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Google Cloud Machine Learning Engineer Jobs in Naperville, IL

Paylocity

Machine Learning Engineer

Schaumburg, IL · On-site

Description: Paylocity is an award-winning provider of cloud-based HR and payroll software ... Machine Learning Engineer Position Overview Paylocity is growing its Machine Learning Engineering ...

Whiztek Corp

Google Cloud Engineer

Schaumburg, IL · On-site

$55 - $73.50/hr

Google Cloud Platform Cloud Engineer Location: Chicago, IL (Hybrid) We are looking for experienced Google Cloud engineers with hands-on expertise in Gemini Enterprise Agent Platform (GEAP) , Google ...

New

Paylocity

Senior Machine Learning Engineer

Schaumburg, IL · On-site

$120K - $159K/yr

Description: Paylocity is an award-winning provider of cloud-based HR and payroll software ... Senior Engineer Machine Learning Position Overview Paylocity is growing its Machine Learning ...

Paylocity

Senior Machine Learning Engineer

Schaumburg, IL · On-site

$120K - $159K/yr

Description: Paylocity is an award-winning provider of cloud-based HR and payroll software ... Senior Engineer Machine Learning Position Overview Paylocity is growing its Machine Learning ...

Whiztek Corp

AI Engineer

Schaumburg, IL · On-site

Role: Agentic AI/AI Engineer - Generative AI & Machine Learning Location: Schaumburg, IL (Hybrid ... Lead the migration of existing projects, including those with agents and LLMs, to our Google Cloud ...

Whiztek Corp

Gen AI Engineer (W2)

Schaumburg, IL · On-site

Role: Agentic AI/AI Engineer - Generative AI & Machine Learning Location:  Schaumburg, IL ... Lead the migration of existing projects, including those with agents and LLMs, to our Google Cloud ...

Accenture LLP

Google Cloud (AGBG) Sales Engineer

Chicago, IL · On-site

$57.50 - $76.75/hr

To accelerate our customers transformation leveraging cloud, we combine world-class learning and ... and engineering best practices * Deep expertise across the Google Cloud Platform ecosystem ...

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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 20, 2026, the average hourly pay for google cloud machine learning engineer in Naperville, IL is $62.79, according to ZipRecruiter salary data. Most workers in this role earn between $53.51 and $71.54 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 Naperville, IL?

The most popular types of Google Cloud Machine Learning Engineer jobs in Naperville, IL are:

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

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Google Cloud Machine Learning Engineer jobs near you

Google Cloud Platform AI/ML Engineer

CoSourcing Partners

Chicago, IL • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

Job Title: Google Cloud Platform AI/ML Engineer
Duration: 6 months Contract to hire
Location: Chicago is the preferred location, but open to candidates from anywhere in the U.S.
Role Overview
We are seeking a talented and experienced Google Cloud Platform AI/ML Engineer to design, build, and operationalize scalable machine learning solutions on Google Cloud Platform (Google Cloud Platform). This role focuses on developing production-grade ML pipelines, automating workflows, and ensuring reliability and governance across enterprise AI platforms.
The ideal candidate will have strong expertise in Vertex AI, MLOps, and cloud-native ML architectures, with a passion for turning data science models into scalable, production-ready systems.
Key Responsibilities
ML Pipeline Development & Automation
  • Build, deploy, and manage production-grade machine learning pipelines using Vertex AI Pipelines and Google Cloud Platform-native services.
  • Design automated workflows for data ingestion, feature engineering, model training, evaluation, and inference.
  • Orchestrate ML workflows using Python, Vertex AI, BigQuery, and Cloud Storage.
  • Ensure pipelines are modular, reusable, and scalable across use cases.

Model Operationalization (MLOps)
  • Operationalize the end-to-end ML lifecycle, including:
  • Model training
  • Deployment
  • Monitoring
  • Retraining and lifecycle management
  • Deploy models using Vertex AI endpoints with support for online and batch predictions.
  • Implement robust CI/CD pipelines for ML artifacts and workflows.
  • Enable automated model retraining and versioning strategies.

Data Integration & Feature Engineering
  • Enable seamless data flows across data lakes, warehouses, and ML platforms.
  • Design and manage feature pipelines for training and inference datasets.
  • Integrate with BigQuery, Cloud Storage, and streaming sources to support real-time and batch ML use cases.
  • Ensure consistency between training and serving data pipelines.

Model Monitoring & Performance Optimization
  • Implement model monitoring solutions to track:
  • Prediction accuracy
  • Data drift and concept drift
  • Model performance degradation
  • Set up alerting mechanisms and dashboards for proactive issue detection.
  • Optimize model performance and infrastructure for scalability, latency, and cost efficiency.

AI Platform Engineering
  • Build and enhance enterprise AI/ML platforms with a focus on:
  • Automation
  • Observability
  • Reliability
  • Develop standardized frameworks for repeatable and governed ML deployments.
  • Establish best practices for MLOps, pipeline orchestration, and infrastructure management.

Collaboration & Cross-Functional Engagement
  • Collaborate closely with:
  • Data Scientists to productionize models
  • Data Engineers for data pipeline integration
  • Architects for scalable cloud designs
  • Translate business requirements into deployable ML solutions.
  • Provide technical leadership and mentoring on ML engineering practices.

Governance, Security & Best Practices
  • Implement model governance frameworks including auditability, lineage, and compliance.
  • Ensure secure handling of data and models using IAM roles and access policies.
  • Promote best practices in:
    • Code versioning (Git)
    • CI/CD
    • Testing and validation
  • Drive documentation and standardization across ML workflows.

Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field.
  • 4+ years of experience in machine learning engineering or MLOps.
  • Hands-on experience with Google Cloud Platform (Google Cloud Platform) services:
  • Vertex AI (Pipelines, Training, Endpoints)
    oBigQuery
    oCloud Storage
  • Strong programming skills in Python.
  • Experience building and deploying end-to-end ML pipelines.
  • Strong understanding of ML lifecycle and MLOps principles.

Preferred Skills
  • Experience with TensorFlow, PyTorch, or Scikit-learn.
  • Familiarity with Kubeflow Pipelines or Apache Beam.
  • Experience with Docker and containerized deployments.
  • Knowledge of real-time ML inference and streaming architectures.
  • Hands-on experience with model monitoring tools and frameworks.
  • Understanding of feature stores and feature engineering pipelines.

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