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Data Scientist / Machine Learning Engineer, GenAI We are not accepting C2C or 1099 arrangements ... Reply STOP to opt out of receiving telephone calls and text messages from Judge and HELP for help.
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$15.25 - $18/hr
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$15.25 - $18/hr
Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously ...
Quick apply
Staff Machine Learning Engineer - Leasing
Richardson, TX ยท On-site
$15.25 - $18/hr
Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously ...
Machine Learning Engineer Opt information
See Frisco, TX salary details
$29.5K - $43.3K
1% of jobs
$43.3K - $57K
1% of jobs
$57K - $70.8K
5% of jobs
$70.8K - $84.6K
6% of jobs
$96K is the 25th percentile. Wages below this are outliers.
$84.6K - $98.4K
14% of jobs
$98.4K - $112.2K
14% of jobs
The median wage is $119.1K / yr.
$112.2K - $126K
18% of jobs
$126K - $139.8K
14% of jobs
$142.6K is the 75th percentile. Wages above this are outliers.
$139.8K - $153.5K
12% of jobs
$153.5K - $167.3K
11% of jobs
$167.3K - $181.1K
5% of jobs
$29.5K
$120.5K
$181.1K
How much do machine learning engineer opt jobs pay per year?
What are Machine Learning Engineers?
What is the difference between Machine Learning Engineer Opt vs Data Scientist?
| Aspect | Machine Learning Engineer Opt | Data Scientist |
|---|---|---|
| Required Credentials | Bachelor's or Master's in CS, AI, or related fields; certifications in ML tools | Bachelor's or Master's in CS, Statistics, or related fields; data analysis certifications |
| Work Environment | Develops, tests, and deploys ML models in production systems | Analyzes data, builds models, and provides insights for decision-making |
| Employer & Industry Usage | Tech companies, AI startups, e-commerce, finance | Research institutions, tech firms, consulting, finance |
| Common Search & Comparison | Often compared for technical skills and deployment focus | Compared 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?
What are some common challenges Machine Learning Engineers face when deploying models to production environments?

Full-time
Re-posted 21 days ago
Job description
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
โข Proficiency with Java
The ML Engineers will be supporting 3 web services applications - tech stack - Java 11 - Azure, AKS, and APIM.
โข Set up and manage Unity Catalog in Databricks to organize and secure data access across teams
โข Design and operationalize Feature Stores to support machine learning models in production
โข Build efficient data pipelines to process and serve features to ML workflows
โข Collaborate with teams using Databricks, Azure Cosmos DB, and other Azure tools to integrate data solutions
โข Monitor and optimize the performance of pipelines and feature stores
โข 5 - Strong experience with Unity Catalog in Databricks for managing data assets and access control
โข 4 - Hands-on experience working with Databricks Feature Store or similar solutions
โข 2 - Knowledge of building and maintaining scalable ETL pipelines in Databricks
โข 2- Familiarity with Azure tools like Azure Cosmos DB and ACR
โข 2- Understanding of machine learning workflows and how feature stores fit into the pipeline
โข 5- Strong problem-solving skills and a collaborative mindset
โข 3- Proficiency in Python and Spark for data engineering tasks
โข 3- Experience with monitoring tools like Splunk or Datadog to ensure system reliability
โข 2- Familiarity with AKS for deploying and managing containers
About Glow Networks
Sourced by ZipRecruiter
Industry
Telecommunications
Company size
51 - 200 Employees
Headquarters location
Dallas, TX, US
Year founded
2000