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Google Cloud Machine Learning Engineer Jobs in Raleigh, NC

CoVar

Machine Learning Engineer

Durham, NC · On-site

The Machine Learning Engineer will develop software and machine learning algorithms to address real-world customer issues and will have opportunities to present their work to high-level customers.

CoVar

Machine Learning Engineer

Durham, NC

Machine Learning Engineer About CoVar CoVar is a small AI/ML R&D software company in Durham, NC, that uses artificial intelligence to solve problems that matter. We develop AI/ML tools to help the ...

Vadum Inc

Machine Learning Engineer

Raleigh, NC

We are seeking a talented and innovative Machine Learning Engineer to join our dynamic team. In this role, you will be responsible for designing and developing machine learning prototypes, as well as ...

Vadum Inc

Machine Learning Engineer

Raleigh, NC · On-site

We are seeking a talented and innovative Machine Learning Engineer to join our dynamic team. In this role, you will be responsible for designing and developing machine learning prototypes, as well as ...

Vadum, Inc.

Machine Learning Engineer

Raleigh, NC · On-site

$96K - $137K/yr

We are seeking a talented and innovative Machine Learning Engineer to join our dynamic team. In this role, you will be responsible for designing and developing machine learning prototypes, as well as ...

CoVar

Machine Learning Engineer

Durham, NC · On-site

... machine learning, Bayesian models, etc. • B.S., preferably M.S. or Ph.D in engineering, math, computer science, or related field • Excellent technical communication skills • Ability to work in ...

Bespoke Labs

Machine Learning Engineer

Raleigh, NC · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post ... scale on cloud or HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology ...

Bespoke Labs

Machine Learning Engineer

Durham, NC · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post ... scale on cloud or HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology ...

Accenture

Google Senior Data Engineer

Raleigh, NC · On-site

$94K - $266K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

You Are A hands-on Engineer with foundational experience in Data Engineering, Analytics, or Machine Learning-now building deep expertise in Google Cloud Platform (GCP). You are eager to apply ...

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All JobsGoogle Cloud Machine Learning Engineer JobsGoogle Cloud Machine Learning Engineer Jobs in Raleigh, NC

Google Cloud Machine Learning Engineer information

See Raleigh, NC salary details

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

As of Aug 17, 2026, the average hourly pay for google cloud machine learning engineer in Raleigh, NC is $61.13, according to ZipRecruiter salary data. Most workers in this role earn between $52.12 and $69.62 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 Raleigh, NC?

The most popular types of Google Cloud Machine Learning Engineer jobs in Raleigh, NC are:

What are popular job titles related to Google Cloud Machine Learning Engineer jobs in Raleigh, NC?

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

What job categories do people searching Google Cloud Machine Learning Engineer jobs in Raleigh, NC look for?

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

What cities near Raleigh, NC are hiring for Google Cloud Machine Learning Engineer jobs?

Cities near Raleigh, NC with the most Google Cloud Machine Learning Engineer job openings:

Google Cloud Machine Learning Engineer jobs near you
Infographic showing various Google Cloud Machine Learning Engineer job openings in Raleigh, NC as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $127,143 per year, or $61.1 per hour.

Lead Machine Learning Engineer

Motion Recruitment Partners, LLC

Raleigh, NC • On-site

$99K - $131K/yr

Other

Posted 5 days ago

Job description

Our Client serves customers in over 150 countries with trusted legal, regulatory, and business information. They are transforming the legal industry through cutting-edge AI, scalable data platforms, and intelligent research systems that power high-stakes decision-making for legal professionals worldwide. Their Global AI Platform Team builds the foundational infrastructure behind next-generation AI products, including LLM-powered research assistants, retrieval systems, and enterprise-grade agentic workflows.
We are seeking a Sr. Machine Learning Engineer to define and lead the architecture of scalable AI/ML and agentic systems across our global product portfolio. This is a senior technical leadership role for someone who thrives at the intersection of:
  • Large-scale distributed ML systems
  • LLM and RAG architectures
  • Agentic AI frameworks and tool orchestration
  • Enterprise platform engineering

You will shape the long-term AI platform strategy and establish technical standards that impact millions of users
What You'll
  • Do Architect Scalable AI Platforms
  • Define reference architecture for LLM, ML, and agent-based systems across products
  • Design high-availability, low-latency inference platforms for global scale
  • le.Establish reusable platform components for model lifecycle, deployment, and monitori
  • Lead Agentic AI & Tool Ecosystems
  • Architect multi-step, reasoning-driven agent systems
  • Design orchestration patterns for tool use, API invocation, and structured function calling
  • Lead implementation and governance of Model Context Protocol (MCP) servers to standardize tool integration and context management
  • Define guardrails, permissions, and audit mechanisms for enterprise-safe AI systems
  • Elevate Engineering Standards. Set best practices for MLOps, CI/CD, observability, and system reliability
  • Embed Responsible AI principles across platform architecture
  • Mentor senior engineers and influence technical direction across teams

What We're Looking
For Experience/Education requirement
  • 10+ yrs of experience with Master's degree or 12+ yrs of experience with bachelor degree
  • 10+ years building production-grade ML systems at scale
  • Extensive experience with LLMs, generative AI, and RAG systems in real-world deployments
  • Proven expertise designing distributed systems in cloud environments (AWS, Azure, or Google Cloud Platform)
  • Hands-on experience with Kubernetes, containerization, and scalable inference systems
  • Experience designing agentic systems and tool orchestration frameworks
  • Experience implementing or governing MCP servers or structured tool-calling architectures
  • Technical Strength/Strong Python engineering background
  • Experience with vector databases and search systems
  • Deep understanding of model evaluation, reliability, and monitoring
  • Strong architectural judgment and systems thinking
  • Leadership/Demonstrated ability to influence technical direction across teams
  • Strong communication skills and executive presence
  • Experience mentoring senior engineers or leading cross-functional initiatives

Apply