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Google Cloud Machine Learning Engineer Jobs in Michigan

FastTek

Machine Learning Engineer

Dearborn, MI

Machine Learning Engineer #1058742 Position Description: We are seeking an experienced AI Engineer ... Experience working with cloud platforms (GCP and/or AWS) * Strong understanding of SDLC, version ...

Eccalon LLC

Machine Learning Engineer

Detroit, MI

Machine Learning Engineer Location: Detroit, MI- Onsite Type: Full-time Security Clearance: No clearance required, must be clearable. The Machine Learning Engineer will be an essential member of the ...

Corning

As a Machine Learning Engineer, you will work within a collaborative technical team to build, deploy, monitor, and maintain machine learning solutions that create measurable business value. You will ...

Corning

$95K - $130K/yr

As a Senior Machine Learning Engineer, you will design, deploy, maintain, and improve robust ... What would be a plus? - Experience with Databricks, MLflow, Kubeflow, Docker, Kubernetes, cloud or ...

Mariana Minerals

Machine Learning Engineer

Ann Arbor, MI · On-site

$120K - $180K/yr

As a Machine Learning Engineer at Mariana, you'll help build and improve the machine learning systems that control our mineral refining facilities. You'll start with well-scoped problems inside our ...

Deloitte

Cyber - Google Cloud Security - Manager

Detroit, MI · On-site

$109K - $148K/yr

... machine learning security, container security, data protection, monitoring, and secure delivery ... Serving as the primary day-to-day client contact, driving outcomes across engineering, security ...

Saanvi Technologies

Machine Learning Engineer 3

Dearborn, MI · On-site

$105K - $126K/yr

Machine Learning Engineering Engineer 3 Dearborn, MI W2 Position Description: We are seeking an ... Experience building AI solutions on cloud platforms such as GCP and/or AWS. Strong understanding of ...

Stefanini

Machine Learning Engineer

Allen Park, MI · On-site

Stefanini is looking for a Machine Learning Engineer(Allen Park, MI) For quick apply, please reach out to Navneet Pathak at / We are looking for a candidate who is responsible for predicting and/ or ...

Domino's Pizza

Lead Machine Learning Engineer

Ann Arbor, MI · On-site

$100K - $132K/yr

Promote engineering best practices across software development, MLOps, and cloud-native platforms ... Develop and operationalize machine learning and Generative AI solutions that support business ...

Domino's

Lead Machine Learning Engineer

Ann Arbor, MI · On-site

$100K - $132K/yr

Promote engineering best practices across software development, MLOps, and cloud-native platforms ... Develop and operationalize machine learning and Generative AI solutions that support business ...

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

As of Aug 31, 2026, the average hourly pay for google cloud machine learning engineer in Michigan is $54.81, according to ZipRecruiter salary data. Most workers in this role earn between $46.73 and $62.45 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 the most commonly searched types of Google Cloud Machine Learning Engineer jobs in Michigan?

The most popular types of Google Cloud Machine Learning Engineer jobs in Michigan are:

What are popular job titles related to Google Cloud Machine Learning Engineer jobs in Michigan?

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

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

The top searched job categories for Google Cloud Machine Learning Engineer jobs in Michigan are:

What cities in Michigan are hiring for Google Cloud Machine Learning Engineer jobs?

Cities in Michigan 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 Michigan as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 2% Contract, and 1% Nights. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $114,007 per year, or $54.8 per hour.

Machine Learning Engineering Senior Engineer- W2 Position-FORD- 327910

Dearborn, MI • On-site

MeganSoft
IT Services • 11 - 50 employees
Learn more about MeganSoftexternal

$96K - $132K/yr

Other

Posted 27 days ago

Job description

Position: Machine Learning Engineering Senior Engineer- 327910

Position Description:

ML Ops Build scalable and robust ML data pipelines in the cloud to process large volumes of connected vehicle data to support Ford's agentic initiatives. Optimize existing ML solutions for performance, security, and cost-effectiveness Utilize continual learning methods to continuously improve model performance Other Develop exceptional analytical data products using both streaming and batch ingestion patterns on Google Cloud Platform with solid data warehouse principles. Build data pipelines to monitoring quality of data and performance of analytical models and agentic solutions. Maintain the infrastructure of the data platform using terraform and continuously develop, evaluate, and deliver code using CI/CD. Collaborate with data analytics stakeholders to streamline the data acquisition, processing, and presentation process. Implement an enterprise data governance model and actively promote the concept of data - protection, sharing, reuse, quality, and standards. Enhance and maintain the DevOps capabilities of the data platform. Continuously optimize and enhance existing data solutions (pipelines, products, infrastructure) for best performance, high security, low vulnerability, low costs, and high reliability. Work in an agile product team to deliver code frequently using Test Driven Development (TDD), continuous integration and continuous deployment (CI/CD). Promptly address code quality issues using SonarQube, Checkmarx, Fossa, and Cycode throughout the development lifecycle. Perform any necessary data mapping, data lineage activities and document information flows. Monitor the production pipelines and provide production support by addressing production issues as per SLAs. Provide analysis of connected vehicle data to support new product developments and production vehicle improvements. Provide visibility to data quality/vehicle/feature issues and work with the business owners to fix the issues. Demonstrate technical knowledge and communication skills with the ability to advocate for well-designed solutions. Continuously enhance your domain knowledge of connected vehicle data, connected services and algorithms/models/solutions developed by data scientists and AI engineers. Stay current on the latest data engineering practices and contribute to the technical direction of the company while keeping a customer-centric approach.

Skills Required:

Technical Communication, Communications, Google Cloud Platform, TensorFlow, Data Governance, Machine Learning, Python, Artificial Intelligence & Expert Systems, GitHub, Tekton, Docker, Jira, Microservices, Data Architecture, Agile Software Development, SQL, Java, Spark, Cloud Architecture, Apache Kafka, REST APIs

1. Technical Communication This person will need to describe clearly the ML/AI Ops needs and strategy to colleagues potentially up to executives across a wide cross section of people from very knowledge to not technically knowledgeable in this area.

2. Communications In addition to the technical communication needed, this person will need to be a great communicator to work with people in other organizations who are stakeholders and we need to work together and not have there be communication gaps

3. Google Cloud Platform Deep knowledge of how to implement ML / AI Ops in the Google Cloud Platform Platform specifically is required

4. TensorFlow

5. Data Governance This role will need to implement an enterprise data governance model and actively promote the concept of data - protection, sharing, reuse, quality, and standards.

6. Machine Learning We need an ML Ops expert

7. Python Some of the ML Ops pipeline will likely need to be setup using this code

8. Artificial Intelligence & Expert Systems The ML Ops pipeline needs to be set up for AI Agentic Solutions in mind as well.

9. GitHub This is where our code will reside, so this is needed SEE 10 TO 21 IN ADDITION INFORMATION

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