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Machine Learning Engineer Opt Jobs in The Colony, TX

Photon

Machine Learning Engineer - NJ

Addison, TX · On-site

$54 - $71.50/hr

We are seeking a Machine Learning Engineer to design and develop robust analytics models using statistical and machine learning algorithms. In this role, you will work closely with product and ...

Photon

Machine Learning Engineer - NJ

Addison, TX · On-site

$54 - $71.50/hr

We are seeking a Machine Learning Engineer to design and develop robust analytics models using statistical and machine learning algorithms. In this role, you will work closely with product and ...

Vanguard

Manager, Machine Learning Engineer

Dallas, TX · 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 ...

Equinix

AI/Machine Learning Engineer

Dallas, TX · Remote

... ML engineering, or related roles, with demonstrated experience in building and integrating production-grade systems * Bachelor's degree in Computer Science, Machine Learning, Data Science, or a ...

Equinix, Inc.

AI/Machine Learning Engineer

Dallas, TX · On-site

... ML engineering, or related roles, with demonstrated experience in building and integrating production-grade systems * Bachelor's degree in Computer Science, Machine Learning, Data Science, or a ...

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All JobsMachine Learning Engineer Opt JobsMachine Learning Engineer Opt Jobs in The Colony, TX

Machine Learning Engineer Opt information

See The Colony, TX salary details

$29.2K

$119.4K

$179.4K

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

As of Jul 16, 2026, the average yearly pay for machine learning engineer opt in The Colony, TX is $119,408.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,100.00 and $143,700.00 per year, depending on experience, location, and employer.

What are Machine Learning Engineers?

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 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 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 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.
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Machine Learning Engineer Opt jobs near you
Machine Learning Engineer, Unity Catalog

Machine Learning Engineer, Unity Catalog

Glow Networks

Dallas, TX • On-site

Full-time

Posted 26 days ago

Job description

Job Description: Machine Learning Engineer, Unity Catalog
Location: Remote (PST)
Duration: 10 Months
We are looking for an ML engineer with expertise in Unity Catalog and Feature Store in Databricks to help us build and maintain a solid foundation for our data and machine learning workflows. You will work on organizing data, managing access, and enabling machine learning models to operate efficiently in production
• Proficiency with Java
The ML Engineers will be supporting 3 web services applications - tech stack - Java 11 - Azure, AKS, and APIM.
• Set up and manage Unity Catalog in Databricks to organize and secure data access across teams
• Design and operationalize Feature Stores to support machine learning models in production
• Build efficient data pipelines to process and serve features to ML workflows
• Collaborate with teams using Databricks, Azure Cosmos DB, and other Azure tools to integrate data solutions
• Monitor and optimize the performance of pipelines and feature stores
• 5 - Strong experience with Unity Catalog in Databricks for managing data assets and access control
• 4 - Hands-on experience working with Databricks Feature Store or similar solutions
• 2 - Knowledge of building and maintaining scalable ETL pipelines in Databricks
• 2- Familiarity with Azure tools like Azure Cosmos DB and ACR
• 2- Understanding of machine learning workflows and how feature stores fit into the pipeline
• 5- Strong problem-solving skills and a collaborative mindset
• 3- Proficiency in Python and Spark for data engineering tasks
• 3- Experience with monitoring tools like Splunk or Datadog to ensure system reliability
• 2- Familiarity with AKS for deploying and managing containers

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