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Machine Learning Engineer Opt Jobs in Justin, TX

Machine Learning Engineer

Frisco, TX ยท On-site

$140 - $190/hr

Overview Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team building the next generation of Intelligent Transportation Systems . In this role, you will design ...

Overview Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team building the next generation of Intelligent Transportation Systems . In this role, you will design ...

As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and work closely with senior architects to implement scalable and efficient data solutions. Your ...

As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and work closely with senior architects to implement scalable and efficient data solutions. Your ...

Machine Learning Engineer II

Irving, TX ยท On-site

$120 - $180/hr

As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and work closely with senior architects to implement scalable and efficient data solutions. Your ...

As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and work closely with senior architects to implement scalable and efficient data solutions. Your ...

Sr Machine Learning Engineer

Irving, TX ยท On-site +1

$112K - $185K/yr

CVS Shared Services Resources LLC, a CVS Health company, is hiring for the following role in Irving, TX: Sr Machine Learning Engineer to Design, develop, and implement machine learning models to ...

... the machine learning function at a market-leading insurance company. As one of the first data ... Leverage continuous engineering practices to deliver business value regarding effectiveness of the ...

Manager, Machine Learning Engineer

Dallas, TX ยท On-site

$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 ...

We are seeking a highly skilled and motivated Machine Learning Engineer to join our technology team. The ideal candidate will be responsible for designing, developing, and deploying cutting-edge ...

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

Machine Learning Developer

Dallas, TX ยท On-site

$115K - $140K/yr

Machine Learning Developer Location (city, state): Dallas, Texas - onstie 5x a week Assignment Type: Direct Hire Pay: $115,000-$140,000 annually, plus a short-term incentive and long-term incentive.

Machine Learning Developer

Dallas, TX ยท On-site

$115K - $140K/yr

Machine Learning Developer Location (city, state): Dallas, Texas - onstie 5x a week Assignment Type: Direct Hire Pay: $115,000-$140,000 annually, plus a short-term incentive and long-term incentive.

Remote Job Summary We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning environments that evaluate AI models on complex software ...

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Showing results 1-20

Machine Learning Engineer Opt information

See Justin, TX salary details

$37.2K

$152.1K

$228.5K

How much do machine learning engineer opt jobs pay per year?

As of Sep 4, 2026, the average yearly pay for machine learning engineer opt in Justin, TX is $152,074.00, according to ZipRecruiter salary data. Most workers in this role earn between $119,900.00 and $183,100.00 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 into production environments. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, reliable systems that organizations can use to make predictions or automate tasks. Their responsibilities include data preprocessing, choosing appropriate algorithms, model training, and ensuring the model's performance in real-world applications. Machine Learning Engineers often collaborate with data scientists, data engineers, and product teams to deliver intelligent solutions.

What are some common challenges machine learning engineers face when deploying models to production environments?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, handling data drift, and integrating models seamlessly with existing systems when deploying to production. Monitoring model performance in real time and retraining models as new data becomes available are also critical tasks. Collaboration with data engineers and DevOps teams is essential to address infrastructure and deployment hurdles while maintaining model accuracy and reliability.

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 a solid background in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch), data processing tools, and cloud platforms, along with relevant certifications, is highly valuable. Strong problem-solving ability, collaboration, and effective communication are standout soft skills in this role. These skills and qualities ensure the successful development, deployment, and integration of machine learning solutions that drive business value.

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

AspectMachine Learning Engineer OptData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; certifications in ML toolsBachelor's or Master's in CS, Statistics, or related fields; data analysis certifications
Work EnvironmentDevelops, tests, and deploys ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI startups, e-commerce, financeResearch institutions, tech firms, consulting, finance
Common Search & ComparisonOften compared for technical skills and deployment focusCompared for data analysis and business insights

Machine Learning Engineers Opt focus on deploying scalable ML models in production environments, while Data Scientists primarily analyze data and develop models for insights. Both roles require strong technical skills, but their core responsibilities differ in application and deployment.

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

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

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

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

Infographic showing various Machine Learning Engineer Opt job openings in Justin, TX as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 23% Part Time, and 2% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $152,074 per year, or $73.1 per hour.

Machine Learning Engineer

UNAVAILABLE

Frisco, TX โ€ข On-site

$140 - $190/hr

Other

Posted 29 days ago


Job description

Overview

Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team building the next generation of Intelligent Transportation Systems. In this role, you will design, develop, and deploy state-of-the-art computer vision and language models that power scalable, real-world solutions. Youโ€™ll work with large-scale image and video data, building and optimizing production-grade vision systems while contributing clean and modular code to shared repositories.

As part of our AI team, youโ€™ll collaborate closely with engineering teams to deliver high-impact features for our growing SaaS platform. The ideal candidate brings handsโ€‘on experience deploying computer vision and language models in production and applying MLOps best practices on cloud platforms.

Responsibilities
  • Fineโ€‘tune and deploy computer vision and deep learning models for object detection, object tracking, and OCR at scale.
  • Develop visionโ€‘language models and Mixture of Experts architectures, from experimental design through production deployment.
  • Architect Retrievalโ€‘Augmented Generation (RAG) systems, including vector store design, hybrid search strategies, chunking pipelines, and context relevance evaluation.
  • Apply MLOps best practices for training, evaluation, deployment, and monitoring of production grade computer vision models, with an emphasis on clean, modular, maintainable code.
  • Contribute to our machine learning repositories and optimize models for performance, scalability, and realโ€‘time inference across edge and cloud environments.
  • Drive performance optimization and scalability of ML systems across edge and cloud environments.
  • Collaborate with crossโ€‘functional teams to integrate computer vision solutions into endโ€‘toโ€‘end products, translating research outcomes into measurable platform impact.

This list of responsibilities might not cover everything you'll end up doing.

Qualifications
  • 5+ years of handsโ€‘on machine learning experience, with deep specialization in computer vision and a proven track record of shipping models to production.
  • Master's degree required (Ph.D. preferred) in Computer Science, Machine Learning, or a closely related field.
  • Extensive knowledge of computer vision architectures such as Vision Transformers and VLMs along with OpenCV and PIL.
  • Experience with MLOps tools (MLflow, Kubeflow, Docker, Kubernetes) able to own the full model lifecycle from experimentation through production monitoring.
  • Experience building and deploying LLM-based systems and Retrievalโ€‘Augmented Generation (RAG) pipelines, including vector store integration and retrieval evaluation.
  • Strong communicator who can translate complex research findings into actionable decisions for engineering and product stakeholders.
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