Machine Learning Scientist Jobs in Texas
Machine Learning Scientist jobs in Texas are among the most active in the country, concentrated in artificial intelligence research, energy analytics, defense systems, and financial technology across a seniority range from entry-level research engineer through principal scientist. Austin, Dallas, and Houston account for the largest share of openings, where employers like Dell Technologies, ExxonMobil, and Lockheed Martin maintain deep, sustained demand for applied ML talent. The most sought-after specialties in Texas are natural language processing, computer vision, and reinforcement learning for large-scale industrial and enterprise applications. Find a role that fits below and apply directly.
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Showing 5 of 6+ Machine Learning Scientist jobs
As a Data Scientist II, your archetype is a Business Decision Scientist. Your passions include building a framework to stitch cross domains learning and optimizing them toward mission-specific and multi-mission tasks, uncovering ML models causality relationships, creating an enterprise domain-specific reasoning system to boost actionable insights and optimize resources of machine learning process, and orchestrating reusable storytelling methodology to apply toward AI translation.
Once you're eligible, you'll become an Owner in the company, so we're looking for commitment, hard work, and focus on quality and Customer service. 'Partner-owned' means our most important resources-People-drive the innovation, growth, and success that make H-E-B The Greatest Omnichannel Retailing Company.
Do you have a:
HEART FOR PEOPLE… willingness to take a break from algorithmic thinking / detecting to translate and share your findings with a variety of business customers?
HEAD FOR BUSINESS... skills to blend mass data-wrangling with business acumen?
PASSION FOR RESULTS... drive to generate business-valued questions and data-based solutions?
We are looking for:
- a thought leader of ML innovation who orchestrates business solutions across ML pipelines
What is the work?
- Handles the design, creation, and maintenance of an ML platform and related environments.
- Manages Docker containers and Kubernetes clusters, oversees dependencies and configurations, and implements CI/CD pipelines for automated building, testing, and deployment of machine learning models.
- Monitors and optimizes model training performance and resource usage.
- Deploys ML models to production environments and manages model versioning and rollback mechanisms.
- Ensures scalable and reliable model serving using tools like Vertex, Databricks, TensorFlow Serving, Flask, or FastAPI, ensuring the infrastructure can scale to meet the growing demands as the complexity and number of ML models increase.
- Works closely with data scientists, data engineers, and stakeholders to understand and fulfill their infrastructure needs. Stays updated with the latest technologies and best practices in ML infrastructure.
- Architects and develops Generative AI solutions utilizing Machine Learning and GenAI techniques. Collaborates with leadership to identify AI opportunities and promote AI strategy. Specializes in engineering and deploying Generative AI
- Handles both unstructured and structured data, preparing it to be used as context for Language Model Learning (LLM). This involves tasks ranging from embedding large text corpora and developing generative SQL queries, to building connectors to structured databases.
- Trains models on prepared data and fine-tuning hyperparameters to achieve optimal performance.
Analytics / Design & Development:
- Builds a framework to stitch cross domains learning; optimizes them toward mission-specific and multi-mission tasks
- Serves as expert specializing in AI interpretation and causality; uncovers ML model's causality relationships; builds framework to measure each model's bias, underspecification, and latent drivers with their connections
- Creates an enterprise domain-specific reasoning system to boost actionable insights and optimize the resources of machine learning process
- Orchestrates reusable storytelling methodology to apply toward AI translation
- Applies an inquisitive nature to creating ML / AI transparency to the business
- Applies AI reasoning into business action recommendations
- Applies AI research to accelerate business innovation
What is your background?
- A related degree or comparable formal training, certification, or work experience
- 5+ years of experience in a retail or retail-related decision science role
- Expertise in ML visualization flow
- Expertise in optimizing distributed machine learning in a heterogeneous domain environment
Do you have what it takes to be a Senior Data Scientist at H-E-B?
- Technical knowledge in programming languages: SQL, R, Python, Scala, Java, C/C++
- Technical knowledge in big data / ML optimization: GPU code optimization, Horovod, Spark MLlib optimization, Cython, JNI, Numba
- Technical knowledge in mainstream ML / AI: manifold learning, distributed clustering, graph network, hierarchical model, Bayesian network, deep learning, computer vision, NLP/NLU, reinforcement learning, meta-Learning, federated learning
- Technical skills to consider and apply causal reasoning representation and learning, and human-centric, explainable, responsible AI
- Ability as a creative storyteller and translator between business questions and ML solutions
- Ability to work comfortably with imperfect or incomplete data
- Ability to apply AI reasoning into business action recommendation
Can you...
- Work in a fast-paced retail environment with frequently shifting priorities
- Work extended hours; sit for long periods
08-2021
As a Data Scientist II, your archetype is a Business Decision Scientist. Your passions include building a framework to stitch cross domains learning and optimizing them toward mission-specific and multi-mission tasks, uncovering ML models causality relationships, creating an enterprise domain-specific reasoning system to boost actionable insights and optimize resources of machine learning process, and orchestrating reusable storytelling methodology to apply toward AI translation.
Once you're eligible, you'll become an Owner in the company, so we're looking for commitment, hard work, and focus on quality and Customer service. 'Partner-owned' means our most important resources-People-drive the innovation, growth, and success that make H-E-B The Greatest Omnichannel Retailing Company.
Do you have a:
HEART FOR PEOPLE… willingness to take a break from algorithmic thinking / detecting to translate and share your findings with a variety of business customers?
HEAD FOR BUSINESS... skills to blend mass data-wrangling with business acumen?
PASSION FOR RESULTS... drive to generate business-valued questions and data-based solutions?
We are looking for:
- a thought leader of ML innovation who orchestrates business solutions across ML pipelines
What is the work?
- Handles the design, creation, and maintenance of an ML platform and related environments.
- Manages Docker containers and Kubernetes clusters, oversees dependencies and configurations, and implements CI/CD pipelines for automated building, testing, and deployment of machine learning models.
- Monitors and optimizes model training performance and resource usage.
- Deploys ML models to production environments and manages model versioning and rollback mechanisms.
- Ensures scalable and reliable model serving using tools like Vertex, Databricks, TensorFlow Serving, Flask, or FastAPI, ensuring the infrastructure can scale to meet the growing demands as the complexity and number of ML models increase.
- Works closely with data scientists, data engineers, and stakeholders to understand and fulfill their infrastructure needs. Stays updated with the latest technologies and best practices in ML infrastructure.
- Architects and develops Generative AI solutions utilizing Machine Learning and GenAI techniques. Collaborates with leadership to identify AI opportunities and promote AI strategy. Specializes in engineering and deploying Generative AI
- Handles both unstructured and structured data, preparing it to be used as context for Language Model Learning (LLM). This involves tasks ranging from embedding large text corpora and developing generative SQL queries, to building connectors to structured databases.
- Trains models on prepared data and fine-tuning hyperparameters to achieve optimal performance.
Analytics / Design & Development:
- Builds a framework to stitch cross domains learning; optimizes them toward mission-specific and multi-mission tasks
- Serves as expert specializing in AI interpretation and causality; uncovers ML model's causality relationships; builds framework to measure each model's bias, underspecification, and latent drivers with their connections
- Creates an enterprise domain-specific reasoning system to boost actionable insights and optimize the resources of machine learning process
- Orchestrates reusable storytelling methodology to apply toward AI translation
- Applies an inquisitive nature to creating ML / AI transparency to the business
- Applies AI reasoning into business action recommendations
- Applies AI research to accelerate business innovation
What is your background?
- A related degree or comparable formal training, certification, or work experience
- 5+ years of experience in a retail or retail-related decision science role
- Expertise in ML visualization flow
- Expertise in optimizing distributed machine learning in a heterogeneous domain environment
Do you have what it takes to be a Senior Data Scientist at H-E-B?
- Technical knowledge in programming languages: SQL, R, Python, Scala, Java, C/C++
- Technical knowledge in big data / ML optimization: GPU code optimization, Horovod, Spark MLlib optimization, Cython, JNI, Numba
- Technical knowledge in mainstream ML / AI: manifold learning, distributed clustering, graph network, hierarchical model, Bayesian network, deep learning, computer vision, NLP/NLU, reinforcement learning, meta-Learning, federated learning
- Technical skills to consider and apply causal reasoning representation and learning, and human-centric, explainable, responsible AI
- Ability as a creative storyteller and translator between business questions and ML solutions
- Ability to work comfortably with imperfect or incomplete data
- Ability to apply AI reasoning into business action recommendation
Can you...
- Work in a fast-paced retail environment with frequently shifting priorities
- Work extended hours; sit for long periods
08-2021
See All 6 Machine Learning Scientist Jobs in Texas
Find roles in Texas that match your experience and apply in just a few clicks.
Find JobsMachine Learning Scientist Jobs by City in Texas
Where Texas roles are concentrated, by current openings.
Machine Learning Scientist Job Market in Texas
A snapshot from current Texas openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Technology & Software
What Texas Employers Look For
The qualifications that appear most often in machine learning scientist jobs across Texas.
- Master's or PhD in computer science, statistics, or a closely related quantitative field
- Hands-on experience building and deploying machine learning models in production environments
- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or scikit-learn
- Experience with large-scale data pipelines, cloud platforms like AWS or Azure, and distributed computing
- Strong background in statistical modeling, probability theory, and applied mathematics
- Ability to communicate complex model results clearly to both technical and non-technical stakeholders
Machine Learning Scientist Jobs in Texas: Frequently Asked Questions
How do you become a machine learning scientist in Texas?
Machine learning scientist is not a licensed profession in Texas, so no state-issued credential is required to work in the role. The standard path is a bachelor's degree in computer science, mathematics, or statistics followed by a master's or doctoral degree with a research focus in machine learning. Texas universities including UT Austin, Texas A&M, and Rice University offer strong graduate programs that feed directly into in-state hiring pipelines at technology, energy, and defense employers.
How much do machine learning scientists make in Texas?
Machine learning scientists in Texas earn a median of about $122,090 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $64,540 for the lowest 10% to over $170,780 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire machine learning scientists in Texas?
Employers hiring machine learning scientists in Texas right now include SentiLink, HEB, and Photon, based on current listings on Migrate Mate as of November 2026. Texas's concentration of energy majors, defense contractors, and financial services firms means demand extends well beyond pure technology companies, making the state's ML job market unusually broad by industry.
Which Texas cities have the most machine learning scientist jobs?
Austin, Houston, and Irving have the most machine learning scientist openings in Texas. Austin drives volume through its dense cluster of technology companies and semiconductor firms, Dallas benefits from large financial services and telecom headquarters, and Houston's openings are concentrated in energy analytics and medical research at institutions affiliated with the Texas Medical Center.
Are there remote machine learning scientist jobs in Texas?
Yes, and more than most fields. About 75% of machine learning scientist openings tied to Texas are remote or hybrid as of November 2026, reflecting the role's fundamentally desk-based and collaborative-by-code nature. Research, model development, and experimentation work transfers well to remote settings, while roles involving proprietary data systems or defense clearance requirements are more likely to require on-site presence.
How can I get hired as a machine learning scientist in Texas with little or no experience?
The most realistic entry path is through a research internship or rotational program during a graduate degree, which Texas employers such as Dell, USAA, and ExxonMobil run specifically to pipeline early-career ML talent. Without a graduate degree, transitioning from a data analyst or data engineer role at a Texas company is a common lateral move, especially when paired with a portfolio of independent projects on GitHub. Completing a capstone or publishing research through a Texas university ML lab also signals readiness to hiring teams that otherwise require prior industry experience.
Where can I find and apply to machine learning scientist jobs in Texas?
Migrate Mate lists current machine learning scientist openings in Texas across industries including technology, energy, finance, and defense. Find the roles that match your background and apply directly to the ones that fit.
See All 6 Machine Learning Scientist Jobs in Texas
Find roles in Texas that match your experience and apply in just a few clicks.
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