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Mlops Jobs in Riverside, CA (NOW HIRING)

FieldAI

Software Engineer, MLOps

Irvine, CA · On-site

The MLOps Engineer will design and maintain infrastructure for machine learning systems, collaborating closely with engineering teams to ensure effective deployment and monitoring of ML models.

FieldAI

Senior Software Engineer, MLOps

Irvine, CA · On-site

$129K - $171K/yr

They are seeking a skilled Senior MLOps Engineer to design and maintain the infrastructure supporting machine learning systems in robotics applications, collaborating with various engineering teams ...

FieldAI

Senior Software Engineer, MLOps

Irvine, CA · On-site

$131K - $173K/yr

We are seeking a skilled and motivated Senior MLOps Engineer to join our engineering team. In this role, you will design and maintain the infrastructure and tooling that supports the full lifecycle ...

FieldAI

Senior Software Engineer, MLOps

Irvine, CA · On-site +1

$131K - $173K/yr

We are seeking a skilled and motivated Senior MLOps Engineer to join our engineering team. In this role, you will design and maintain the infrastructure and tooling that supports the full lifecycle ...

FieldAI

Senior Software Engineer, MLOps

Irvine, CA · On-site +1

$131K - $173K/yr

We are seeking a skilled and motivated Senior MLOps Engineer to join our engineering team. In this role, you will design and maintain the infrastructure and tooling that supports the full lifecycle ...

FieldAI

Senior Machine Learning Platform Engineer

Irvine, CA · On-site

$110K - $152K/yr

The Senior Machine Learning Platform Engineer will design and manage scalable ML infrastructure, develop cloud-based pipelines, and ensure the reliability of MLOps workflows while mentoring junior ...

Yum Brands

Sr Machine Learning Engineer

Irvine, CA

$112K - $154K/yr

The ideal candidate combines strong software engineering and MLOps fundamentals with a passion for building reliable, scalable machine learning systems. Required Qualifications * Bachelor's or Master ...

Yum! Brands

Sr Machine Learning Engineer

Irvine, CA

$112K - $154K/yr

The ideal candidate combines strong software engineering and MLOps fundamentals with a passion for building reliable, scalable machine learning systems. Required Qualifications * Bachelor's or Master ...

Yum

Sr Machine Learning Engineer

Irvine, CA · On-site

$112K - $154K/yr

The ideal candidate combines strong software engineering and MLOps fundamentals with a passion for building reliable, scalable machine learning systems. Responsibilities * Support the deployment ...

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All JobsMlops JobsMlops Jobs in Riverside, CA

Mlops information

See Riverside, CA salary details

$102.4K

$160.7K

$191.2K

How much do mlops jobs pay per year?

As of Sep 1, 2026, the average yearly pay for mlops in Riverside, CA is $160,741.00, according to ZipRecruiter salary data. Most workers in this role earn between $151,874.00 and $174,579.00 per year, depending on experience, location, and employer.

What is MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.

What are the key skills and qualifications needed to thrive as an MLOps engineer?

To thrive as an MLOps Engineer, you need a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

What are some common challenges faced by MLOps professionals when deploying machine learning models to production?

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

Is MLOps in demand?

MLOps is a rapidly growing field as organizations increasingly adopt machine learning models in production. Professionals with skills in cloud platforms, automation, and tools like Kubernetes and Docker are highly sought after, reflecting strong industry demand for MLOps expertise.

Is MLOps outdated?

MLOps is an evolving field focused on deploying and managing machine learning models efficiently. It remains highly relevant as organizations increasingly adopt AI solutions, with skills in automation, cloud platforms, and monitoring tools in demand. Staying current with new tools and best practices is essential for MLOps professionals.

What is the average salary in MLOps?

The average salary for MLOps engineers typically ranges from $100,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and machine learning deployment tend to earn higher salaries.

What are popular job titles related to Mlops jobs in Riverside, CA?

For Mlops jobs in Riverside, CA, the most frequently searched job titles are:

What job categories do people searching Mlops jobs in Riverside, CA look for?

The top searched job categories for Mlops jobs in Riverside, CA are:

What cities near Riverside, CA are hiring for Mlops jobs?

Cities near Riverside, CA with the most Mlops job openings:

Mlops jobs near you
Infographic showing various Mlops job openings in Riverside, CA as of August 2026, with employment types broken down into 90% Full Time, 7% Part Time, and 3% Contract. Highlights an 79% Physical, 6% Hybrid, and 15% Remote job distribution, with an average salary of $160,741 per year, or $77.3 per hour.

Software Engineer, MLOps

FieldAI

Irvine, CA • On-site

Full-time

Re-posted 5 days ago

Job description

Job Summary:
FieldAI is transforming how robots interact with the real world by building reliable AI systems for complex robotics challenges. The MLOps Engineer will design and maintain infrastructure for machine learning systems, collaborating closely with engineering teams to ensure effective deployment and monitoring of ML models.
Responsibilities:
• Design, build, and maintain GPU based infrastructure for machine learning pipelines, including data processing, training, evaluation, inference and deployment workflows.
• Collaborate closely with robotics teams to implement model serving infrastructure for edge/robot deployment.
• Build tools and automation to support reproducible experiments, model versioning, and dataset management.
• Deploy and manage ML services and inference pipelines using containerized environments for efficient scaling and scheduling of heterogeneous compute resources.
• Monitor model performance and system reliability across development and production environments.
• Improve the efficiency, scalability, and reliability of ML workflows and infrastructure.
• Work with cross-functional engineering teams to integrate ML components into robotics software systems.
Qualifications:
Required:
• Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent work experience).
• 3-7 years of experience in MLOps, machine learning infrastructure, or related engineering roles.
• Strong programming skills in Python or similar languages.
• Experience building and maintaining machine learning pipelines.
• Hands-on experience with cloud and cloud-native tools such as AWS (SageMaker, S3, or similar cloud ML services), Kubernetes etc.
• Solid understanding of Linux systems and distributed computing environments.
• Experience with GPU workload scheduling and orchestration across multi-region cloud environments.
• Excellent problem-solving skills and the ability to work collaboratively in a team environment.
Preferred:
• Experience deploying and operating ML systems for robotics or real-world physical systems.
• Experience with scaling AI, ML, and inference workloads on Kubernetes.
• Exposure to ROS-based robotics data formats and pipelines (rosbags, point clouds)
• Experience with experiment tracking, model versioning, or dataset versioning tools.
• Experience optimizing ML pipelines for large-scale training and data processing.
• Experience working closely with research or applied machine learning teams.
Company:
FieldAI is building general robot intelligence for the physical world. Founded in 2023, the company is headquartered in Mission Viejo, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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