ML Engineer Jobs in Texas
ML Engineer jobs in Texas are among the most active in the country, with strong demand across aerospace and defense, enterprise software, financial technology, and energy tech at levels from entry-level through principal and staff engineer. Austin, Dallas, and Houston anchor most of the hiring, where companies like Dell Technologies, ExxonMobil, and AT&T maintain large technical operations and invest heavily in applied machine learning. The most sought-after specialties in Texas are natural language processing, MLOps and model deployment infrastructure, and computer vision tied to industrial and energy applications. Find a role that fits below and apply directly.
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Showing 5 of 287+ ML Engineer jobs
Senior Lead AI/ML Engineer - R01567102
About Brillio:
Brillio is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. Brillio, renowned for its world-class professionals, referred to as "Brillians", distinguishes itself through their capacity to seamlessly integrate cutting-edge digital and design thinking skills with an unwavering dedication to client satisfaction. Brillio takes pride in its status as an employer of choice, consistently attracting the most exceptional and talented individuals due to its unwavering emphasis on contemporary, groundbreaking technologies, and exclusive digital projects. Brillio's relentless commitment to providing an exceptional experience to its Brillians and nurturing their full potential consistently garners them the Great Place to Work® certification year after year.
Primary Skills
- Value Quantification: Pre-Model Development, Model Provisioning: Kubernetes, Kibana, Model Monitoring, Cloud Computing, Python/PySpark, SAS/SPSS, Great Expectation, Evidently AI, Deployment Strategies (A/B, Blue green, Canary), Model testing, Tools (KubeFlow, BentoML), Integration testing, ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Value Quantification: Post-Model Deployment, Model Experimentation, R/ R Studio
Specialization
- ML Engineering: AI/ML Engineer
Job Requirements
Senior Lead AI/Full Stack Engineer
Location: Deerfield, FL
Work Type: Hybrid Model with min 2-3 days per week
The mission
Brillio is standing up this team because AI has collapsed attacker timelines from weeks to hours. You’ll build the AI-native tooling that lets a small senior pod respond at machine speed, implementing work-focused security agents and the deterministic orchestration around them, wired into vulnerability-response stack (SIEM/SOAR/EDR, identity, vuln management, CMDB, ITSM). This is a hands-on-keyboard seat: you write production code, run agent fleets, and ship across the full SDLC.
What you’ll actually do
- Write production code. Repeatable work becomes compiled, testable binaries (Go-first); prompts drive persona behavior only. You build the tooling, not slideware about it.
- Operate agent fleets. Take a decomposed backlog, fan out parallel streams on conflict-free work, and bring it back through review gates.
- Build security in, not on. TDD test-writers ahead of coders, a dedicated validation stage (models hallucinate passing tests), persona judging against threat/security models, failures auto-looped as issues. Signed builds, SBOMs, and access control are table stakes.
- Integrate natively. Azure + GitHub Copilot, build-what’s-missing in-stack. No third-party platforms, no AWS.
- Run model-agnostic. Personas across Claude / Copilot / ChatGPT because they fail differently; disagreement is signal. Manage token and context economics as an engineering constraint — just-in-time context loading, per-task budgets, alerts.
- Own your slice end to end. Design, develop, test, integrate, deploy, document, and KT — across the full SDLC.
Must-have
- Senior engineer who still ships daily — hands-on-keyboard delivery, no exceptions.
- Deep Azure + GitHub Copilot delivery experience (primary stack, not a footnote).
- Strong Go (or equivalent systems language) for deterministic, testable orchestration tooling.
- Hands-on experience operating multi-agent / multi-model workflows in real delivery, not demos.
- Security-native instincts — TDD discipline, secure SDLC, threat modeling; comfort in a vulnerability-response context.
- Full-SDLC ownership of a work stream: design through deployment, docs, and knowledge transfer.
Nice-to-have
- SIEM / SOAR / EDR, identity, vuln-management, or CMDB / ITSM integration experience.
- Eval-harness, red-team, or agent-guardrail work (blast-radius sandboxing, reversibility, confidence thresholds, audit trails).
Salary: 140-150 USD per year salary
Senior Lead AI/ML Engineer - R01567102
About Brillio:
Brillio is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. Brillio, renowned for its world-class professionals, referred to as "Brillians", distinguishes itself through their capacity to seamlessly integrate cutting-edge digital and design thinking skills with an unwavering dedication to client satisfaction. Brillio takes pride in its status as an employer of choice, consistently attracting the most exceptional and talented individuals due to its unwavering emphasis on contemporary, groundbreaking technologies, and exclusive digital projects. Brillio's relentless commitment to providing an exceptional experience to its Brillians and nurturing their full potential consistently garners them the Great Place to Work® certification year after year.
Primary Skills
- Value Quantification: Pre-Model Development, Model Provisioning: Kubernetes, Kibana, Model Monitoring, Cloud Computing, Python/PySpark, SAS/SPSS, Great Expectation, Evidently AI, Deployment Strategies (A/B, Blue green, Canary), Model testing, Tools (KubeFlow, BentoML), Integration testing, ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Value Quantification: Post-Model Deployment, Model Experimentation, R/ R Studio
Specialization
- ML Engineering: AI/ML Engineer
Job Requirements
Senior Lead AI/Full Stack Engineer
Location: Deerfield, FL
Work Type: Hybrid Model with min 2-3 days per week
The mission
Brillio is standing up this team because AI has collapsed attacker timelines from weeks to hours. You’ll build the AI-native tooling that lets a small senior pod respond at machine speed, implementing work-focused security agents and the deterministic orchestration around them, wired into vulnerability-response stack (SIEM/SOAR/EDR, identity, vuln management, CMDB, ITSM). This is a hands-on-keyboard seat: you write production code, run agent fleets, and ship across the full SDLC.
What you’ll actually do
- Write production code. Repeatable work becomes compiled, testable binaries (Go-first); prompts drive persona behavior only. You build the tooling, not slideware about it.
- Operate agent fleets. Take a decomposed backlog, fan out parallel streams on conflict-free work, and bring it back through review gates.
- Build security in, not on. TDD test-writers ahead of coders, a dedicated validation stage (models hallucinate passing tests), persona judging against threat/security models, failures auto-looped as issues. Signed builds, SBOMs, and access control are table stakes.
- Integrate natively. Azure + GitHub Copilot, build-what’s-missing in-stack. No third-party platforms, no AWS.
- Run model-agnostic. Personas across Claude / Copilot / ChatGPT because they fail differently; disagreement is signal. Manage token and context economics as an engineering constraint — just-in-time context loading, per-task budgets, alerts.
- Own your slice end to end. Design, develop, test, integrate, deploy, document, and KT — across the full SDLC.
Must-have
- Senior engineer who still ships daily — hands-on-keyboard delivery, no exceptions.
- Deep Azure + GitHub Copilot delivery experience (primary stack, not a footnote).
- Strong Go (or equivalent systems language) for deterministic, testable orchestration tooling.
- Hands-on experience operating multi-agent / multi-model workflows in real delivery, not demos.
- Security-native instincts — TDD discipline, secure SDLC, threat modeling; comfort in a vulnerability-response context.
- Full-SDLC ownership of a work stream: design through deployment, docs, and knowledge transfer.
Nice-to-have
- SIEM / SOAR / EDR, identity, vuln-management, or CMDB / ITSM integration experience.
- Eval-harness, red-team, or agent-guardrail work (blast-radius sandboxing, reversibility, confidence thresholds, audit trails).
Salary: 140-150 USD per year salary
See All 287+ ML Engineer Jobs in Texas
Find roles in Texas that match your experience and apply in just a few clicks.
Find ML Engineer JobsML Engineer Jobs by City in Texas
Where Texas roles are concentrated, by current openings.
ML Engineer Job Market in Texas
A snapshot from current Texas openings, updated as new roles post.
Who's Hiring
- Apple59
- LTIMindtree14
- Tiger Analytics11
- PepsiCo7
- Photon7
Top Industries Hiring
- Technology & Software103
- Electronics & Hardware62
- Consulting & Professional Services36
- Banking & Financial Services30
- Energy14
What Texas Employers Look For
The qualifications that appear most often in ML engineer jobs across Texas.
- Bachelor's or master's degree in computer science, statistics, or a closely related field
- Proficiency in Python and core machine learning libraries such as TensorFlow, PyTorch, or scikit-learn
- Experience designing, training, and deploying production-grade machine learning models
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud for model hosting
- Ability to work with large structured and unstructured datasets using SQL and distributed computing tools
- Strong communication skills for presenting model results and tradeoffs to non-technical stakeholders
ML Engineer Jobs in Texas: Frequently Asked Questions
How do you become a ml engineer in Texas?
ML engineering in Texas does not require a state-issued license. Most Texas employers expect a bachelor's degree in computer science, mathematics, or a related discipline, though many roles at major Texas tech and energy firms specify a master's degree. Building a portfolio of deployed projects, contributing to open-source machine learning repositories, and completing industry-recognized certifications in cloud ML services strengthens a candidacy considerably in this market.
Which companies hire ml engineers in Texas?
Employers hiring ml engineers in Texas right now include Apple, LTIMindtree, and Tiger Analytics, based on current listings on Migrate Mate as of June 2026. Texas's concentration of Fortune 500 headquarters, defense contractors, and large energy firms creates steady demand for machine learning talent across sectors beyond the typical software industry.
Which Texas cities have the most ml engineer jobs?
Austin, Dallas, and Irving have the most ml engineer openings in Texas. Austin's dense concentration of tech company headquarters and semiconductor firms drives the bulk of postings there, while Dallas's large enterprise software and financial services sector and Houston's energy and aerospace industries generate consistent demand in those metros.
Are there remote ml engineer jobs in Texas?
Yes, and more than most fields. About 17% of ml engineer openings tied to Texas are remote or hybrid as of June 2026, reflecting how much of the role involves writing code, running experiments, and reviewing results in cloud environments. Model research and experimentation work tends to be the most remote-friendly, while roles involving on-premises infrastructure or sensitive government and defense data are more likely to require in-office presence.
How can I get hired as a ml engineer in Texas with little or no experience?
The most realistic entry path is through a data analyst or data engineer role at a Texas employer that has an internal ML platform team, then transitioning once you have hands-on model work to show. Large Texas employers in energy and defense, including firms in the Houston and Dallas corridors, run associate data science programs and rotational engineering tracks that accept recent graduates. Building a public portfolio with end-to-end projects covering data preparation, training, and deployment gives candidates an edge over those with coursework alone.
Where can I find and apply to ml engineer jobs in Texas?
You can find and apply to ml engineer jobs in Texas on Migrate Mate, which lists current Texas openings updated regularly. Search the available roles, find the ones that match your background and location preference, and apply directly to each employer through the listing.
See All 287+ ML Engineer Jobs in Texas
Find roles in Texas that match your experience and apply in just a few clicks.
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