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Machine Learning Jobs in Forney, TX (NOW HIRING)

System One

Machine Learning Operations Engineer

Dallas, TX · On-site

$68K - $93K/yr

Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Visa : USC, GC, EAD (Only W2, No Sponsorship) Responsibilities * Optimize and maintain large-scale feature ...

AppFolio

Sr. Machine Learning Engineer

Richardson, TX

$94K - $129K/yr

Who We Are Looking For We're hiring a Senior Machine Learning Engineer to design and ship the next generation of voice and conversational AI agents within Realm-X. This role helps define AppFolio ...

AppFolio

Sr. Machine Learning Engineer

Richardson, TX

$94K - $129K/yr

Who We Are Looking For We're hiring a Senior Machine Learning Engineer to design and ship the next generation of voice and conversational AI agents within Realm-X. This role helps define AppFolio ...

Equinix

AI/Machine Learning Engineer

Dallas, TX · Remote

Bachelor's degree in Computer Science, Machine Learning, Data Science, or a related field The targeted pay range for this position in the following location is / locations are: United States - Dallas ...

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All JobsMachine Learning JobsMachine Learning Jobs in Forney, TX

Machine Learning information

See Forney, TX salary details

$23K

$38.4K

$79.3K

How much do machine learning jobs pay per year?

As of Aug 6, 2026, the average yearly pay for machine learning in Forney, TX is $38,362.00, according to ZipRecruiter salary data. Most workers in this role earn between $29,300.00 and $41,400.00 per year, depending on experience, location, and employer.

What is a machine learning?

A Machine Learning job involves developing algorithms and models that enable computers to learn from data and make predictions or decisions without explicit programming. Professionals in this field work with large datasets, design and train machine learning models, and optimize them for performance and accuracy. Roles often require knowledge of programming languages like Python or R, experience with frameworks like TensorFlow or PyTorch, and an understanding of statistics and data science principles. Machine learning engineers and data scientists collaborate with software developers and domain experts to build AI-driven solutions for various industries.

What are the typical day-to-day responsibilities in a machine learning role?

As a machine learning professional, your daily tasks may include data preprocessing, developing and training models, evaluating performance metrics, and experimenting with algorithms to optimize results. You’ll often collaborate closely with data scientists, software engineers, and business stakeholders to align technical solutions with organizational goals. Regular activities can also involve deploying models to production, monitoring performance, and troubleshooting any issues that arise post-deployment. Staying up to date with recent ML research and participating in team discussions or code reviews are also common parts of the job.

What jobs can I get with machine learning?

With a background in machine learning, you can pursue roles such as machine learning engineer, data scientist, AI researcher, or data analyst. These positions typically require skills in programming languages like Python or R, knowledge of algorithms, and experience with tools like TensorFlow or PyTorch.

What are the key skills and qualifications needed to thrive in a machine learning position?

To thrive in Machine Learning, you need a solid background in mathematics, statistics, programming (especially Python or R), and a formal degree in computer science, data science, or a related field. Experience with popular ML frameworks (such as TensorFlow, PyTorch, or Scikit-learn), version control, and relevant certifications like AWS Certified Machine Learning are highly valued. Strong problem-solving skills, curiosity, clear communication, and the ability to work both independently and within multidisciplinary teams make candidates stand out. These skills and qualities are essential for developing robust models, staying updated with technology advancements, and collaborating effectively on complex projects.

What job categories do people searching Machine Learning jobs in Forney, TX look for? The top searched job categories for Machine Learning jobs in Forney, TX are:
What cities near Forney, TX are hiring for Machine Learning jobs? Cities near Forney, TX with the most Machine Learning job openings:
Machine Learning jobs near you
Infographic showing various Machine Learning job openings in Forney, TX as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 19% Part Time, 1% Temporary, and 7% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution, with an average salary of $38,362 per year, or $18.4 per hour.

Machine Learning Operations Engineer

System One

Farmers Branch, TX • On-site

$66K - $90K/yr

Other

Posted 28 days ago

Job description

Job Title: Machine Learning Operations Engineer
Location: Dallas, Texas
Type: Contract To Hire
Visa : (Only W2, No Sponsorship)

Responsibilities

  • Optimize and maintain large-scale feature engineering pipelines using PySpark, Pandas, and PyArrow on Hadoop-based infrastructure.
  • Refactor and modularize ML codebases to enhance reusability, maintainability, and performance.
  • Collaborate with platform teams on compute capacity planning, resource allocation, and system upgrades.
  • Integrate with existing model serving frameworks to support testing, deployment, and rollback processes.
  • Monitor and troubleshoot production ML pipelines, ensuring high reliability, low latency, and cost efficiency.
  • Contribute to internal ML platforms by sharing insights, proposing improvements, and documenting best practices.
  • Build near real-time ML pipelines using Kafka and Spark Streaming.
  • Work with AWS and SageMaker MLOps ecosystem.
Requirements
  • 6+ years of experience in software engineering, data engineering, or MLOps roles.
  • Strong programming expertise in Python, with hands-on experience in Pandas, PySpark, and PyArrow.
  • Deep understanding of the Hadoop ecosystem, distributed computing, and performance tuning.
  • Experience with CI/CD pipelines and best practices in ML environments.
  • Hands-on experience with monitoring tools for ML pipeline health and performance.
  • Strong collaboration skills with experience working in cross-functional teams (platform, data science, engineering).
  • Experience contributing to or building internal MLOps frameworks/platforms.
  • Familiarity with SLURM clusters or other distributed job schedulers.
  • Exposure to Kafka, Spark Streaming, or other real-time data processing technologies.
  • Understanding of ML lifecycle management, including versioning, deployment, and drift detection.

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