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

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AI Engineer

Houston, TX

As an AI Engineer on this team, you will partner with AI and Machine Learning Engineers across the ... Experience implementing machine learning frameworks and libraries. * Development experience with ...

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Deloitte

Senior AI Engineer - SFL Scientific

Houston, TX

$99K - $137K/yr

Work You'll Do As a Senior AI Engineer, you'll work cross-functionally with data scientists, machine learning engineers, project managers, and industry experts to develop robust AI infrastructure and ...

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All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Katy, TX

Machine Learning Engineer information

See Katy, TX salary details

$28.9K

$118.1K

$177.5K

How much do machine learning engineer jobs pay per year?

As of Aug 16, 2026, the average yearly pay for machine learning engineer in Katy, TX is $118,144.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,100.00 and $142,200.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

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.

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.

More about Machine Learning Engineer jobs

What are the most commonly searched types of Machine Learning Engineer jobs in Katy, TX?

The most popular types of Machine Learning Engineer jobs in Katy, TX are:

What are popular job titles related to Machine Learning Engineer jobs in Katy, TX?

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

What job categories do people searching Machine Learning Engineer jobs in Katy, TX look for?

The top searched job categories for Machine Learning Engineer jobs in Katy, TX are:

What cities near Katy, TX are hiring for Machine Learning Engineer jobs?

Cities near Katy, TX with the most Machine Learning Engineer job openings:

Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Katy, TX as of August 2026, with employment types broken down into 78% Full Time, and 22% Contract. Highlights an 100% In-person job distribution, with an average salary of $118,144 per year, or $56.8 per hour.

Product Manager (Specialized in Machine Learning)

Carter Support Services

Houston, TX • On-site

Other

Re-posted 3 days ago

Job description

Product Manager (Machine Learning)

Company: Carter Support Services
Location: Houston
 

Position Summary

We are seeking a Product Manager with deep experience in machine learning–driven products to lead the strategy, development, and lifecycle of ML-powered solutions. This role sits at the intersection of business, data science, engineering, and user experience, translating complex machine learning capabilities into scalable, valuable, and user-friendly products.

The ideal candidate understands both product management fundamentals and machine learning concepts, and can effectively guide teams through experimentation, model iteration, and production deployment while keeping a strong focus on customer outcomes and business impact.

Key Responsibilities

  • Define product vision, strategy, and roadmap for machine learning–based products

  • Translate business problems into ML product requirements and measurable success metrics

  • Partner closely with data science and engineering teams to guide model development, training, evaluation, and deployment

  • Own product discovery, including user research, hypothesis testing, and experimentation

  • Define and prioritize features using data, experimentation results, and business impact

  • Establish KPIs for ML products, including model performance, business outcomes, and user adoption

  • Manage product lifecycle from concept through launch, iteration, and scale

  • Communicate product strategy and progress to stakeholders and executive leadership

  • Ensure responsible AI practices, including fairness, transparency, and compliance considerations

Required Qualifications

  • 4+ years of product management experience, with at least 2 years working on machine learning or data-driven products

  • Strong understanding of machine learning concepts (e.g., supervised vs. unsupervised learning, model evaluation, training pipelines)

  • Experience working with data scientists, ML engineers, and software engineers

  • Ability to translate technical concepts into clear product requirements and user value

  • Experience defining success metrics and using data to drive product decisions

  • Excellent communication, stakeholder management, and prioritization skills

Preferred Qualifications

  • Experience launching ML products into production at scale

  • Familiarity with MLOps practices and model lifecycle management

  • Experience with cloud-based ML platforms (AWS, GCP, Azure)

  • Background in AI-driven products such as recommendations, forecasting, NLP, or computer vision

  • Knowledge of regulatory, ethical, and responsible AI considerations

Core Competencies

  • Strategic thinking with strong execution focus

  • Data-driven decision making

  • Customer-centric mindset

  • Ability to manage ambiguity and complex problem spaces

  • Strong cross-functional leadership

Why Join Us

  • Build innovative products powered by cutting-edge machine learning

  • Work closely with talented data science and engineering teams

  • High ownership and visibility across the organization

  • Competitive compensation, benefits, and growth opportunities

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