Best Machine Learning Degree Programs in Texas, 2026 Rankings

Verdict: Texas ML students benefit from the standard Texas state aid stack (TEXAS Grant, TEG, Top 10% Scholarship, UT Austin Texas Advance Commitment) combined with substantial University of Texas at Austin ML research depth + access to Texas Advanced Computing Center (TACC), one of the largest US academic distributed-training compute resources. The ML-specific consideration is that Texas has substantial production-ML engineering employment at scale, Capital One Plano (one of the larger US enterprise-ML organizations outside Big Tech), Tesla AI (substantial ML systems engineering for Autopilot, FSD, Optimus, and the Dojo custom ML training infrastructure), Apple Austin ML, plus the accelerating Austin ML platform startup ecosystem. Combined with no state income tax, Texas ML take-home compensation regularly exceeds equivalent California roles.

TEXAS Grant (up to ~$5,195/year for need-based in-state students) and Top 10% Scholarship (automatic for Texas HS grads in top 10% of class, $2,000/year + guaranteed admission to most Texas publics) stack with federal Pell. UT Austin's Texas Advance Commitment (free tuition for Pell-eligible Texas residents) makes UT Austin CS-with-ML-emphasis one of the strongest financial value propositions in US ML education, particularly given UT Austin's substantial ML research depth and TACC compute access.

Texas's ML-specific institutional aid is concentrated at: UT Austin Computer Science ML research with substantial faculty depth across machine learning, deep learning, NLP, and applied ML. TACC (Texas Advanced Computing Center), one of the largest US academic supercomputing operations providing distributed-training compute access to UT Austin graduate students at a scale matched by few US academic institutions, particularly with the Frontera and Lonestar6 systems. Texas A&M College Station has growing ML research depth particularly around robotics and engineering-applied ML. UT Dallas has substantial CS-with-ML-emphasis. Rice University has growing ML research with substantial institutional aid for admitted students.

Industry-track scholarships from Texas-based ML employers, Capital One Plano production-ML organization (one of the largest US enterprise-ML platforms outside Big Tech, employing hundreds of ML engineers across credit modeling, fraud detection, customer-experience ML, and ML platform infrastructure), Tesla AI Dojo team (substantial ML systems engineering for the Dojo custom ML training supercomputer, exit-tier engineering work that doesn't exist at most US employers), Apple Austin ML engineering, Oracle Austin ML, Indeed ML, Dell Technologies ML, plus the growing Austin ML platform startup ecosystem (Anthropic Austin, OpenAI Austin, Together AI Austin operations, plus dozens of applied-ML startups), provide additional funding most students don't pursue.

Use our interactive ROI calculator to estimate your return on investment for a machine learning degree in Texas. Enter your expected tuition costs, financial aid, and career goals to see projected payback periods and lifetime earnings. The calculator uses current salary data from BLS and tuition data from IPEDS to provide accurate estimates.

Data sources: BLS OEWS May 2024, IPEDS 2024. Calculations use median salaries, 3% discount rate, and assume salary growth declines from 6% to 2% over career. Individual results will vary. | Powered by Hakia.com

Verdict: Texas's Common Course Numbering System (TCCNS) supports ML-track transfer pathways well at the foundational level. The ML-specific consideration is that UT Austin CS-with-ML transfer admission is competitive (10-20% for CS) while UT Dallas CS-with-ML, Texas A&M CS-with-ML, UT San Antonio, UT Arlington, and Texas State University have substantially more accessible ML-track transfer admission and produce graduates well-placed into Austin ML startup hiring and DFW enterprise-ML roles at Capital One Plano.

Best Texas community college ML-track-feeder schools: Collin College (Plano, strong UT Dallas pipeline with direct Capital One Plano industry exposure during community college years), Houston Community College (HCC) (largest CS/ML-track enrollment in Texas, strong UT Houston and Rice transfer record), Austin Community College (ACC) (well-established UT Austin and Texas State articulation, geographically positioned for direct Austin ML startup and Apple Austin / Tesla AI exposure), Dallas College (DFW area, strong UT Dallas, UT Arlington, and Texas A&M transfer pipelines), Lone Star College (suburban Houston), and San Jacinto College (Houston metro). Each has refined ML-prerequisite mathematics and intro-CS sequences via TCCNS articulation.

The ACC → UT Austin CS-with-ML-emphasis pathway is genuinely competitive, UT Austin CS transfer admission is roughly 10-20% for highly-qualified applicants with strong mathematics and demonstrated ML project work. The Collin College → UT Dallas CS-with-ML pathway is substantially more accessible (roughly 30-45% for qualified applicants) and produces graduates well-placed into Capital One Plano production-ML at exceptional scale. The ACC → Texas State University CS-with-ML pathway is the substantially-accessible Austin-area alternative with substantial Austin ML startup industry exposure.

Beyond UT Austin: Texas A&M College Station has growing CS-ML research depth particularly in robotics-ML (transfer admission roughly 20-35%). UT Dallas has substantial CS-with-ML-emphasis with accessible transfer admission and DFW enterprise-ML placement (particularly Capital One Plano). University of Houston has CS programs with ML-emphasis. Rice University has growing ML research depth but transfer admission is genuinely difficult (under 15%). Texas State University (San Marcos) has growing CS programs with accessible transfer admission and direct Austin ML startup industry exposure. For Texas ML-track students, the UT Dallas pathway is the accessible Capital One Plano production-ML optimization; UT Austin CS is the elite UT Austin + TACC distributed-training optimization with substantial ML research depth; Texas A&M is the robotics-ML optimization; Texas State is the affordable Austin-ML-startup-adjacent optimization; Rice is the elite private optimization.

Verdict: Texas has a substantial and rapidly-growing ML engineer job market with two distinct clusters: Austin's tech-company ML + frontier-AI ML startup acceleration, Apple Austin ML engineering (one of Apple's substantial ML engineering organizations outside Cupertino), Tesla AI's substantial ML systems engineering (Autopilot ML, FSD computer vision at fleet scale, Optimus humanoid robotics ML, plus the substantial Dojo custom ML training supercomputer infrastructure engineering which is exit-tier ML systems work that doesn't exist at most US employers), Oracle Austin ML, Indeed ML, Dell Technologies ML, plus accelerating Austin ML platform startup ecosystem, and DFW's enterprise-ML cluster, anchored by Capital One Plano production-ML organization (one of the largest US enterprise-ML platforms outside Big Tech, with hundreds of ML engineers across credit modeling, fraud detection, customer-experience ML, and ML platform infrastructure), USAA production-ML (San Antonio + Plano), Toyota North America IT ML, AT&T ML at scale. Combined with no state income tax, Texas ML take-home compensation often exceeds equivalent California roles.

By metro: Austin averages ~$165,000-$230,000 for ML engineer roles per Levels.fyi with substantial concentrations at Apple Austin ML engineering, Tesla AI (substantial ML systems engineering for vehicle Autopilot ML, FSD computer vision ML, Optimus humanoid robotics ML, plus the substantial Dojo custom ML training infrastructure team, Dojo engineering work is vertically-integrated custom-silicon-plus-ML-infrastructure engineering at a scale matched only by Google TPU teams and Apple Silicon ML acceleration), Oracle Austin ML, Indeed ML, Dell Technologies ML, IBM Austin ML/cloud-ML integration, Cisco Austin ML/networking-ML, plus the rapidly-growing Austin frontier-ML startup engineering scene. Anthropic Austin operations, OpenAI Austin operations, Together AI Austin operations, Google Austin ML engineering have each added substantial Austin ML engineering headcount through 2024-2026.

Dallas-Fort Worth averages ~$150,000-$200,000 for ML engineer roles with Capital One Plano production-ML organization as the standout employer, one of the largest US enterprise-ML platforms outside Big Tech, with substantial ML engineering across credit modeling (loan decisioning, line management), fraud detection, customer-experience ML (personalization, marketing ML), ML platform infrastructure, and ML feature stores. USAA's combined San Antonio + Plano production-ML organization for insurance underwriting ML, claims-processing ML, fraud detection ML, substantial ML engineering at insurance-and-banking scale. Toyota North America IT ML applications (Plano). AT&T's substantial ML organization (network-operations ML, customer-experience ML). Houston averages ~$135,000-$180,000 with NASA Johnson Space Center ML applications, ExxonMobil computational geophysics ML, Schlumberger/SLB ML, plus growing Houston Medical Center ML applications.

Three Texas ML-specific dynamics: (1) Tesla AI's Dojo custom ML training infrastructure team is one of the most-distinctive US ML systems engineering operations, vertically-integrated custom-silicon-plus-ML-infrastructure engineering at a scale matched only by Google TPU teams. (2) Capital One Plano's substantial production-ML organization is one of the most-overlooked elite US ML engineering destinations, comparable to Big Tech ML platform engineering in technical depth, but with substantially more accessible new-graduate hiring than Bay Area FAANG ML platform engineering. (3) Austin's frontier-ML startup acceleration through 2024-2026 is the most-underappreciated US ML startup trend, Anthropic, OpenAI, Together AI, and Google have substantially expanded Austin ML engineering operations as an alternative to Bay Area hiring. See Machine Learning Engineer Career Guide and AI/ML Engineer Career Guide.

Last Updated: June 26, 2026. Rankings based on IPEDS 2024 data. Salary data from BLS OEWS May 2024.