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Google Cloud Machine Learning Engineer Jobs in California

Prosum Inc.

Machine Learning Engineer

CA · On-site

$75 - $89/hr

Machine Learning Engineer Pay Rate: $75-$89/hour Position Summary We are seeking a skilled Machine ... Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform * Experience with ...

Capital One

Sr. Lead Machine Learning Engineer

San Jose, CA · On-site +1

$120K - $158K/yr

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be ... Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google ...

RAPID EAGLE INC

Machine Learning Engineer

Sunnyvale, CA · Remote

$70 - $80/hr

The ideal candidate will have strong expertise in machine learning algorithms, data engineering, model deployment, and cloud technologies. Key Responsibilities: * Design, develop, and deploy machine ...

Omni Inclusive

AI/ML Engineer

San Jose, CA

$134K - $161K/yr

Machine Learning Engineer We are seeking a talented and experienced Machine Learning Engineer. In ... You'll work with a modern tech stack centered on Python, Google Cloud Platform, and the latest in ...

Wells Fargo

Lead Machine Learning Engineer

San Francisco, CA · Hybrid

$120K - $159K/yr

The AI/ML Data Architecture, Engineering, and Enablement team is seeking a Lead Machine Learning ... In this role, you will leverage Google Cloud Platform (GCP) services and modern ML frameworks to ...

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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 Jun 15, 2026, the average hourly pay for google cloud machine learning engineer in California is $62.06, according to ZipRecruiter salary data. Most workers in this role earn between $52.88 and $70.67 per hour, depending on experience, location, and employer.

What are Google Cloud Machine Learning Engineers?

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, and why are they important?

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 salary of an aiml engineer in Google?

A Google Cloud Machine Learning Engineer typically earns a salary ranging from $120,000 to $180,000 annually, depending on experience, location, and level within the company. Compensation may also include bonuses, stock options, and benefits, with higher salaries often associated with advanced skills in cloud platforms and machine learning frameworks.

How much do machine learning engineers make at GCP?

Machine learning engineers at Google Cloud Platform (GCP) typically earn between $120,000 and $180,000 annually, depending on experience, location, and level. Salaries can increase with specialized skills in cloud services, data modeling, and certifications like Google Cloud Professional Machine Learning Engineer.

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 engineers make $500,000?

Senior engineers in high-demand fields such as software development, data science, and machine learning can earn $500,000 or more annually, especially with extensive experience, specialized skills, and leadership roles. Roles like senior software engineers, machine learning engineers, and technical architects often reach this compensation level in large tech companies or through equity and bonuses.

Does Google hire machine learning engineers?

Yes, Google hires machine learning engineers to develop and implement AI and machine learning solutions across various products and services. These roles typically require expertise in programming, data analysis, and familiarity with tools like TensorFlow and Google Cloud Platform.

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 the most commonly searched types of Google Cloud Machine Learning Engineer jobs in California? The most popular types of Google Cloud Machine Learning Engineer jobs in California are:
What are popular job titles related to Google Cloud Machine Learning Engineer jobs in California? For Google Cloud Machine Learning Engineer jobs in California, the most frequently searched job titles are:
What job categories do people searching Google Cloud Machine Learning Engineer jobs in California look for? The top searched job categories for Google Cloud Machine Learning Engineer jobs in California are:
What cities in California are hiring for Google Cloud Machine Learning Engineer jobs? Cities in California with the most Google Cloud Machine Learning Engineer job openings:
Google Cloud Machine Learning Engineer jobs near you
Machine Learning Engineer

Machine Learning Engineer

Prosum Inc.

CA • On-site

$75 - $89/hr

Contractor

Posted 11 days ago

Job description

Job Description
Machine Learning Engineer
Pay Rate: $75-$89/hour
Position Summary
We are seeking a skilled Machine Learning Engineer (MLOps) to support the full lifecycle of machine learning models, including design, development, deployment, and maintenance. This role focuses on building scalable, production-ready AI/ML solutions and ensuring seamless integration within existing systems.
The ideal candidate will collaborate with cross-functional teams to deploy, monitor, and optimize machine learning models that drive operational efficiency, innovation, and data-driven decision-making. This position requires strong experience in MLOps, DevOps practices, and cloud-based AI infrastructure.
Key Responsibilities
  • Design, build, deploy, and maintain machine learning models in production environments
  • Develop and manage end-to-end MLOps pipelines, including model versioning, monitoring, and automation
  • Implement scalable ML infrastructure using cloud platforms (AWS, Azure, or GCP)
  • Build and optimize CI/CD pipelines for automated testing and deployment of ML models
  • Collaborate with data scientists, data engineers, and DevOps teams to operationalize AI solutions
  • Monitor model performance, system health, and data drift; implement logging and alerting solutions
  • Ensure reliability, scalability, and performance of ML systems in real-time inference environments
  • Maintain version control for models and code to support reproducibility and collaboration
  • Apply best practices for testing, debugging, and performance optimization
  • Ensure compliance with data security, privacy, and regulatory standards
  • Create and maintain technical documentation for ML systems and processes
Required Qualifications
  • Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, or a related field
  • 3+ years of experience in machine learning engineering or MLOps
  • Hands-on experience managing the end-to-end machine learning lifecycle
  • Strong programming skills in Python, R, and/or SQL
  • Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform
  • Experience with containerization (Docker) and orchestration tools (Kubernetes)
  • Experience with infrastructure as code tools such as Terraform
  • Experience building and maintaining CI/CD pipelines (e.g., GitHub Actions)
  • Strong understanding of software development, system architecture, and deployment processes
  • Experience with monitoring, logging, and performance tuning of ML systems
  • Knowledge of version control systems (e.g., Git)
Preferred Qualifications
  • Master's degree in Computer Science, Engineering, or a related field
  • Experience working with healthcare data or regulated environments
  • Familiarity with Electronic Health Record (EHR) systems
  • Experience with predictive modeling, natural language processing (NLP), and large language models (LLMs)
  • Knowledge of retrieval-augmented generation (RAG) frameworks and their applications
  • Understanding of agile methodologies and DevOps lifecycle practices
Core Competencies
  • Production-grade ML model deployment and lifecycle management
  • Scalable infrastructure design for AI/ML workloads
  • Cross-functional collaboration and technical leadership
  • Strong analytical and problem-solving skills
  • Effective technical communication and documentation

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