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Google Cloud Machine Learning Engineer Jobs in Dallas, TX

Rivago infotech inc

Machine Learning Engineer

Dallas, TX · On-site

Machine Learning Engineer - Fraud Detection Location: Dallas, TX (100% Onsite) Role Summary We are ... Google Cloud Platform and Databricks * Neo4j / Graph Databases and Feature Stores * Data Pipelines ...

New

Capital One

Lead Machine Learning Engineer

Plano, TX

$98K - $129K/yr

Lead Machine Learning Engineer Join the Dealer Tech division within Capital One's Financial ... Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google ...

Capital One

Lead Machine Learning Engineer

Plano, TX · On-site

$98K - $130K/yr

Lead Machine Learning Engineer Join the Dealer Tech division within Capital One's Financial ... Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google ...

UNAVAILABLE

Machine Learning Engineer

Frisco, TX · On-site

$140 - $190/hr

Overview Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team ... Drive performance optimization and scalability of ML systems across edge and cloud environments.

Capital One

Lead Machine Learning Engineer

Plano, TX

$98K - $129K/yr

Lead Machine Learning Engineer As a Capital One Lead Machine Learning Engineer (MLE), you'll be ... Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google ...

Capital One

Lead Machine Learning Engineer

Plano, TX · On-site

$98K - $129K/yr

Lead Machine Learning Engineer As a Capital One Lead Machine Learning Engineer (MLE), you'll be ... Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google ...

Quarterhill

Machine Learning Engineer

Frisco, TX · On-site

  • Medical

  • Dental

  • Retirement

  • PTO

Overview Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team ... Drive performance optimization and scalability of ML systems across edge and cloud environments.

Resolve Tech Solutions

Senior ML Engineer

Addison, TX

$101K - $138K/yr

Experience with cloud platforms such as Google Cloud Platform (GCP), including services like BigQuery, Cloud Storage, and AI Platform. GCP Professional Machine Learning Engineer certification is ...

Yum

Machine Learning Engineer II

Plano, TX · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Stay updated on emerging technologies and industry trends in machine learning, software engineering, and cloud computing, and evaluate their potential impact on our business operations.

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Google Cloud Machine Learning Engineer information

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How much do google cloud machine learning engineer jobs pay per hour?

As of Aug 17, 2026, the average hourly pay for google cloud machine learning engineer in Dallas, TX is $62.24, according to ZipRecruiter salary data. Most workers in this role earn between $53.03 and $70.91 per hour, depending on experience, location, and employer.

What is a Google Cloud Machine Learning engineer?

Google Cloud Machine Learning Engineers are professionals who design, build, and deploy machine learning models using Google Cloud Platform (GCP) services and tools. They work with large datasets, develop scalable ML solutions, and collaborate with data scientists and software engineers. Their role often includes automating data pipelines, optimizing model performance, and ensuring the reliability and security of ML deployments on the cloud. These engineers have expertise in both machine learning algorithms and cloud infrastructure, making them key contributors to data-driven projects.

What are the key skills and qualifications needed to thrive as a Google Cloud Machine Learning engineer?

To thrive as a Google Cloud Machine Learning Engineer, you need strong programming skills in Python or Java, a deep understanding of machine learning algorithms, and a degree in computer science or a related field. Familiarity with Google Cloud Platform (GCP) services such as Vertex AI, BigQuery, TensorFlow, and relevant certifications like the Professional Machine Learning Engineer certification is highly valuable. Excellent problem-solving abilities, collaboration, and clear communication make someone stand out in this position. These skills and qualities are critical for designing, deploying, and optimizing scalable ML solutions that meet business objectives in cloud environments.

What are some typical cross-functional collaborations for a Google Cloud Machine Learning engineer?

As a Google Cloud Machine Learning Engineer, you'll frequently work alongside data scientists, software engineers, and product managers to design, deploy, and maintain machine learning solutions at scale. Collaboration often involves translating business requirements into machine learning pipelines, integrating models into cloud-based applications, and ensuring that solutions are robust, secure, and scalable. Regular communication with DevOps and infrastructure teams is also common to optimize model deployment and monitor performance. This cross-disciplinary teamwork is crucial for delivering impactful, production-ready AI solutions.

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

AspectGoogle Cloud Machine Learning EngineerData Scientist
Required CredentialsGoogle Cloud certifications, programming skills, ML knowledgeStatistics, data analysis, programming, often with advanced degrees
Work EnvironmentCloud platforms, coding, deploying ML modelsData analysis, modeling, reporting, often in research or business settings
Employer & Industry UsageTech companies, cloud service providers, enterprises using Google CloudVarious industries including finance, healthcare, marketing, research

Google Cloud Machine Learning Engineers focus on developing and deploying ML models on Google Cloud, requiring cloud certifications and coding skills. Data Scientists analyze data, build models, and generate insights, often with advanced degrees. While both roles work with data and ML, the Engineer role emphasizes cloud deployment and infrastructure, whereas Data Scientists focus on data analysis and modeling.

What are popular job titles related to Google Cloud Machine Learning Engineer jobs in Dallas, TX?

For Google Cloud Machine Learning Engineer jobs in Dallas, TX, the most frequently searched job titles are:

What job categories do people searching Google Cloud Machine Learning Engineer jobs in Dallas, TX look for?

The top searched job categories for Google Cloud Machine Learning Engineer jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Google Cloud Machine Learning Engineer jobs?

Cities near Dallas, TX with the most Google Cloud Machine Learning Engineer job openings:

Google Cloud Machine Learning Engineer jobs near you
Infographic showing various Google Cloud Machine Learning Engineer job openings in Dallas, TX as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $129,449 per year, or $62.2 per hour.

Machine Learning Engineer

Rivago infotech inc

Dallas, TX • On-site

Other

Posted 3 days ago

New

Job description

Role: Machine Learning Engineer - Fraud Detection

Location: Dallas, TX (100% Onsite)

 

Role Summary

We are looking for a Machine Learning Engineer to build and support production-grade fraud detection solutions. The role focuses on real-time inference, feature engineering, APIs, graph-based fraud detection, and production deployment support.

 

Key Skills

  • Machine Learning Engineering and Real-Time Inference
  • Python, APIs, and Microservices
  • Google Cloud Platform and Databricks
  • Neo4j / Graph Databases and Feature Stores
  • Data Pipelines and Feature Engineering
  • MLOps, Monitoring, and Production Support
  • Agentic AI Architecture (good to have)

 

Responsibilities

  • Build and deploy fraud detection services for production use.
  • Develop low-latency inference solutions with a target of less than 250 ms.
  • Design feature engineering pipelines for ML use cases.
  • Integrate ML models with REST APIs and microservices.
  • Support graph-based fraud detection using Neo4j.
  • Improve scoring performance, reliability, and scalability.
  • Work with MLOps teams for releases, monitoring, and production support.
  • Support data quality, governance, and operational activities.

 

Required Qualifications

  • Hands-on experience in Python and ML model deployment.
  • Experience with APIs, microservices, and production ML systems.
  • Knowledge of data pipelines, data engineering, and feature stores.
  • Exposure to Google Cloud Platform, Databricks, Data Lake, or Data Warehouse platforms.
  • Basic understanding of MLOps, monitoring, and release support.
  • Good communication and problem-solving skills.

 

Nice to Have

  • Fraud detection, risk analytics, or scoring model experience.
  • Experience with Neo4j or graph-based ML solutions.
  • Understanding of Agentic AI architecture.

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