1

Machine Learning Engineer Opt Jobs in Justin, TX

Senior ML Ops Engineer

Irving, TX · On-site

$123K - $170K/yr

... end-to-end machine learning lifecycle, including model training, deployment, monitoring, and ... Engineers, and Infrastructure teams to operationalize ML solutions and improve deployment ...

Oversee teams of data scientists, modelers, and ML engineers to deliver innovative and scalable ... Strategic Leadership and Vision Provide strategic direction for the organization's machine learning ...

This role sits at the intersection of AI/ML, platform engineering, and product strategy, responsible for building scalable systems thatleveragecontext, memory, and retrieval to deliver differentiated ...

You will partner closely with Product Management, Data Science, Machine Learning Engineering, Search Engineering, Customer Experience, and senior technology leaders to deliver strategic initiatives ...

This individual will be responsible for leading a high-performing team of data scientists and machine learning engineers to tackle our most challenging business and technical problems. Leveraging ...

Senior Security Engineer

Irving, TX · On-site

$109K - $150K/yr

AI/ML certifications (e.g., Microsoft Azure AI Engineer, AWS ML Specialty, GIAC Machine Learning Engineer, ISC2 Building AI Strategy) * Experience with security telemetry and detections in SIEM or ...

Senior AI ML Engineer

Irving, TX · On-site

$100K - $137K/yr

... Machine Learning Engineering, Production ML Deployment, and end-to-end AI solution development ✔ Experience building LLM-powered RAG (Retrieval-Augmented Generation) and Agentic AI systems ✔ ...

Engineer II, AI/ML

Dallas, TX

$96K - $132K/yr

Build and maintain production machine learning capabilities spanning featureengineering, training ... Partner with engineering and product teams to turn machine learning models into mission-critical ...

We are looking for a motivated and passionate Machine Learning Engineers for our team. As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers that support ...

We are looking for a motivated and passionate Machine Learning Engineers for our team. As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers that support ...

We are looking for a motivated and passionate Machine Learning Engineers for our team. As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers that support ...

Senior AI Engineer - SFL Scientific

Dallas, TX · On-site

$103K - $142K/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 ...

AI/ML Engineer

Dallas, TX · On-site

$113K - $136K/yr

The ideal candidate should have strong expertise in machine learning algorithms, data processing ... Collaborate with data engineers, software developers, and business stakeholders to understand ...

Showing results 41-60

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.

Senior ML Ops Engineer

WTS Paradigm LLC

Irving, TX • On-site

$123K - $170K/yr

Full-time

Re-posted 22 days ago


Job description

Paradigm is a software company transforming the way that the residential, construction & building product industries operate across the globe. We are looking for a Senior ML Ops Engineer to be part of revolutionizing these industries. We are building the future with modern software engineering, agent-assisted systems, and mobile-first experiences. We are powered by our parent company, Builders FirstSource (NYSE: BLDR): a Fortune 300 company with over $23 billion in revenue and more than 29,000 employees across 550+ locations, BFS is redefining construction through data, digital infrastructure, and AI-powered innovation.
What You Will Do:
• Design, build, and maintain scalable MLOps solutions that support the end-to-end machine learning lifecycle, including model training, deployment, monitoring, and retraining.
• Develop and optimize automated ML deployment pipelines, ensuring reliable, reproducible, and efficient model delivery to production environments.
• Deploy and support machine learning models and AI solutions in production, maintaining best practices for scalability, reliability, security, and operational excellence.
• Implement and maintain model registries, experiment tracking, versioning, and governance practices to support consistent model lifecycle management.
• Build and support containerized ML workloads and deployment workflows using technologies such as Docker and Kubernetes.
• Develop monitoring, observability, and alerting capabilities for machine learning systems, including model performance tracking, drift detection, and data quality monitoring.
• Collaborate with Machine Learning Engineers, Data Scientists, Software Engineers, and Infrastructure teams to operationalize ML solutions and improve deployment efficiency.
• Implement and maintain IaC patterns using Terraform.
• Troubleshoot and resolve complex technical challenges related to model deployment, ML infrastructure, and production operations.
• Provide guidance and mentorship to other engineers.
What You Need to Succeed:
• Bachelor's degree in Computer Science, Data Science, Artificial Intelligence or related field or equivalent experience.
• 4+ years of professional experience in software engineering, machine learning engineering, MLOps, platform engineering, DevOps, or a related technical discipline.
• Strong understanding of the machine learning lifecycle, including model training, validation, deployment, monitoring, and retraining.
• Experience building and maintaining automated machine learning pipelines and CI/CD workflows.
• Experience with MLOps platforms and tools such as MLflow, Kubeflow, Azure Machine Learning, Databricks, or similar technologies.
• Experience in Python programming, ML Framework and Agentic AI. Implemented model monitoring, experiment tracking, model versioning, and governance practices.
• Experience working with cloud-based machine learning solutions, preferably within Azure.
• Ability to independently solve complex technical challenges, make sound decisions with minimal guidance, and drive work to completion.
Ready to Join? Apply now at myparadigm.com/careers/