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Artificial Intelligence Machine Learning Engineer Jobs in Midland, TX

... advanced artificial intelligence and machine learning systems. • Perform temporary duty aboard Navy surface ships and submarines, based on assignment requirements. What to Expect CTIs have an ...

... advanced artificial intelligence and machine learning systems. • Perform temporary duty aboard Navy surface ships and submarines, based on assignment requirements. What to Expect CTIs have an ...

... advanced artificial intelligence and machine learning systems. • Perform temporary duty aboard Navy surface ships and submarines, based on assignment requirements. What to Expect CTIs have an ...

... advanced artificial intelligence and machine learning systems. • Perform temporary duty aboard Navy surface ships and submarines, based on assignment requirements. What to Expect CTIs have an ...

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Artificial Intelligence Machine Learning Engineer information

See Midland, TX salary details

$30.9K

$126.4K

$190K

How much do artificial intelligence machine learning engineer jobs pay per year?

As of Aug 29, 2026, the average yearly pay for artificial intelligence machine learning engineer in Midland, TX is $126,424.00, according to ZipRecruiter salary data. Most workers in this role earn between $99,700.00 and $152,200.00 per year, depending on experience, location, and employer.

What is an artificial intelligence machine learning engineer?

An Artificial Intelligence (AI) Machine Learning Engineer is a professional who designs, builds, and implements machine learning models and AI systems. They work with large datasets, develop algorithms, and use programming languages like Python or R to enable computers to learn from data and make predictions or decisions. Their work is essential in fields such as natural language processing, computer vision, and robotics. These engineers collaborate with data scientists, software developers, and business stakeholders to deploy AI solutions in real-world applications.

What are some common challenges faced by artificial intelligence machine learning engineers when deploying models to production?

One of the main challenges AI/ML engineers encounter is ensuring that models trained in a controlled environment perform reliably in real-world production settings. This often involves handling issues like data drift, scaling models to handle large volumes of requests, and integrating with existing infrastructure. Collaboration with data engineers and software developers is crucial to streamline deployment, monitor model performance, and address any unexpected behavior quickly. Keeping up with evolving tools and best practices is also important for long-term model maintenance and success.

What are the key skills and qualifications needed to thrive as an artificial intelligence machine learning engineer, and why are they important?

To thrive as an Artificial Intelligence Machine Learning Engineer, you need strong programming skills (typically in Python or R), a background in mathematics or statistics, and a degree in computer science or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch, or scikit-learn), cloud platforms, and relevant certifications are highly valuable. Problem-solving ability, creativity, and effective communication are important soft skills that distinguish top performers in this role. These competencies are crucial for designing robust AI solutions, collaborating with cross-functional teams, and driving innovation in rapidly evolving technological environments.

What is the difference between Artificial Intelligence Machine Learning Engineer vs Data Scientist?

AspectArtificial Intelligence Machine Learning EngineerData Scientist
Required CredentialsBachelor's or higher in CS, AI, ML, or related; certifications like TensorFlow, AWSBachelor's or higher in CS, Statistics, or related; certifications in data analysis or visualization
Work EnvironmentDevelops AI/ML models, coding, deploying algorithms in software environmentsAnalyzes data, builds models, interprets data insights for business decisions
Employer & Industry UsageTech companies, AI startups, R&D departmentsFinance, healthcare, marketing, consulting firms

While both roles involve working with data and algorithms, Artificial Intelligence Machine Learning Engineers focus on designing, building, and deploying AI/ML models in software systems. Data Scientists primarily analyze data to extract insights and support decision-making. The roles often overlap but differ in their core focus and daily tasks.

What are popular job titles related to Artificial Intelligence Machine Learning Engineer jobs in Midland, TX?

For Artificial Intelligence Machine Learning Engineer jobs in Midland, TX, the most frequently searched job titles are:

What cities near Midland, TX are hiring for Artificial Intelligence Machine Learning Engineer jobs?

Cities near Midland, TX with the most Artificial Intelligence Machine Learning Engineer job openings:

Infographic showing various Artificial Intelligence Machine Learning Engineer job openings in Midland, TX as of August 2026, with employment types broken down into 100% Full Time. Highlights an 86% In-person, and 14% Remote job distribution, with an average salary of $126,424 per year, or $60.8 per hour.

AI Product Owner - Engineering

Diamondback Energy, Inc.

Midland, TX • On-site

$140 - $210/hr

Other

Posted 4 days ago


Diamondback Energy rating

8.7

Company rating: 8.7 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

9th of 87 rated oil and gas companies


Job description

The AI Product Owner - Engineering serves as the primary business representative for Artificial Intelligence (AI) products supporting Diamondback Energy's Drilling, Completions, Production Operations, Marketing, and related field operations workflows. This role owns the product outcome lifecycle from opportunity identification and discovery through prioritization, product definition, business validation, adoption, value realization, and continuous improvement. The Product Owner partners with AI Engineering & Delivery and other technology teams for technical feasibility, solution engineering, productionization, deployment, and technical operations. The Product Owner combines operational domain knowledge, product ownership skills, and AI fluency to deliver responsible solutions that improve operational efficiency, production optimization, field execution, commercial performance, and operational decision-making across the asset lifecycle.

Job Responsibilities: Include but are not limited to Partner with Drilling, Completions, Production Operations, Marketing, and related stakeholders to understand workflows, user needs, data requirements, controls, and operational challenges, and identify, evaluate, and prioritize AI and automation opportunities based on business value, technical feasibility, risk, data readiness, and strategic alignment. Own the business problem definition, product vision, target users and workflows, value hypothesis, roadmap, priorities, success measures, and expected business outcomes, partnering with business sponsors and the AI Value & Economics Lead to establish baseline performance and the approach for measuring realized outcomes before significant investment. Lead product discovery to validate the business problem, user needs, workflows, constraints, value hypothesis, and potential solution approaches before significant production investment, using rapid prototypes, workflow simulations, data exploration, user testing, vendor capability assessment, and other lightweight experiments to reduce uncertainty and test assumptions. Translate business needs into clear product requirements, user stories, success criteria, acceptance criteria, and AI quality expectations. Manage the product backlog and make product scope and priority decisions; lead business validation and user acceptance testing; and hold product acceptance decision rights against defined requirements, acceptance criteria, user needs, and expected business outcomes. Partner with technology, data, security, governance, legal, risk, and operational subject matter experts to deliver scalable solutions that meet responsible AI, confidentiality, data quality, and compliance requirements. Represent Drilling, Completions, Production Operations, and Marketing stakeholder needs; coordinate cross‑functional dependencies with Geoscience, Reservoir Engineering, Finance, and other business functions; and support user readiness and adoption. Own post‑launch product performance and continuous improvement by monitoring adoption, AI performance, business outcomes, realized value, user feedback, and relevant risk measures, and prioritize the changes needed to improve product outcomes.

Required Qualifications: Bachelor's degree in Petroleum Engineering, Mechanical Engineering, Chemical Engineering, Industrial Engineering, Data Science, Information Systems, or a related technical field 5+ years of experience supporting drilling, completions, production operations, marketing, or a closely related operational function within oil and gas Experience leading cross‑functional technology, analytics, process improvement, or business transformation initiatives in an operational environment Experience translating operational workflows and business needs into clear requirements, priorities, and measurable outcomes Experience working with operational, engineering, production, field, commercial, or analytics data and systems, including technologies such as Spotfire, WellView, Ignition, Snowflake, XSPOC, Python, Power BI, and similar platforms Experience delivering or supporting AI, machine learning, automation, data, or analytics products from concept through adoption.

Preferred Qualifications: Direct Product Owner experience managing product roadmaps, backlogs, user stories, and acceptance criteria in an Agile environment Experience using AI‑enabled rapid product discovery techniques, including prototyping, user testing, workflow simulation, data exploration, or lightweight experimentation to test assumptions and reduce uncertainty before development. Knowledge of AI model evaluation, operational data practices, responsible AI governance, model risk controls, and data quality requirements Experience leading digital transformation or change adoption across Drilling, Completions, Production Operations, Marketing, or field operations teams Familiarity with drilling performance optimization, completions execution, production surveillance, artificial lift optimization, production forecasting, operational reporting, and field data management workflows.

Diamondback is an Equal Employment Opportunity Employer. Diamondback provides equal employment opportunities to all qualified applicants without regard to race, sex, sexual orientation, gender identity, national origin, color, age, religion, veteran or disability status, genetic information, pregnancy, or any other status protected by law.

Diamondback participates in E‑Verify. Learn more about E‑Verify.

Diamondback Energy is an independent oil and natural gas company headquartered in Midland, Texas focused on the acquisition, development, exploration, and exploitation of unconventional, onshore oil and natural gas reserves in the Permian Basin in West Texas.

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