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Google Cloud Machine Learning Engineer Jobs in California

Capital One

Lead Machine Learning Engineer

San Jose, CA · On-site +1

$120K - $158K/yr

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

Capital One

Lead Machine Learning Engineer

San Francisco, CA · On-site +1

$120K - $159K/yr

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

Capital One

Lead Machine Learning Engineer

San Jose, CA · On-site

$120K - $158K/yr

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

Capital One

Lead Machine Learning Engineer (IC)

San Jose, CA · On-site +1

$120K - $158K/yr

Lead Machine Learning Engineer (IC) 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 ...

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 ...

Tapestry

Machine Learning Engineer

Mountain View, CA · On-site

About Tapestry Tapestry is a group within Google working to build the AI-powered electric grid. We ... Experience with cloud platforms such as AWS, GCP, or Azure. * A strong portfolio of projects ...

Tapestry

Machine Learning Engineer

Mountain View, CA · On-site

About Tapestry Tapestry is a group within Google working to build the AI-powered electric grid. We ... Experience with cloud platforms such as AWS, GCP, or Azure. * A strong portfolio of projects ...

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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 22, 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 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 California?

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

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

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The top searched job categories for Google Cloud Machine Learning Engineer jobs in California are:

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Cities in California with the most Google Cloud Machine Learning Engineer job openings:

Google Cloud Machine Learning Engineer jobs near you

Google Cloud Platform MLOPs Tech Lead - Python, Google Cloud Platform (Google Cloud Platform) *** D

Projas Technologies, LLC

Oakland, CA • On-site

$64 - $85.50/hr

Other

Re-posted 24 days ago

Job description

We are seeking a Senior Data Infrastructure Engineer with deep expertise in Python, Google Cloud Platform (Google Cloud Platform), and MLOps to join our growing data engineering team. You will be responsible for building scalable data pipelines, optimizing cost and performance, and supporting machine learning workflows across our organization.

Key Responsibilities

  • Technical Leadership

    • Proven experience leading MLOps initiatives across the full ML lifecycle
    • Deep understanding of CI/CD pipelines, model deployment, monitoring, and scaling
    • Hands-on expertise with cloud platforms (AWS, Google Cloud Platform, Azure) and containerization (Docker, Kubernetes)

  • Project Ownership

    • Ability to define and drive project plans from inception to delivery
    • Skilled in managing execution timelines, resource allocation, and risk mitigation
    • Comfortable working with cross-functional teams including data scientists, engineers, and product managers

  • Communication & Collaboration

    • Strong communicator who can translate technical concepts to non-technical stakeholders
    • Experience in stakeholder management and status reporting
    • Capable of leading meetings, resolving conflicts, and aligning teams toward shared goals

  • Problem Solving in Ambiguity

    • Thrives in undefined or evolving problem spaces
    • Demonstrates creativity and initiative in shaping solutions from vague requirements
    • Comfortable making decisions with incomplete information and iterating quickly

  • Strategic Thinking

    • Ability to align MLOps practices with business goals
    • Experience in evaluating and implementing tools and frameworks that improve team productivity and model reliability
  • Develop and enhance Python frameworks and libraries for:
    • Cost tracking
    • Data processing
    • Data quality
    • Lineage
    • Governance
    • MLOps
  • Optimize data processing workflows to reduce costs for large-scale training data and feature pipelines.
  • Build scalable batch pipelines using BigQuery, Dataflow, and Composer on Google Cloud Platform (Google Cloud Platform).
  • Implement robust monitoring, logging, and alerting systems to ensure infrastructure reliability.
  • Plan and execute infrastructure rollouts with phased deployments, validation, and rollback strategies.
  • Serve as the primary liaison for Data Scientists, ML Engineers, and other stakeholders during rollout coordination and issue resolution.
  • Collaborate with ML Platform Engineers to ensure seamless integration of updates.
  • Document processes and changes, creating clear runbooks and handoff materials for ongoing support.
Required Skills & Qualifications
  • Strong proficiency in Python and experience with building reusable libraries and frameworks.
  • Hands-on experience with Google Cloud Platform (Google Cloud Platform) services: BigQuery, Dataflow, Composer, Cloud Monitoring, etc.
  • Solid understanding of MLOps, data governance, and data lineage principles.
  • Experience with CI/CD, infrastructure as code, and DevOps practices.
  • Excellent communication and collaboration skills.
  • Proven ability to manage infrastructure rollouts and support cross-functional teams.
Preferred Qualifications
  • Experience with Terraform, Kubernetes, or Airflow.
  • Familiarity with machine learning workflows and feature engineering pipelines.
  • Background in data quality frameworks and cost optimization strategies.

Python, Google Cloud Platform, Google Cloud Platform, BigQuery, Dataflow, Composer, MLOps, Data Engineering, Machine Learning, Data Pipelines, Infrastructure, Monitoring, Logging, Alerting, Cost Optimization, Data Governance, Data Lineage, CI/CD, DevOps, Airflow, Terraform, Kubernetes, ML Platform, Feature Engineering, Batch Pipelines, Cloud Infrastructure, Rollout Strategy, Runbooks

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