1

Machine Learning Engineer Jobs in Houston, TX (NOW HIRING)

Partner with executive leadership, engineering, product, and data science teams to ensure AI ... Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow) * Experience ...

next page

Showing results 1-20

Machine Learning Engineer information

See Houston, TX salary details

$30.1K

$123K

$184.8K

How much do machine learning engineer jobs pay per year?

As of Jun 14, 2026, the average yearly pay for machine learning engineer in Houston, TX is $122,971.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,900.00 and $148,000.00 per year, depending on experience, location, and employer.

Is ML full of coding?

Machine Learning Engineers typically do a significant amount of coding, especially in languages like Python or R, to develop algorithms, preprocess data, and build models. Strong programming skills are essential, along with knowledge of frameworks such as TensorFlow or PyTorch, but the role also involves data analysis, model evaluation, and collaboration with teams. Coding is a core component of the job, though some tasks may involve model deployment and optimization that require different skills.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-paying industries such as finance or technology can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at large tech companies or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as they develop, implement, and maintain AI systems, requiring specialized skills in programming, data analysis, and model optimization. Roles that involve complex problem-solving, creativity, and human interaction—such as healthcare professionals, educators, skilled tradespeople, and certain managerial positions—are also expected to persist despite AI advancements. These jobs typically require emotional intelligence, adaptability, and domain expertise that AI cannot easily replicate.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

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

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in Houston, TX? The most popular types of Machine Learning Engineer jobs in Houston, TX are:
What cities near Houston, TX are hiring for Machine Learning Engineer jobs? Cities near Houston, TX with the most Machine Learning Engineer job openings:
Infographic showing various Machine Learning Engineer job openings in Houston, TX as of June 2026, with employment types broken down into 98% Full Time, and 2% Part Time. Highlights an 89% Physical, 4% Hybrid, and 7% Remote job distribution, with an average salary of $122,971 per year, or $59.1 per hour.
Machine Learning Engineer, Data & Insights, Surface & HSE

Machine Learning Engineer, Data & Insights, Surface & HSE

Chevron

Houston, TX • On-site

$109K - $131K/yr

Full-time

Posted 14 days ago


Chevron rating

6.0

Company rating: 6.0 out of 10

Based on 213 frontline employees who took The Breakroom Quiz

56th of 74 rated oil and gas companies


Job description

Total Number of Openings
1
Chevron is accepting online applications for the position Machine Learning Engineer, Data & Insights, Surface & HSE through June15th, 2025 at 11:59 p.m. (CST)
Overview
Chevron is seeking a Machine Learning Engineer to transform AI and data science concepts into scalable, production-grade solutions. You will build, deploy, and maintain machine learning systems that operate reliably at enterprise scale. Working alongside data scientists, software engineers, and cross-functional partners, you will bridge the gap between research and production to deliver AI systems aligned with strategic business objectives. Your work will drive smarter decisions and measurable outcomes across the organization, with strong emphasis on enterprise data platforms, AI-enabled transformation, and real-time analytics in complex domains such as Upstream (Surface) and Health Safety and Environment (HSE).
Responsibilities for this position may include but are not limited to:
Solution Design & Development
Identify data sources, technology stacks, and design patterns to address business challenges using AI and ML, with emphasis on Azure-based data platforms and enterprise architectures.
Partner with Data Scientists, Data Engineers, and IT teams to integrate models into enterprise data pipelines and large-scale data ecosystems.
Design scalable data and AI solutions enabling near real-time analytics and cross-domain data integration.
Model Operationalization
Transform prototypes into scalable, production-ready solutions across distributed and cloud-native environments.
Design and execute experiments to fine-tune algorithms for performance, latency, and resource efficiency, aligned with enterprise-scale workloads.
Configure and manage infrastructure for low-latency, highly available, and resilient ML workloads integrated with enterprise data platforms.
Deployment & Integration
Build, maintain, and optimize CI/CD pipelines for automated AI/ML deployments using modern DevOps and automation tooling.
Integrate models with enterprise MLOps infrastructure, APIs, and downstream business applications across multiple domains.
Leverage automation tools to operationalize workflows and improve delivery consistency.
Monitoring & Maintenance
Implement comprehensive monitoring, alerting, and exception-handling systems for deployed models and data pipelines.
Collaborate with Data Scientists and business stakeholders to ensure inference outputs drive accurate, consistent, and high-value decisions.
Proactively identify and resolve model drift, performance degradation, data quality issues, and system integration challenges.
Required Qualifications
- Bachelor's degree in Engineering, Computer Science, Data Science, or a related technical field.
- Minimum 7 years of hands-on experience in software engineering, ML engineering, or enterprise data platforms, with strong proficiency in Python.
- Proven track record of deploying machine learning models and/or enterprise data-driven platforms into production environments at scale.
- Solid understanding of the AI/ML lifecycle, including data preparation, model training, evaluation, deployment, and inference.
- Experience with Azure cloud services, including Azure Machine Learning, data platforms, and enterprise integration patterns.
- Experience building and maintaining CI/CD pipelines and applying DevOps practices for ML systems.
- Strong understanding of data governance principles (e.g., Lineage, MDM) and integration across enterprise systems.
- Demonstrated ability to troubleshoot complex distributed systems and work across cross-functional teams.
Preferred Qualifications
- Master's or Ph.D. in Engineering, Computer Science, Data Science, or a related field.
- 10+ years of relevant technical and enterprise experience in AI, data platforms, or digital transformation.
- Experience with large-scale enterprise data architectures and real-time analytics platforms.
- Deep understanding of model lifecycle management, performance optimization, and ML system design patterns in enterprise environments.
- Domain experience in Oil & Gas, including Surface, Subsurface, Wells and HSE
- Experience enabling AI adoption, defining enterprise roadmaps, and delivering measurable business value through data and AI solutions.
Relocation Options:
Relocation is not offered for this role. Only local candidates will be considered.
International Considerations:
Expatriate assignments will not be considered.
Chevron regrets that it is unable to sponsor employment Visas or consider individuals on time-limited Visa status for this position.
U.S. Regulatory notice:
Chevron is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, sex (including pregnancy), sexual orientation, gender identity, gender expression, national origin or ancestry, age, mental or physical disability, medical condition, reproductive health decision-making, military or veteran status, political preference, marital status, citizenship, genetic information or other characteristics protected by applicable law.
U.S. Regulatory notice:
Chevron is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, sex (including pregnancy), sexual orientation, gender identity, gender expression, national origin or ancestry, age, mental or physical disability, medical condition, reproductive health decision-making, military or veteran status, political preference, marital status, citizenship, genetic information or other characteristics protected by applicable law.
We are committed to providing reasonable accommodations for qualified individuals with disabilities. If you need assistance or an accommodation, please email us at emplymnt@chevron.com.
Chevron participates in E-Verify in certain locations as required by law.

What Chevron employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


Chevron logo

About Chevron

Sourced by ZipRecruiter

Chevron is one of the world's leading integrated energy companies. We believe affordable, reliable and ever-cleaner energy is essential to achieving a more prosperous and sustainable world. Chevron produces crude oil and natural gas; manufactures transportation fuels, lubricants, petrochemicals and additives; and develops technologies that enhance our business and the industry. We are focused on lowering the carbon intensity in our operations and seeking to grow lower carbon businesses along with our traditional business lines. More information about Chevron is available at www.chevron.com.

Industry

Oil and coal products manufacturing, civic and social organizations and oil and gas extraction

Company size

10,000+ Employees

Headquarters location

San Ramon, CA, US

Social media