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Google Cloud Machine Learning Engineer Jobs in Walnut, CA

Layup Parts

Machine Learning Engineer

Huntington Beach, CA · On-site

$120K - $185K/yr

We're looking for a Machine Learning Engineer to train custom models using our internal data. This work spans design generation, cost estimation, evaluating the complexity and difficulty of a given ...

Turion Space

Machine Learning Engineer

Irvine, CA

As a Machine Learning Integration Engineer, you will help rapidly prototype, mature, and monitor ML/CV solution that are integral to Turion's Space Domain Awareness data products. You will work on ...

Bespoke Labs

Machine Learning Engineer

Pomona, CA

Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues * Build evaluation harnesses and benchmark infrastructure, with held ...

Bespoke Labs

Machine Learning Engineer

Pomona, CA · On-site

Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues * Build evaluation harnesses and benchmark infrastructure, with held ...

Bespoke Labs

Machine Learning Engineer

Los Angeles, CA · On-site

Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues * Build evaluation harnesses and benchmark infrastructure, with held ...

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All JobsGoogle Cloud Machine Learning Engineer JobsGoogle Cloud Machine Learning Engineer Jobs in Walnut, CA

Google Cloud Machine Learning Engineer information

See Walnut, CA salary details

$24

$64

$88

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

As of Sep 2, 2026, the average hourly pay for google cloud machine learning engineer in Walnut, CA is $64.08, according to ZipRecruiter salary data. Most workers in this role earn between $54.62 and $72.98 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 job categories do people searching Google Cloud Machine Learning Engineer jobs in Walnut, CA look for?

The top searched job categories for Google Cloud Machine Learning Engineer jobs in Walnut, CA are:

What cities near Walnut, CA are hiring for Google Cloud Machine Learning Engineer jobs?

Cities near Walnut, CA with the most Google Cloud Machine Learning Engineer job openings:

Google Cloud Machine Learning Engineer jobs near you

Machine Learning Engineer, Level 5

Snap Inc.

Los Angeles, CA • On-site

Full-time

Re-posted 28 days ago

Job description

Job Summary:
Snap Inc is a technology company that operates Snapchat and other digital services. They are looking for a Machine Learning Engineer to build and deploy machine learning models, solve large-scale problems, and partner with teams to launch ML-driven features.
Responsibilities:
• Build and deploy machine learning models that power core products, serving millions of Snapchatters
• Apply modern ML techniques to solve large-scale, real-world problems
• Own the full ML lifecycle from data analysis to production deployment
• Partner with cross-functional teams to prototype and launch ML-driven features
• Utilize AI tools to design and ship scalable services while upholding rigorous standards for code correctness, security, and production
Qualifications:
Required:
• Strong understanding of machine learning approaches and algorithms
• Able to prioritize duties and work well on your own
• Ability to work with both internal and external partners
• Skilled at solving open ambiguous problems
• Strong collaboration and mentorship skills
• Proficiency in, or a strong aptitude for, leveraging AI tools to streamline development, paired with the critical judgment to audit generated output for architectural integrity, performance bottlenecks, and security risks
• Adaptability in learning and applying evolving AI systems and tools to remain at the forefront of engineering trends and modern development practices
• Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience
• 5+ years of post-Bachelor’s machine learning experience; or Master’s degree in a technical field + 4+ year of post-grad machine learning experience; or PhD in a relevant technical field + 1 years of post-grad machine learning experience
• Experience developing machine learning models for ranking, recommendations, search, content understanding, image generation, or other relevant applications of machine learning
Preferred:
• Advanced degree in computer science or related field
• Experience working with machine learning frameworks such as TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn, or related frameworks
• Experience working with machine learning, ranking infrastructures, and system design
Company:
Snap is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Founded in 2011, the company is headquartered in Venice, USA, with a team of 5001-10000 employees. The company is currently Late Stage.

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