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Remote Google Cloud Machine Learning Engineer Jobs in Washington, DC

Dark Wolf Solutions

Google Cloud Engineer

Herndon, VA · Remote

$57.25 - $76.50/hr

Dark Wolf is looking for Google Cloud Engineers are responsible for designing, implementing, and ... Experience with DoD/DISA cybersecurity policies This position will be hybrid / remote based out of ...

Spear AI

Machine Learning Engineer

Washington, DC · On-site +1

Deploy models to production environments in the cloud and at the edge * Build and maintain ML ... Onsite / Remote / Flexible work arrangements or hybrid options (position dependent) * Relocation ...

Halvik

Machine Learning Engineer - Remote

Vienna, VA · On-site +1

$140K - $150K/yr

... Machine Learning, Cyber Security and Cutting Edge Technology across the US Government. Be a part of ... Cloud and Platform Engineering * Leverage AWS services including S3, EC2, Lambda, SageMaker, and ...

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Remote Google Cloud Machine Learning Engineer information

See Washington, DC salary details

$26

$71

$98

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

As of Sep 7, 2026, the average hourly pay for remote google cloud machine learning engineer in Washington, DC is $71.22, according to ZipRecruiter salary data. Most workers in this role earn between $60.72 and $81.15 per hour, depending on experience, location, and employer.

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.

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 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 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.

What are the most commonly searched types of Google Cloud Machine Learning Engineer jobs in Washington, DC?

The most popular types of Google Cloud Machine Learning Engineer jobs in Washington, DC are:

Remote Google Cloud Machine Learning Engineer jobs near you
Infographic showing various Remote Google Cloud Machine Learning Engineer job openings in Washington, DC as of August 2026, with employment types broken down into 1% As Needed, 68% Full Time, 30% Part Time, and 1% Contract. Highlights an 80% Physical, 2% Hybrid, and 18% Remote job distribution, with an average salary of $148,146 per year, or $71.2 per hour.

Google Cloud Engineer

Dark Wolf Solutions

Herndon, VA • Remote

$57.25 - $76.50/hr

Full-time, Contractor

Re-posted 6 days ago

Job description

Dark Wolf is looking for Google Cloud Engineers are responsible for designing, implementing, and managing Google Cloud Platform (GCP) solutions for our customers. The ideal candidate will have a deep understanding of GCP services and technologies, as well as experience in designing and implementing scalable, reliable, and secure cloud solutions.

Responsibilities:

  • Driving and implementing technology solutions on Google Cloud.
  • Working with customers, partners, and internal project team members to understand customer business requirements and translate into actionable engineering tasks.
  • Developing and maintaining infrastructure as code (IaC) using tools such as Terraform.
  • Configuring and managing GCP services such as Compute Engine, App Engine, Google Kubernetes Engine, Cloud Storage, and Cloud SQL.
  • Architecting and implementing security best practices for GCP solutions using products such as Assured Workloads, Cloud Logging, Cloud Monitoring, and Security Command Center.
  • Staying up-to-date on the latest GCP services and technologies and their applicability to the Public Sector space.

Mid Level Required Qualifications:

  • Bachelor's degree in computer science or a related field
  • 3+ years of experience in cloud computing
  • 1+ years of experience with GCP
  • 1+ years of experience with one or more programming languages (e.g. Python)
  • Strong understanding of Google Cloud services and technologies
  • Experience leading technical teams including delegating tasks, reviewing designs and completed tasks by team members, and coaching junior team members
  • Experience designing and implementing scalable, reliable, and secure cloud solutions
  • Experience developing technical architecture documents, driving design decisions, developing documentation artifacts, and presenting technical solutions to clients and executive level leaders
  • Experience with infrastructure automation using IaC tools such as Terraform
  • Strong understanding of DevOps practices and experience implementing CI/CD in cloud environments
  • Excellent communication and teamwork skills
  • Ability to work collaboratively with the broader team as well as independently
  • US Citizenship and active Secret clearance

Mid Level Preferred Qualifications:

  • At least one Google Cloud Professional Certification
  • Experience working within Agile teams
  • Experience working with Google Cloud compliance products such as Security Command Center and Assured Workloads
  • Experience working with customers in the U.S. Public Sector
  • Experience with DoD/DISA cybersecurity policies

Senior Level Qualifications:

  • Bachelor's degree in computer science or a related field
  • 8+ years of experience in cloud computing
  • 3+ years of experience with GCP
  • 5+ years of experience with one or more programming languages (e.g. Python)
  • Strong understanding of Google Cloud services and technologies
  • Experience leading technical teams including delegating tasks, reviewing designs and completed tasks by team members, and coaching junior team members
  • Experience designing and implementing scalable, reliable, and secure cloud solutions
  • Experience developing technical architecture documents, driving design decisions, developing documentation artifacts, and presenting technical solutions to clients and executive level leaders
  • Experience with infrastructure automation using IaC tools such as Terraform
  • Strong understanding of DevOps practices and experience implementing CI/CD in cloud environments
  • Excellent communication and teamwork skills
  • Ability to work collaboratively with the broader team as well as independently
  • At least one Google Cloud Professional Certification
  • US Citizenship and active Secret clearance

Senior Level Preferred Qualifications:

  • Experience working within Agile teams
  • Experience working with Google Cloud compliance products such as Security Command Center and Assured Workloads
  • Experience working with customers in the U.S. Public Sector
  • Experience with DoD/DISA cybersecurity policies

This position will be hybrid / remote based out of a Dark Wolf Hub: Herndon VA, Tampa FL, Colorado Springs CO, Ogden UT, or Omaha NE. 

The salary range for this position is estimated to be the following, commensurate on experience and technical skillset.

  • Mid Level: $120,000.00 - $160,000.00
  • Senior Level: $160,000.00 - $190,000.00

We are proud to be an EEO/AA employer Minorities/Women/Veterans/Disabled and other protected categories.

In compliance with federal law, all persons hired will be required to verify identity, confirm US Citizenship, and complete the required employment eligibility verification upon hire.

We are strictly looking for direct, full-time W2 employees. We do not engage with third-party staffing agencies, C2C, or 1099 independent contractors for this role.