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Google Cloud Machine Learning Engineer Jobs in Milwaukee, WI

Deloitte

Oracle Gen AI Manager

Milwaukee, WI · On-site

... Google Cloud Agent Space, LangGraph, Glean, and other enterprise AI platforms to drive business ... machine learning initiatives * Identify high-value AI use cases and guide teams on prompt ...

Varsity Tutors

Machine Learning Tutor

Milwaukee, WI · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Pwc

AI & GenAI Data Scientist-Director

Milwaukee, WI · On-site

$155K - $410K/yr

Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials ...

Deloitte

Senior AI Engineer - SFL Scientific

Milwaukee, WI · On-site

$103K - $141K/yr

... machine learning applications. Key to this role is the ability to demonstrate both traditional data engineering expertise, leveraging cloud/enterprise/open-source solutions, and constructing ...

SmartIPlace

DevOps Engineer[remote]

Milwaukee, WI · Remote

$54 - $74/hr

SQL Server, Azure SQL DB, HDInsight/Hadoop, Machine Learning, Cosmos DB * Experience with ... Experience deploying/migrating mixed Cloud architectures (PaaS, SaaS, and/or IaaS)

Pwc

CTIO AI Engineering Manager

Milwaukee, WI · On-site

$73K - $244K/yr

... machine learning models - Developing scalable, cloud-native microservices using Docker and ... into software-engineered AI solutions - Deploying on cloud platforms like AWS, GCP, Azure ...

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All JobsGoogle Cloud Machine Learning Engineer JobsGoogle Cloud Machine Learning Engineer Jobs in Milwaukee, WI

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 10, 2026, the average hourly pay for google cloud machine learning engineer in Milwaukee, WI is $61.96, according to ZipRecruiter salary data. Most workers in this role earn between $52.84 and $70.58 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 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 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 are popular job titles related to Google Cloud Machine Learning Engineer jobs in Milwaukee, WI? For Google Cloud Machine Learning Engineer jobs in Milwaukee, WI, the most frequently searched job titles are:
What job categories do people searching Google Cloud Machine Learning Engineer jobs in Milwaukee, WI look for? The top searched job categories for Google Cloud Machine Learning Engineer jobs in Milwaukee, WI are:
What cities near Milwaukee, WI are hiring for Google Cloud Machine Learning Engineer jobs? Cities near Milwaukee, WI 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 Milwaukee, WI as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $128,872 per year, or $62 per hour.

Cloud Engineer - GCP & Google Workspace

Brady

Milwaukee, WI

$53.25 - $71/hr

Full-time

Posted 11 days ago

Brady Corporation rating

9.3

Company rating: 9.3 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

Job description

Brady Corporation is seeking an exceptional Cloud Engineer to design, build, administer, maintain, and optimize our growing multi-cloud infrastructure and Google Workspace ecosystem. In this role, you will collaborate with cross-functional software and security teams to deploy highly available, cost-effective cloud solutions while driving collaboration and productivity through Workspace optimizations. You will play a vital part in modernizing our technological footprint and establishing best practices for cloud adoption across our global operations.

  • Implement and maintain secure cloud infrastructure (primarily GCP and Azure) using Infrastructure as Code (IaC) tools such as Terraform or Ansible.
  • Administer, maintain, and optimize the Google Workspace environment (Gmail, Drive, Meet, Chat, etc.) to ensure a seamless and productive experience for end-users.
  • Manage unified user provisioning, Cloud Identity, and role-based access control (RBAC) linking GCP and Google Workspace.
  • Deploy, configure, and manage containerized applications utilizing Docker and Kubernetes (GKE/AKS).
  • Monitor cloud performance, troubleshoot complex system issues, and optimize resource utilization for FinOps cost efficiency.
  • Build, maintain, and optimize CI/CD pipelines to streamline automated deployment processes.
  • Collaborate on the migration of legacy on-premise systems and applications to modern, scalable cloud architectures.
  • Ensure all cloud deployments meet strict internal security standards and regulatory compliance frameworks.
  • Participate in incident response and root-cause analysis to ensure high availability of critical services.

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