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Mlops Jobs in Riverside, CA (NOW HIRING)

FieldAI

Agentic AI/ML Engineer, Multimodal

Irvine, CA · On-site

... of MLOps best practices (CI/CD, experiment tracking). • Hands-on experience fine-tuning open-source multimodal models using HuggingFace, DeepSpeed, vLLM, FSDP, LoRA/QLoRA. • Knowledge of ...

FieldAI

2.53 3D Machine Learning Engineer

Irvine, CA · On-site

... with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow. • Strong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning. • Exposure to graph neural networks ...

Veritone

AI/Machine Learning Engineer

Irvine, CA · On-site

MLOps & Infrastructure: Docker, Kubernetes, MLflow / Weights & Biases, Cloud ML Platforms (AWS SageMaker, GCP Vertex AI, etc.) * Backend & API: Python, Go, Node.js, GraphQL, ElasticSearch, and ...

Veritone

AI/Machine Learning Engineer

Irvine, CA · On-site

MLOps & Infrastructure: Docker, Kubernetes, MLflow / Weights & Biases, Cloud ML Platforms (AWS SageMaker, GCP Vertex AI, etc.) * Backend & API: Python, Go, Node.js, GraphQL, ElasticSearch, and ...

Turion Space

Machine Learning Engineer

Irvine, CA

Contribute to the deployment of MLOps processes and techniques * Assist in the development of automated methods for responsible AI monitoring and best practices Qualifications Minimum Qualifications:

CYNET SYSTEMS

Senior AI Engineer

Irvine, CA · On-site

$56 - $61/hr

Kubernetes and advanced MLOps practices. * Familiarity with Model Context Protocol (MCP) patterns and agent-based architectures. Skills: * Strong problem-solving and analytical thinking. * Ability to ...

Munich Re

Sr. Solution Architect

Irvine, CA · On-site

Define and govern enterprise AI architecture standards , including model lifecycle management, MLOps, and AI platform integration. * Ensure responsible and compliant AI adoption, aligned with AI ...

Munich Re

Sr. Solution Architect

Corona, CA · On-site

Define and govern enterprise AI architecture standards , including model lifecycle management, MLOps, and AI platform integration. * Ensure responsible and compliant AI adoption, aligned with AI ...

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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 2, 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.

Agentic AI/ML Engineer, Multimodal

FieldAI

Irvine, CA • On-site

Full-time

Re-posted 14 days ago

Job description

Job Summary:
FieldAI is transforming how robots interact with the real world by building risk-aware, reliable, and field-ready AI systems. As an AI/ML Engineer on the FiFM team, you will drive research and model development focused on multimodal data, computer vision, and agentic AI, contributing to the company's innovative initiatives in robotics.
Responsibilities:
• Train and fine-tune million- to billion-parameter multimodal models, with a focus on computer vision, video understanding, and vision-language integration.
• Track state-of-the-art research, adapt novel algorithms, and integrate them into FiFM.
• Curate datasets and develop tools to improve model interpretability.
• Build scalable evaluation pipelines for vision and multimodal models.
• Contribute to model observability, drift detection, and error classification.
• Fine-tune and optimize open-source VLMs and multimodal embedding models for efficiency and robustness.
• Build and optimize Multi-VectorRAG pipelines with vector DBs and knowledge graphs.
• Create embedding-based memory and retrieval chains with token-efficient chunking strategies.
Qualifications:
Required:
• Master’s/Ph.D. in Computer Science, AI/ML, Robotics, or equivalent industry experience.
• 2+ years of industry experience or relevant publications in CV/ML/AI.
• Strong expertise in computer vision, video understanding, temporal modeling, and VLMs.
• Proficiency in Python and PyTorch with production-level coding skills.
• Experience building pipelines for large-scale video/image datasets.
• Familiarity with AWS or other cloud platforms for ML training and deployment.
• Understanding of MLOps best practices (CI/CD, experiment tracking).
• Hands-on experience fine-tuning open-source multimodal models using HuggingFace, DeepSpeed, vLLM, FSDP, LoRA/QLoRA.
• Knowledge of precision tradeoffs (FP16, bfloat16, quantization) and multi-GPU optimization.
• Ability to design scalable evaluation pipelines for vision/VLMs and agent performance.
Preferred:
• Experience with Agentic/RAG pipelines and knowledge graphs (LangChain, LangGraph, LlamaIndex, OpenSearch, FAISS, Pinecone).
• Familiarity with agent operations logging and evaluation frameworks.
• Background in optimization: token cost reduction, chunking strategies, reranking, and retrieval latency tuning.
• Experience deploying models under quantized (int4/int8) and distributed multi-GPU inference.
• Exposure to open-vocabulary detection, zero/few-shot learning, multimodal RAG.
• Knowledge of temporal-spatial modeling (event/scene graphs).
• Experience deploying AI in edge or resource-constrained environments.
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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