due entirely to the state tax differential.
table tx-2 : industry concentration
Texas's industry mix is more diverse than coastal-metro tech-heavy markets, which produces more sector-specific ML opportunities but also a wider compensation distribution. The trade-off is real: working in Texas energy-sector ML can offer technically deep, domain-rich work at lower nominal comp than coastal Big Tech, but with stronger work-life balance and a tighter local community of specialised colleagues.
| Sector | Metro presence | Note |
|---|---|---|
| Tech outposts (T2 hyperscaler, T3 unicorn) | Strong in Austin, growing in Dallas | Compensation often within 10-15 percent of Bay Area HQ, lower COL net wins |
| Energy and oil-and-gas ML | Houston dominant; some Dallas | Optimisation, reservoir-modelling, exploration ML. Cyclical with oil price |
| Defense and aerospace ML | Dallas, San Antonio, El Paso | Federal contracting, clearance-required. Lower nominal comp but stable |
| Enterprise / Fortune 500 ML | Dallas, Houston anchors many F500 HQs | T5 enterprise tier, broader scope per engineer, stable hours |
| Healthcare ML | Houston (Texas Medical Center), Dallas | Imaging ML, EHR ML, claims processing. Often mission-driven slower-pay |
| Crypto and blockchain ML | Austin since 2021 relocations | Cyclical, comp tied to token prices, smaller market |
Figure tx-2. Sector-level ML employment concentration across Texas metros, May 2026. Editorial description of where the hiring sits, drawn from job-board observation across 2024 and 2025. It carries no wage figures and is not a BLS product; the official Texas wages are in table tx-1b above.
section tx-3 : Bay Area to Austin
A common decision for senior ML engineers since 2018 has been whether to relocate from the Bay Area to Austin (or Dallas), often while staying with the same employer remote or transferring to a local office. The financial math typically favours the move for engineers whose compensation is base-and-RSU rather than frontier-lab pre-IPO equity.
A representative example: a senior L5 ML engineer at a T2 hyperscaler with $230,000 base, $150,000 annual RSU vesting, and $30,000 cash bonus in San Francisco. The same engineer transferring to the Austin office of the same employer typically receives a geo-cut to roughly $200,000 base, $130,000 RSU, and $25,000 bonus. After-tax take-home in SF: approximately $260,000 cash. After-tax take-home in Austin: approximately $245,000 cash. Bay Area median rent (two-bedroom): approximately $4,500 per month; Austin median rent: approximately $2,200 per month. Net annual housing-and-tax savings in Austin: approximately $27,000 to $35,000.
The calculation is different for engineers whose compensation is heavily equity-weighted at a Bay Area pre-IPO frontier lab. Relocating to Austin typically requires changing employers (most frontier labs do not have Austin offices), and the eventual equity-vesting upside at the lab is sacrificed for the relocation. For engineers earlier in career stage with smaller equity holdings, the relocation is easier. For senior engineers with $1,000,000+ unvested at a frontier lab, the relocation is a major decision with substantial opportunity cost.
A non-financial factor: Texas's regulatory and political environment differs meaningfully from California's. For some ML engineers, this is a positive (lighter regulatory burden on AI deployment, more flexible labour rules), and for others, a negative (state-level policies on reproductive healthcare, LGBT rights, gun law). These factors weigh into the decision alongside the financial math.
section tx-4 : common questions
What is the average ML engineer salary in Texas?
On the official wage series, the May 2025 OEWS median for SOC 15-2051 Data Scientists in Texas is $122,090 (rank 13 of 49 published states) and for SOC 15-1252 Software Developers $132,150 (rank 15). Neither is a machine learning engineer series; BLS publishes none. Texas carries 25,860 data scientists and 163,880 software developers, second only to California on both counts. On market-comp self-report, which is not a BLS figure, Austin leads the state at $165,000 base for L5 senior engineers with total compensation of $180,000 to $250,000; Dallas and Houston follow at $140,000 to $148,000 base.
Does Texas pay better than California after tax?
On purchasing power for non-equity compensation, often yes. Texas has no state income tax, so a $200,000 Texas ML engineer takes home approximately $144,000 after federal and FICA. A California ML engineer at the same $200,000 base takes home approximately $128,000 after state income tax. Combined with Texas housing costs roughly 40 to 60 percent below the Bay Area, the cost-of-living-adjusted purchasing power often favours Texas. The Bay Area regains the lead at high equity compensation because frontier-lab and pre-IPO unicorn equity is mostly unavailable in Texas.
Why is Austin the highest-paying Texas metro?
Austin has been the primary recipient of West Coast tech relocations since 2018. Several major hyperscalers have substantial Austin offices, multiple AI-focused unicorns have opened Austin campuses, and a sustained inflow of senior engineers from the Bay Area has raised the local cost of living and the local salary band. Austin L5 ML compensation now runs approximately 15 to 25 percent above the Dallas and Houston equivalents, reflecting the concentration of T2 and T3 employers in the metro.
Is there a frontier AI lab presence in Texas?
Limited and growing. xAI's headquarters relocation to Austin in 2024 brought the first major T1 frontier-lab presence to the state. Several other frontier-AI companies have established small Austin offices but generally not as primary research locations. For pure foundation-model research roles, San Francisco still dominates the labour market. For applied ML and product-AI work at T2 and T3 employers, Austin has substantial opportunity.
What is the after-tax take-home for a $200,000 Texas ML engineer?
On a $200,000 base in 2026, federal income tax (single, simplified) is approximately $38,000 and FICA is approximately $10,800. Texas state income tax is zero. Net take-home: approximately $151,200. The same $200,000 base in San Francisco returns approximately $128,000 take-home, a $23,000 annual differential in favour of Texas. Once Texas's lower housing cost is factored in, the lifestyle advantage is larger.
Are remote ML engineers paid the same as in-Austin engineers?
Mostly no, but variation is wide. Most T2 hyperscalers apply a geo-adjustment that pays Austin remote roughly 88 to 92 percent of Bay Area in-office for the same level. Some AI-focused unicorns and T3 startups pay flat national rates regardless of location. For a senior IC ML engineer leaving the Bay Area for Austin remote at the same employer, the typical pay cut is 10 to 15 percent, which is often more than offset by Texas's lack of state income tax and lower housing cost.
What is the Texas energy-sector ML market like for compensation?
Houston-based oil-and-gas ML pays $130,000 to $200,000 base for senior engineers, with bonus tied to commodity-price cycles. The work is technically interesting (reservoir modelling, seismic interpretation, drilling optimisation, supply-chain ML) but the comp ceiling is below Big Tech. Renewable-energy ML (wind farm optimisation, grid ML, battery analytics) has been growing in Texas since 2022 and pays competitively. For ML engineers prioritising domain depth over comp maximisation, energy-sector ML in Houston offers a distinct path.
California comparison
Bay Area equity ceiling vs Texas take-home math
Washington comparison
Other major no-state-income-tax tech metro
Remote pay and geo-adjustment
How employers cut Bay-Area-to-Austin transfers
Employer tiers
T1 through T6 framework
All states ranked
National state-by-state distribution
Total compensation breakdown
Base vs RSU vs bonus across tiers
In [ ]: # reader_questions.ipynb
Write in. A person opens every message, and the ones we can answer come back with the survey release or the public disclosure the number is drawn from, stated plainly enough that you can go and check it. We put no clock on a reply. Should an answer be worth reading twice we might set it out here stripped of anything identifying, and we will write and ask you before that happens.
Out[ ]:
Treat everything on this site as compensation reference assembled from published surveys and public filings, not as financial advice and not as a benchmark any single negotiation should be anchored to. Your own offer turns on the employer, the level it is pitched at, the equity instrument behind it and the market on the day it is made.