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Machine Learning Operations Jobs in California (NOW HIRING)

Voltai

Machine Learning Operations

Menlo Park, CA · On-site

Job Summary : Voltai is the leading AI company building agentic systems and frontier foundation models for semiconductor and electronics design. The role involves designing, building, and maintaining ...

Intuit

Staff Machine Learning Engineer

Mountain View, CA · On-site

$202K - $274K/yr

Important skills include creating data pipelines, developing and deploying models, and machine learning operations. Responsibilities * Work with AI scientists to create and refine features from the ...

Intuit

Staff Machine Learning Engineer

Mountain View, CA · On-site

$202K - $274K/yr

Important skills include creating data pipelines, developing and deploying models, and machine learning operations. Responsibilities * Work with AI scientists to create and refine features from the ...

Intuit

Staff Machine Learning Engineer

Mountain View, CA · On-site

$202K - $274K/yr

Important skills include creating data pipelines, developing and deploying models, and machine learning operations. Responsibilities * Work with AI scientists to create and refine features from the ...

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Machine Learning Operations information

What is the difference between Machine Learning Operations vs Data Scientist?

AspectMachine Learning OperationsData Scientist
Primary FocusDeploying, maintaining, and scaling ML models in productionAnalyzing data to develop insights and build models
Required SkillsML deployment, cloud platforms, automation, scriptingStatistical analysis, data visualization, programming (Python/R)
Work EnvironmentOperations teams, cloud infrastructure, production systemsResearch environments, data analysis teams, R&D
Common CertificationsCloud certifications, MLOps tools certificationsData science certifications, statistical courses

Machine Learning Operations and Data Scientists often collaborate, but MLOps focuses on deploying and maintaining models in production, while Data Scientists focus on analyzing data and developing models. Both roles require technical skills, but their day-to-day tasks and environments differ.

Is machine learning operations a high paying job?

Machine Learning Operations (MLOps) roles typically offer high salaries due to the specialized skills required, such as expertise in cloud platforms, automation, and data engineering. Compensation varies based on experience, location, and company size, but generally ranks among well-paying tech jobs.

What are machine learning operations?

Machine Learning Operations (MLOps) is a set of practices that combines machine learning, software engineering, and DevOps to deploy, monitor, and maintain machine learning models in production environments. It involves tasks such as model versioning, automation, testing, and ensuring scalability and reliability using tools like CI/CD pipelines and cloud platforms.

What cities in California are hiring for Machine Learning Operations jobs?

Cities in California with the most Machine Learning Operations job openings:

Machine Learning Operations jobs near you
Infographic showing various Machine Learning Operations job openings in California as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 9% Part Time, 1% Temporary, 2% Contract, and 1% Nights. Highlights an 93% Physical, 3% Hybrid, and 4% Remote job distribution.

Machine Learning Operations

Menlo Park, CA • On-site

Voltai
See more jobs at Voltaiexternal

Full-time

Re-posted 25 days ago

Job description

Job Summary:
Voltai is the leading AI company building agentic systems and frontier foundation models for semiconductor and electronics design. The role involves designing, building, and maintaining scalable ML pipelines, operationalizing evaluation workflows, and collaborating with cross-functional teams to integrate foundation models into customer-facing AI products.
Responsibilities:
• Design, build, and maintain scalable ML pipelines for training, evaluation, and deployment of LLMs and retrieval-augmented systems, optimized for performance, traceability, and reproducibility
• Operationalize evaluation workflows using both synthetic and human-labeled datasets to monitor model quality at scale across multiple downstream tasks and customer deployments
• Automate the ML Developer lifecycle by implementing robust data versioning, model tracking, and CI/CD pipelines using modern ML Ops tooling
• Optimize model training and inference, focusing on reducing latency, maximizing throughput, and controlling cost across heterogeneous hardware environments.
• Collaborate cross-functionally with research, infrastructure, and product teams to productionize foundation models and integrate them into customer-facing AI products
• Deploy and manage both open-source and proprietary models within stringent constraints on latency, security, and compliance—balancing reliability with innovation.
• Implement real-time monitoring and alerting systems to detect model/data drift, quality regressions, and infrastructure bottlenecks in live environments.
• Work directly with enterprise customers, supporting deployment strategies, ensuring production readiness, and creating tight feedback loops from real-world usage to continuous model improvement
Qualifications:
Required:
• Proven experience in building reliable and scalable systems, with a strong foundation in software engineering principles and expertise with Python, Go, or Rust
• Hands-on experience with ML Ops platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, or Apache Airflow, facilitating efficient model lifecycle management
• Proficiency in cloud-native tools including Docker, Kubernetes, storage optimizers. Experience with major cloud providers like AWS, GCP or other compute providers for deploying and managing ML workloads with a focus on cost optimization
• Proficiency in tools like Weights & Biases, MLflow, and/or CometML for tracking experiments, managing model metadata, and facilitating collaboration
• Familiarity with infrastructure tools like Terraform, Pulumi, and Chronosphere for monitoring and alerting.
• Demonstrated ability to translate research findings into robust, production-ready systems, bridging the gap between experimentation and deployment.
Preferred:
• Some background in hardware/electronics, gained through professional, academic, or personal projects
• Contributions to open-source initiatives
• Notable awards or publications in leading journals/conferences
• Experience thriving in a fast-paced, hyper-growth startup environment
Company:
AI models for electronics Founded in , the company is headquartered in , , with a team of 11-50 employees. The company is currently Early Stage.

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