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Google Cloud Machine Learning Engineer Jobs in Colorado

Bespoke Labs

Machine Learning Engineer

Greeley, CO · 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

Denver, CO · On-site

$94K - $266K/yr

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 ...

New

Bet365

Senior Data Scientist

Denver, CO

Collaborating with Machine Learning Engineers to champion the adoption of robust MLOps practices on our Google Cloud Platform (GCP) stack, ensuring models are automated, monitored, and scalable.

Bet365

Data Science Team Lead/Manager

Denver, CO

... Machine Learning Engineers to champion the adoption of robust MLOps practices on our Google Cloud Platform (GCP) stack, ensuring models are automated, monitored, and scalable. • Establishing data ...

Apex Systems

Cloud Engineer

Denver, CO · On-site

$57.50 - $76.75/hr

Deploy and manage Google Cloud Platform infrastructure natively as code (IaC) utilizing Terraform ... UnitedHealthcare creates and publishes the Transparency in Coverage Machine-Readable Files on ...

Accenture

Google Data Specialist

Denver, CO · On-site

$70K - $196K/yr

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

bet365

Data Science Team Leader

Denver, CO · On-site

$155K - $165K/yr

Collaborating with Machine Learning Engineers to champion the adoption of robust MLOps practices on our Google Cloud Platform (GCP) stack, ensuring models are automated, monitored, and scalable.

bet365

Data Science Team Leader

Denver, CO · On-site

Collaborating with Machine Learning Engineers to champion the adoption of robust MLOps practices on our Google Cloud Platform (GCP) stack, ensuring models are automated, monitored, and scalable.

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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 15, 2026, the average hourly pay for google cloud machine learning engineer in Colorado is $66.13, according to ZipRecruiter salary data. Most workers in this role earn between $56.35 and $75.34 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 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 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 popular job titles related to Google Cloud Machine Learning Engineer jobs in Colorado?

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

Google Cloud Machine Learning Engineer jobs near you

Machine Learning Engineer

Bespoke Labs

Fort Collins, CO • On-site

Full-time

Re-posted 28 days ago

Job description

About Us

We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.

We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.

What You'll Be Working On

You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.

Must-Have Skills

3+ years of ML engineering experience — model training, fine-tuning, or post-training pipelines in research or production

Strong Python and deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed precision)

Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior

Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives

Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)

Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination

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