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Remote Google Cloud Machine Learning Engineer Jobs in Chicago, IL

Career Renew

Senior Machine Learning Engineer

Chicago, IL · Remote

$165K - $225K/yr

... Machine Learning Engineer - this is a fully remote role for US/Canada based candidates. Salary ... cloud ML infrastructure (AWS, GCP, or Azure) Knowledge of handling large scale image data, data ...

Burwood Group, Inc

Sr. Consultant, Cloud Solutions

Chicago, IL · On-site +1

$60.25 - $82.25/hr

Active Google Cloud Professional Certifications (e.g., Cloud Architect, Cloud DevOps Engineer, Machine Learning Engineer) are highly preferred. CNCF (CKA/CKAD) or HashiCorp certifications are a ...

Adidev Technologies

AI/ML

Des Plaines, IL · Remote

Adidev is looking for an adept Machine Learning Engineer to take the helm in deploying advanced ... Azure, Google Cloud and OpenAI. * Software engineering and/or Data Engineering background ...

Adidev Technologies Inc

AI/ML

Chicago, IL · Remote

Adidev is looking for an adept Machine Learning Engineer to take the helm in deploying advanced ... such as AWS, Databricks, Azure, Google Cloud and OpenAI. Software engineering and/or Data ...

NinjaTrader

Staff Cloud Security Engineer

Chicago, IL · On-site +1

$145K - $195K/yr

You will set the technical direction for how we secure workloads across Google Cloud Platform (GCP ... We are not open to remote candidates for this role. Hybrid: For Chicago-based employees, we follow ...

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Remote Google Cloud Machine Learning Engineer information

See Chicago, IL salary details

$24

$64

$89

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

As of Jul 26, 2026, the average hourly pay for remote google cloud machine learning engineer in Chicago, IL is $64.78, according to ZipRecruiter salary data. Most workers in this role earn between $55.24 and $73.80 per hour, depending on experience, location, and employer.

What is the difference between Remote Google Cloud Machine Learning Engineer vs Remote AWS Machine Learning Engineer?

AspectRemote Google Cloud Machine Learning EngineerRemote AWS Machine Learning Engineer
Required CredentialsGoogle Cloud certifications, Python, ML frameworksAWS certifications, Python, ML frameworks
Work EnvironmentGoogle Cloud Platform, GCP toolsAWS Cloud, AWS tools
Industry UsageTech, finance, healthcare using GCPTech, retail, finance using AWS
Search & Comparison IntentHigh overlap in cloud-based ML rolesSimilar roles in cloud ML, different platform

Both roles involve developing machine learning models in cloud environments, requiring cloud platform certifications and expertise in Python and ML frameworks. The main difference lies in the cloud platform used: Google Cloud vs AWS. Candidates should choose based on their platform familiarity and employer requirements.

How does a Remote Google Cloud Machine Learning Engineer typically collaborate with cross-functional teams?

As a Remote Google Cloud Machine Learning Engineer, collaboration often happens through virtual meetings, shared documentation, and cloud-based development environments. You'll regularly interact with data scientists, software developers, and product managers to align machine learning solutions with business objectives. Clear communication and proactive updates are essential, as you may work across time zones and need to coordinate on project requirements, data pipelines, and model deployment strategies. Tools such as Google Meet, Slack, and shared code repositories like Git are commonly used to facilitate seamless teamwork.

What does a Remote Google Cloud Machine Learning Engineer do?

A Remote Google Cloud Machine Learning Engineer designs, develops, and deploys machine learning models on Google Cloud Platform (GCP) from a remote location. They work with cloud-based tools and services such as TensorFlow, Vertex AI, BigQuery, and Dataflow to build scalable, production-ready ML solutions. Their responsibilities also include data preprocessing, model training and evaluation, and integrating ML solutions with other cloud services. Collaboration with data scientists, software engineers, and stakeholders is a key part of the role, ensuring that ML solutions meet business goals while leveraging the full capabilities of Google Cloud.

What are the key skills and qualifications needed to thrive as a Remote Google Cloud Machine Learning Engineer, and why are they important?

To thrive as a Remote Google Cloud Machine Learning Engineer, you need expertise in machine learning algorithms, data analysis, and proficiency in programming languages like Python, along with a degree in computer science or a related field. Familiarity with Google Cloud Platform (GCP) services such as Vertex AI, BigQuery, and TensorFlow, as well as relevant certifications like Google Professional Machine Learning Engineer, is highly valued. Strong problem-solving skills, self-motivation, and effective remote communication set top performers apart in this role. These competencies are critical for building scalable ML solutions, collaborating remotely, and delivering impactful results using cloud technologies.
What are the most commonly searched types of Google Cloud Machine Learning Engineer jobs in Chicago, IL? The most popular types of Google Cloud Machine Learning Engineer jobs in Chicago, IL are:
Remote Google Cloud Machine Learning Engineer jobs near you
GCP Site Reliability Engineer

GCP Site Reliability Engineer

TekCommands Inc

Buffalo Grove, IL • Remote

$58.25 - $77.50/hr

Contractor

Posted 15 days ago

Job description

Job Title: Google Site Reliability Engineer (SRE)

Location: Remote or Hybrid (Buffalo Grove, IL)
Job Type: Contract

Job Summary

We are seeking a Google Site Reliability Engineer (SRE) to build, operate, and support highly available, scalable, and secure cloud services on Google Cloud Platform (GCP). The ideal candidate will have strong experience in incident management, observability, automation, and cloud operations with expertise in GCP technologies.

Key Responsibilities

  • Monitor production systems and manage incident detection, logging, and resolution while meeting SLA targets.

  • Lead bridge calls and communications for P1/P2 incidents.

  • Perform root cause analysis (RCA) and prepare postmortem reports.

  • Build and maintain monitoring dashboards, alerts, and observability using Prometheus, Grafana, and Splunk.

  • Automate operational tasks to improve reliability and reduce manual effort.

  • Define and manage SLIs, SLOs, and error budgets.

  • Participate in on-call rotations and ensure timely incident mitigation and recovery.

  • Collaborate with development and operations teams to improve system reliability and performance.

  • Support problem management and continuous service improvements.

Required Skills

  • Strong experience with Google Cloud Platform (GCP) services including:

    • BigQuery

    • Cloud Storage

    • Dataproc

    • GKE (Google Kubernetes Engine)

    • Airflow/Cloud Composer

    • Pub/Sub

    • Cloud Functions

    • Cloud SQL

  • Experience with Prometheus, Grafana, and Splunk.

  • Proficiency with GitHub and Visual Studio Code.

  • Familiarity with Microsoft Copilot.

  • Strong understanding of Incident Management, Problem Management, and Agile methodologies.

  • Excellent communication, analytical, and troubleshooting skills.

Nice to Have

  • Python, PySpark, or Machine Learning experience.

  • Experience with Tidal, ServiceNow, xMatters, Ab Initio, Tableau, Opsgenie, and Zeke.

Top 3 Skills

  1. Google Cloud Platform (GCP)

  2. Site Reliability Engineering (SRE) & Incident Management

  3. Prometheus, Grafana & Splunk Monitoring

Work Location: Remote or Hybrid (Buffalo Grove, IL)