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Mlops Jobs in Riverside, CA (NOW HIRING)

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 ...

Kia America

Data Scientist- Applied AI

Irvine, CA · On-site

Partner with IT system developers on model deployment and MLOps best practices to ensure production readiness 4th Priority - 20% Clear documentation, source code management, and reproducible analysis

Kia America, Inc.

Data Scientist- Applied AI

Irvine, CA · On-site +1

Partner with IT system developers on model deployment and MLOps best practices to ensure production readiness 4th Priority - 20% Clear documentation, source code management, and reproducible analysis

Kia America

Data Scientist- Applied AI

Irvine, CA · On-site +1

Partner with IT system developers on model deployment and MLOps best practices to ensure production readiness 4th Priority - 20% Clear documentation, source code management, and reproducible analysis

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Mlops information

See Riverside, CA salary details

$102.4K

$160.7K

$191.2K

How much do mlops jobs pay per year?

As of Jun 7, 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 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.

What are the key skills and qualifications needed to thrive as an MLOps Engineer, and why are they important?

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 are 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 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 May 2026, with employment types broken down into 89% Full Time, 7% Part Time, 1% Temporary, and 3% Contract. Highlights an 78% Physical, 5% Hybrid, and 17% Remote job distribution, with an average salary of $160,741 per year, or $77.3 per hour.

Senior Software Engineer, MLOps

FieldAI

Irvine, CA • On-site

$131K - $173K/yr

Full-time

Posted 24 days ago

Job description

Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.
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 of machine learning systems used in robotics applications. You will work closely with machine learning engineers, robotics engineers, and infrastructure teams to ensure reliable training, evaluation, deployment, and monitoring of ML models. This is an exciting opportunity to help operationalize machine learning in real-world robotic systems within a fast-growing and dynamic environment.
What You Will Get To Do
  • Design, build, and maintain GPU based infrastructure for machine learning pipelines, including data processing, training, evaluation, inference and deployment workflows.
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  • Collaborate closely with robotics teams to implement model serving infrastructure for edge/robot deployment.
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  • Build tools and automation to support reproducible experiments, model versioning, and dataset management.
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  • Deploy and manage ML services and inference pipelines using containerized environments for efficient scaling and scheduling of heterogeneous compute resources.
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  • Monitor model performance and system reliability across development and production environments.
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  • Improve the efficiency, scalability, and reliability of ML workflows and infrastructure.
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  • Work with cross-functional engineering teams to integrate ML components into robotics software systems.
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What You Have
  • Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent work experience).
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  • 3-7 years of experience in MLOps, machine learning infrastructure, or related engineering roles.
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  • Strong programming skills in Python or similar languages.
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  • Experience building and maintaining machine learning pipelines.
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  • Hands-on experience with cloud and cloud-native tools such as AWS (SageMaker, S3, or similar cloud ML services), Kubernetes etc.,
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  • Solid understanding of Linux systems and distributed computing environments.
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  • Experience with GPU workload scheduling and orchestration across multi-region cloud environments.
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  • Excellent problem-solving skills and the ability to work collaboratively in a team environment.
    • >

What Will Set You Apart
  • Experience deploying and operating ML systems for robotics or real-world physical systems.
    • >
  • Experience with scaling AI, ML, and inference workloads on Kubernetes.
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  • Exposure to ROS-based robotics data formats and pipelines (rosbags, point clouds)
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  • Experience with experiment tracking, model versioning, or dataset versioning tools.
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  • Experience optimizing ML pipelines for large-scale training and data processing.
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  • Experience working closely with research or applied machine learning teams.
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Compensation and Benefits
Our salary range is competitive with the market, but we take into consideration an individual's background and experience in determining final salary; base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. Also, while we enjoy being together on-site, we are open to exploring a hybrid or remote option.
Why Join Field AI?
We are solving one of the world's most complex challenges: deploying robots in unstructured, previously unknown environments. Our Field Foundational Models™ set a new standard in perception, planning, localization, and manipulation, ensuring our approach is explainable and safe for deployment.
You will have the opportunity to work with a world-class team that thrives on creativity, resilience, and bold thinking. With a decade-long track record of deploying solutions in the field, winning DARPA challenge segments, and bringing expertise from organizations like DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise Self-Driving, Zoox, Toyota Research Institute, and SpaceX, we are set to achieve our ambitious goals.
Be Part of the Next Robotics Revolution
To tackle such ambitious challenges, we need a team as unique as our vision - innovators who go beyond conventional methods and are eager to tackle tough, uncharted questions. We're seeking individuals who challenge the status quo, dive into uncharted territory, and bring interdisciplinary expertise. Our team requires not only top AI talent but also exceptional software developers, engineers, product designers, field deployment experts, and communicators.
We are headquartered in always-sunny Irvine, Southern California and have US based and global teammates.
Join us, shape the future, and be part of a fun, close-knit team on an exciting journey!
We celebrate diversity and are committed to creating an inclusive environment for all employees. Candidates and employees are always evaluated based on merit, qualifications, and performance. We will never discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability, or any other legally protected status.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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