ZipRecruiter

Log In

2

Part Time Google Cloud Machine Learning Engineer Jobs

Booz Allen Hamilton, Inc.

Data Engineer, Senior

Tampa, FL · On-site +1

$99K - $225K/yr

  • Medical

  • Life

  • Retirement

  • PTO

As a machine learning engineer on our data team, you'll train, test, deploy, and maintain models ... Experience with a public cloud such as AWS, Microsoft Azure, or Google Cloud * Ability to develop ...

Booz Allen Hamilton, Inc.

Data Engineer, Mid

Tampa, FL · On-site +1

$61K - $141K/yr

  • Medical

  • Life

  • Retirement

  • PTO

As a machine learning engineer on our data team, you'll train, test, deploy, and maintain models ... Experience with public clouds such as AWS, Microsoft Azure, or Google Cloud * Ability to develop ...

Booz Allen Hamilton, Inc.

AI/ML Engineer, Lead

Ashburn, VA · On-site +1

$104K - $138K/yr

  • Medical

  • Life

  • Retirement

  • PTO

As an AI/Machine Learning Engineer, you'll lead the design, development, and deployment of advanced ... Experience with cloud platforms, such as AWS, Azure, or GCP, and tools for scalable ML systems ...

Booz Allen Hamilton, Inc.

AI Engineer

Arlington, VA · On-site +1

$77K - $176K/yr

  • Medical

  • Life

  • Retirement

  • PTO

As a machine learning engineer on our Global Defense team, you'll train, test, deploy, and maintain ... Experience with cloud platforms including AWS, Azure, or GCP and their AI or ML services

previous pagenext page

Showing results 21-40

All Jobspart time JobsTechnology JobsData JobsData Scientist JobsPart Time Google Cloud Machine Learning Engineer Jobs

Part Time Google Cloud Machine Learning Engineer information

See salary details

$23

$62

$87

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

As of Aug 18, 2026, the average hourly pay for part time google cloud machine learning engineer in the United States is $62.89, according to ZipRecruiter salary data. Most workers in this role earn between $53.61 and $71.63 per hour, depending on experience, location, and employer.

What does a part time Google Cloud Machine Learning Engineer do?

A Part Time Google Cloud Machine Learning Engineer designs, develops, and deploys machine learning models using Google Cloud Platform services. They collaborate with teams to analyze data, build scalable solutions, and optimize machine learning workflows, all while working on a part-time schedule. Their responsibilities often include data preprocessing, model training, evaluation, and integrating models into production environments using Google Cloud tools like AI Platform, BigQuery, and Dataflow.

What are the key skills and qualifications needed to thrive as a part time Google Cloud Machine Learning Engineer?

To thrive as a Part Time Google Cloud Machine Learning Engineer, you need a solid background in machine learning, data analysis, Python programming, and a relevant degree in computer science or a related field. Experience with Google Cloud Platform (GCP) services like AI Platform, BigQuery, TensorFlow, and relevant certifications such as Google Professional Machine Learning Engineer are highly valuable. Strong problem-solving abilities, communication skills, and the ability to work independently are essential soft skills for balancing technical demands with part-time flexibility. These skills are crucial to designing, deploying, and optimizing machine learning solutions efficiently on GCP while collaborating effectively in a part-time capacity.

How does working part time as a Google Cloud Machine Learning Engineer affect collaboration and project involvement?

Part-time Google Cloud Machine Learning Engineers typically work closely with cross-functional teams, such as data scientists, software developers, and cloud architects. While the part-time schedule offers flexibility, it may require proactive communication to stay aligned with ongoing projects and ensure smooth handoffs. Engineers in this role often contribute to specific phases of the machine learning pipeline, such as model deployment or optimization, and attend regular meetings to stay connected. Balancing part-time hours with project demands can be challenging, but clear expectations and strong team coordination help ensure meaningful contributions and professional growth.

What is the difference between Part Time Google Cloud Machine Learning Engineer vs Part Time Data Scientist?

AspectPart Time Google Cloud Machine Learning EngineerPart Time Data Scientist
Required SkillsGoogle Cloud ML tools, Python, ML algorithmsData analysis, statistical skills, Python/R
CertificationsGoogle Cloud certifications preferredData science certifications beneficial
Work EnvironmentCloud platforms, remote or on-siteData analysis projects, remote or on-site
Industry UsageTech, AI, cloud servicesFinance, healthcare, marketing

While both roles involve data handling and programming, the Part Time Google Cloud Machine Learning Engineer focuses on deploying ML models on Google Cloud, whereas the Part Time Data Scientist emphasizes data analysis and insights across various industries.

More about Part Time Google Cloud Machine Learning Engineer jobs

What cities are hiring for Part Time Google Cloud Machine Learning Engineer jobs?

Cities with the most Part Time Google Cloud Machine Learning Engineer job openings:

What are the most commonly searched types of Google Cloud Machine Learning Engineer jobs?

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

What job categories do people searching Part Time Google Cloud Machine Learning Engineer jobs look for?

The top searched job categories for Part Time Google Cloud Machine Learning Engineer jobs are:

Part Time Google Cloud Machine Learning Engineer jobs near you
Infographic showing various Part Time Google Cloud Machine Learning Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $130,802 per year, or $62.9 per hour.

Machine Learning Engineer - Model Evaluation & Experimentation

Weekday AI

Remote

$60 - $90/hr

Part-time

Posted 19 days ago

Job description

This role is for one of our clients
Compensation: $60-$90 per hour
Join a pioneering AI initiative focused on building the next generation of evaluation benchmarks for frontier AI models. We are seeking experienced Machine Learning Engineers and Researchers to bring hands-on expertise in model development, experimentation, and evaluation to create rigorous benchmark tasks for advanced AI systems.
In this role, you will design sophisticated, multi-step machine learning challenges inspired by real-world research workflows. From implementing experimental ideas and running training pipelines to analyzing model behavior and validating results, you will help establish high-quality evaluation benchmarks that reveal the strengths and limitations of frontier AI models.
This is a fully remote, full-time engagement requiring approximately 35 hours per week.
Requirements
Key Responsibilities
  • Design realistic machine learning benchmark tasks based on research workflows, including model implementation, experimentation, training, evaluation, and performance analysis.
  • Translate open-ended research concepts into structured, reproducible evaluation tasks with clearly defined success criteria.
  • Implement machine learning solutions using Python, execute experiments, and produce reference implementations that demonstrate correct methodology and expected outcomes.
  • Develop benchmark tasks involving reinforcement learning concepts such as reward functions, policy optimization, training dynamics, and model behavior where applicable.
  • Evaluate AI-generated solutions by identifying implementation errors, experimental flaws, incorrect reasoning, and unsupported conclusions.
  • Collaborate with AI researchers and fellow subject matter experts to continuously improve benchmark quality, technical rigor, and evaluation consistency.
Required Qualifications
  • Master's degree, PhD, or equivalent practical experience in Machine Learning, Computer Science, Artificial Intelligence, Data Science, or another quantitative STEM discipline.
  • Minimum 1 year of professional experience in machine learning research, research engineering, applied AI, or another research-intensive technical role.
  • Strong hands-on experience designing, training, evaluating, and optimizing machine learning models through complete experimental workflows.
  • Practical experience conducting machine learning experiments, including experiment setup, hyperparameter tuning, execution, validation, and analysis.
  • Strong understanding of modern Large Language Models (LLMs), their capabilities, limitations, and evaluation methodologies.
  • Proficiency in Python and Git, with experience working in both script-based and notebook-based development environments.
  • Familiarity with reinforcement learning concepts-including reward functions, policy optimization, and training behavior-is preferred.
  • Experience with AI evaluation, benchmark development, AI training, or task authoring is highly desirable.
  • Excellent analytical thinking, creativity, attention to detail, and the ability to solve complex, open-ended technical problems independently.
  • Strong written communication skills for documenting experimental methodologies and technical findings.
  • Ability to commit approximately 35 hours per week on a consistent basis.
Preferred Qualifications
  • Experience developing or evaluating large language models, foundation models, or generative AI systems.
  • Background in reinforcement learning, deep learning, distributed training, or model optimization.
  • Familiarity with benchmark design, AI safety evaluations, or research-quality experimentation.
  • Experience contributing to research publications, open-source machine learning projects, or advanced AI systems.
Why Join
  • Help shape how next-generation AI systems are evaluated through rigorous machine learning experimentation.
  • Collaborate with leading AI researchers developing frontier evaluation benchmarks.
  • Apply your expertise to improve AI reasoning, model quality, and experimental reliability.
  • Contribute directly to benchmark development that advances the capabilities of state-of-the-art AI systems.
  • Enjoy the flexibility of a fully remote engagement while working on impactful AI research initiatives.
Equal Opportunity
We are committed to fostering an inclusive and diverse environment where all qualified applicants receive equal consideration. Reasonable accommodations are available throughout the application and engagement process.
Contract & Engagement Details
  • Independent contractor engagement.
  • Fully remote with flexible working hours.
  • Expected commitment of approximately 35 hours per week.
  • Project duration may be extended, shortened, or concluded based on project requirements and individual performance.
  • Work does not require access to confidential or proprietary information from any current or former employer.
  • Payments are issued weekly based on approved work completed.
  • At this time, we are unable to support H1-B or STEM OPT candidates.

Apply