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

Optimum

Sr Machine Learning Engineer

Plano, TX · On-site

$97K - $134K/yr

Job Summary Machine Learning Engineers work to deploy end-to-end solutions to business problems leveraging AI and/or ML principles as needed to create those solutions. MLEs will take requests from ...

Vanguard

Manager, Machine Learning Engineer

Dallas, TX

$101K - $133K/yr

Leads a team of Machine Learning Engineers responsible for designing, building, deploying, and scaling AI/ML solutions that support Financial Advisory Services (FAS) business objectives. Partners ...

Covetus

Machine Learning Engineer

Irving, TX · On-site

$53.50 - $70.75/hr

Data Engineer -ML We are looking for a highly skilled and passionate Data Engineer with strong Python/PySpark experience and understanding of AL/ML models to implement AL/ML models in OnPrem and ...

New

Veho

Senior Machine Learning Platform Engineer

Dallas, TX · On-site

$103K - $142K/yr

The Senior Machine Learning Platform Engineer will work alongside data scientists and software engineers to create and maintain ML infrastructure, ensuring the deployment and performance of models in ...

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

Machine Learning Engineer information

See Keller, TX salary details

$28.6K

$116.9K

$175.7K

How much do machine learning engineer jobs pay per year?

As of Jun 16, 2026, the average yearly pay for machine learning engineer in Keller, TX is $116,948.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,200.00 and $140,800.00 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.

More about Machine Learning Engineer jobs
What are the most commonly searched types of Machine Learning Engineer jobs in Keller, TX? The most popular types of Machine Learning Engineer jobs in Keller, TX are:
What are popular job titles related to Machine Learning Engineer jobs in Keller, TX? For Machine Learning Engineer jobs in Keller, TX, the most frequently searched job titles are:
What cities near Keller, TX are hiring for Machine Learning Engineer jobs? Cities near Keller, TX with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you

AI / Machine Learning Engineer (Python)

Prophecy Technologies

Plano, TX • On-site

Full-time

Posted 19 days ago

Job description

Job Summary
We are looking for a Senior AI / Machine Learning Engineer with strong expertise in Python and end-to-end AI solution development. The role involves designing, building, deploying, and optimizing machine learning and deep learning models while contributing to scalable, secure, and high-performance application architectures.
Key Responsibilities
  • Develop, train, optimize, and evaluate machine learning and deep learning models for business use cases
  • Build and deploy end-to-end AI solutions including data ingestion, model development, testing, and production integration
  • Design, develop, and maintain high-performance Python applications and services
  • Ensure scalability, reliability, and security of AI applications
  • Build and integrate RESTful APIs and third-party services
  • Automate workflows, data processing, and reporting using Python
  • Troubleshoot complex application and database issues and implement long-term solutions
  • Contribute to system architecture decisions and technology roadmaps
  • Lead code reviews, enforce best practices, and mentor junior engineers
  • Collaborate with product owners, data analysts, and stakeholders to translate business requirements into technical solutions

Required Skills & Experience
  • Strong hands-on experience with Python for application and AI development
  • Experience developing and deploying machine learning and deep learning models
  • Knowledge of end-to-end AI/ML pipelines including data ingestion, training, evaluation, and deployment
  • Strong understanding of RESTful API design and integration
  • Experience with scalable application architectures and cloud-native services
  • Strong debugging and troubleshooting skills across application and database layers

Competencies
  • Strong analytical and problem-solving skills
  • Ability to translate business problems into technical solutions
  • Leadership and mentoring capabilities
  • Excellent communication and collaboration skills
  • Ownership mindset and attention to code quality and performance

Preferred Skills
  • Experience with cloud platforms and MLOps practices
  • Exposure to system design and architecture planning
  • Familiarity with automation frameworks and CI/CD pipelines
  • Experience working in Agile development environments

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