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Google Machine Learning Engineer information
See Dallas, TX salary details
$31.2K - $45.7K
1% of jobs
$45.7K - $60.3K
1% of jobs
$60.3K - $74.9K
5% of jobs
$74.9K - $89.4K
6% of jobs
$101.5K is the 25th percentile. Wages below this are outliers.
$89.4K - $104K
14% of jobs
$104K - $118.6K
14% of jobs
The median wage is $125.9K / yr.
$118.6K - $133.1K
18% of jobs
$133.1K - $147.7K
14% of jobs
$150.7K is the 75th percentile. Wages above this are outliers.
$147.7K - $162.3K
12% of jobs
$162.3K - $176.8K
11% of jobs
$176.8K - $191.4K
5% of jobs
$31.2K
$127.4K
$191.4K
How much do google machine learning engineer jobs pay per year?
What is a Google machine learning engineer?
A Google Machine Learning Engineer designs, builds, and optimizes machine learning models to improve Google's products and services. They work with large datasets, implement algorithms, and deploy scalable AI systems. Collaboration with data scientists, software engineers, and product teams is essential to integrate models into real-world applications. Strong knowledge of Python, TensorFlow, and cloud computing is often required. This role focuses on both research and practical implementation to enhance automation and decision-making across Google products.
What skills and qualifications are needed to thrive as a Google machine learning engineer?
To thrive as a Google Machine Learning Engineer, you need strong expertise in mathematics, statistics, programming (especially Python or C++), and a solid background in computer science or a related field. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), cloud platforms (like Google Cloud), and advanced certifications can be highly beneficial. Excellent problem-solving, teamwork, and communication skills help you collaborate across teams and explain complex models to stakeholders. These skills are essential to driving innovation, building scalable solutions, and ensuring impactful results in a fast-paced, research-driven environment.
What types of projects and collaborations can Google machine learning engineers expect to be involved in?
Google Machine Learning Engineers often contribute to diverse projects, such as developing next-generation search algorithms, optimizing user experiences across products, or creating scalable machine learning systems for internal and external clients. The role frequently involves collaborating with data scientists, product managers, software engineers, and researchers to define project goals and deliver impactful solutions. You can expect to participate in code reviews, prototype new models, and provide expert input during technical discussions. This collaborative, interdisciplinary approach ensures innovative outcomes and offers ongoing opportunities for professional growth and skill development.
What are the most commonly searched types of Google Machine Learning Engineer jobs in Dallas, TX?
The most popular types of Google Machine Learning Engineer jobs in Dallas, TX are:
What are popular job titles related to Google Machine Learning Engineer jobs in Dallas, TX?
For Google Machine Learning Engineer jobs in Dallas, TX, the most frequently searched job titles are:
- Machine Learning Engineer New Grad
- Junior Machine Learning Engineer
- Senior Machine Learning Software Engineer
- Remote Machine Learning Engineer
- Machine Learning Engineer Biotech
- Flexible Remote Machine Learning Engineer
- Google Cloud Machine Learning Engineer
- Manager Edge Ai Machine Learning
- Embedded Machine Learning Engineer
- Senior Machine Learning Engineer
What job categories do people searching Google Machine Learning Engineer jobs in Dallas, TX look for?
The top searched job categories for Google Machine Learning Engineer jobs in Dallas, TX are:
What cities near Dallas, TX are hiring for Google Machine Learning Engineer jobs?
Cities near Dallas, TX with the most Google Machine Learning Engineer job openings:

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This job post has expired today. Applications are no longer accepted.
Job description
Role: Machine Learning Engineer - Fraud Detection
Location: Dallas, TX (100% Onsite)
Experience: 7-12 Years
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.
About Compugra Systems
Sourced by ZipRecruiter
Company size
11 - 50 Employees
Headquarters location
South Plainfield, NJ, US
Year founded
2002