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Google Cloud Machine Learning Engineer Jobs in Utah

NICE

Senior Machine Learning Engineer

Sandy, UT · Hybrid

$99K - $136K/yr

As Senior Machine Learning Engineer, you will own the evaluation and optimization of speech ... Familiarity with multi-cloud deployment across AWS, Azure, and Google Cloud. * Experience with ...

NICE

Senior Machine Learning Engineer

Sandy, UT · On-site

$113K - $150K/yr

As Senior Machine Learning Engineer, you will own the evaluation and optimization of speech ... Familiarity with multi-cloud deployment across AWS, Azure, and Google Cloud. * Experience with ...

Patientco

Senior ML Engineer

Lehi, UT

$98K - $134K/yr

ABOUT THIS POSITION Summary We are seeking a highly skilled and innovative Machine Learning ... Deploy, manage, and monitor LMs and agentic components on Google Cloud Platform (GCP) using ...

Waystar

Senior ML Engineer

Lehi, UT · On-site

$98K - $134K/yr

ABOUT THIS POSITION Summary We are seeking a highly skilled and innovative Machine Learning ... Deploy, manage, and monitor LMs and agentic components on Google Cloud Platform (GCP) using ...

Crew Career Center

Senior ML Engineer

Lehi, UT · On-site

$98K - $134K/yr

ABOUT THIS POSITION Summary We are seeking a highly skilled and innovative Machine Learning ... Deploy, manage, and monitor LMs and agentic components on Google Cloud Platform (GCP) using ...

Entrata

Senior Machine Learning Engineer

Lehi, UT · On-site +1

$144K - $233K/yr

We are seeking a Senior Machine Learning Engineer to help build and scale Entrata's applied AI capabilities. This role will focus on adapting and fine-tuning foundation models for property management ...

Entrata

Senior Machine Learning Engineer

Lehi, UT · On-site +1

$144K - $233K/yr

We are seeking a Senior Machine Learning Engineer to help build and scale Entrata's applied AI capabilities. This role will focus on adapting and fine-tuning foundation models for property management ...

Entrata

Senior Machine Learning Engineer

Lehi, UT · On-site

$144K - $233K/yr

We're looking for a Senior AI and Machine Learning Engineer to help design, build, and optimize our ... Experience with Terraform Cloud or other infrastructure as code tools * Experience integrating AI ...

Berkadia

Senior Engineer - Machine Learning

Midvale, UT · Hybrid

$98K - $135K/yr

As a Senior Engineer, Machine Learning at Berkadia, you'll be at the forefront of applying cutting ... Write maintainable, high-quality code using Python and modern cloud infrastructure; use notebooks ...

Berkadia

Senior Engineer - Machine Learning

Midvale, UT · On-site

$98K - $135K/yr

As a Senior Engineer, Machine Learning at Berkadia, you'll be at the forefront of applying cutting ... Write maintainable, high-quality code using Python and modern cloud infrastructure; use notebooks ...

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

As of Aug 25, 2026, the average hourly pay for google cloud machine learning engineer in Utah is $57.25, according to ZipRecruiter salary data. Most workers in this role earn between $48.80 and $65.19 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.

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Google Cloud Machine Learning Engineer jobs near you

Machine Learning Engineer

Leash Bio

Salt Lake City, UT • On-site

Full-time

Re-posted 19 days ago

Job description

Job Summary:
Leash Biosciences is at the forefront of integrating machine learning with drug discovery, aiming to revolutionize medicinal chemistry. They are seeking a highly skilled Machine Learning Engineer to manage large datasets, optimize cloud-based computing resources, and train advanced machine-learning models to contribute to new therapies for devastating diseases.
Responsibilities:
• Manage and optimize data processing workflows for large-scale datasets, with an approach akin to language data handling.
• Scale and maintain machine learning model training processes, with a focus on cloud environments (primarily Google Cloud, with flexibility to other platforms).
• Collaborate closely with ML researchers, data scientists, and lab automation teams to ensure seamless integration of lab data and ML model training.
• Innovate and iterate on our existing technology stack, taking the initiative to solve problems and improve our ML operations.
• Act as a self-sufficient project manager, overseeing your projects from conception to completion.
Qualifications:
Required:
• Strong experience in machine learning engineering, including data handling, model training, and scaling in cloud environments.
• Comfortable building ML infrastructure
• Experience working with large amounts of text data, NLP, or training LLMs
• Demonstrated capability to make informed decisions, take ownership of solutions, and drive projects forward in a startup environment.
• Excellent collaboration skills, with the ability to work effectively with cross-functional teams.
Preferred:
• Familiarity with common MLops tooling (e.g., Dagster, Prefect, Airflow, Docker, MLflow, Kubeflow, W&B, Ray, etc.)
• Ability to manage own compute cluster
• Ability to maximize GPU utilization and keep cluster busy 24/7
• Ability to analyze model results and kick off new experiments in response
• Experience with BERT or similar language models in PyTorch.
• Experience or interest in biology, chemistry, or related fields is a plus.
Company:
Leash Bio uses AI and machine learning to innovate drug design and medicinal chemistry. Founded in 2021, the company is headquartered in Salt Lake City, USA, with a team of 2-10 employees. The company is currently Early Stage.

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