ZipRecruiter

Log In

1

Machine Learning Operations Jobs in California (NOW HIRING)

Winaxis

Machine Learning Engineer

Fremont, CA · On-site

Collaborate with cross-functional teams to identify, define, and solve high-impact operational challenges. Build and maintain end-to-end machine learning pipelines, from data collection and ...

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

Company Description PatternAI is an automated machine learning platform that reveals critical ... Experience with Linux, Docker and AWS, and basic development operations. * Advanced degree in ...

Pattern AI

Machine Learning Engineer

San Mateo, CA

Company Description PatternAI is an automated machine learning platform that reveals critical ... Experience with Linux, Docker and AWS, and basic development operations. * Advanced degree in ...

Hive

Machine Learning Manager

San Francisco, CA · On-site

$180K - $250K/yr

Machine Learning Manager In order to execute our vision, we're constantly growing our machine ... Interface closely with product management, engineering, devops, labeling, and sales teams to build ...

Hive

Machine Learning Manager

San Francisco, CA

$180K - $250K/yr

Machine Learning Manager In order to execute our vision, we're constantly growing our machine ... Interface closely with product management, engineering, devops, labeling, and sales teams to build ...

previous page

Showing results 41-60

All JobsMachine Learning Operations JobsMachine Learning Operations Jobs in California

Machine Learning Operations information

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 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 above average compared to other tech roles.
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 74% Full Time, 21% Part Time, 3% Temporary, and 2% Contract. Highlights an 94% Physical, 2% Hybrid, and 4% Remote job distribution.

Senior Machine Learning Engineer - Worldwide Product Marketing

Apple

Cupertino, CA • On-site

$128K - $177K/yr

Full-time

Posted 15 days ago

Apple rating

8.0

Company rating: 8.0 out of 10

Based on 677 frontline employees who took The Breakroom Quiz

7th of 30 rated technology retailers

Job description

As a Machine Learning Engineer, you will design and build cutting-edge AI/ML systems that drive meaningful business outcomes at scale. You will work cross-functionally to bring innovative machine learning solutions from research and experimentation through to robust, production-grade deployment.
Description
The MLE will collaborate with other MLEs to build scalable, production-ready ML solutions, taking algorithms from initial concept through to deployment. This hire will design end-to-end AI/ML solutions with clear business impact, from concept to deployment, with a strong focus on feasibility, scalability, and performance. You will benchmark, adapt, and integrate AI/ML models into existing systems.
Minimum Qualifications
8 years of related experience building high-throughput, scalable applications or machine learning models in a production environment.
Bachelor's Degree in Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or related field.
Proficiency in one or more object-oriented programming languages such as Python, Java, or C++, with hands-on experience building distributed systems.
Experience building large-scale machine learning systems using big data technologies such as Spark, SQL, Snowflake, or similar platforms.
Experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
Familiarity with MLOps practices including model versioning, CI/CD pipelines, and experiment tracking tools such as MLflow or similar.
Experience building and deploying applications using large language models (e.g., GPT-4, Claude, Gemini, or open-source alternatives) via APIs or self-hosted inference.
Hands-on experience with agentic frameworks such as LangChain, LlamaIndex, or AutoGen to build multi-step, tool-augmented AI workflows.
Preferred Qualifications
10 years of related experience building high-throughput, scalable applications or machine learning models in a production environment.
Solid understanding of ML fundamentals including supervised/unsupervised learning, model evaluation, and feature engineering.
Strong problem-solving skills with the ability to translate ambiguous business problems into well-defined ML solutions.
Excellent cross-functional communication skills with the ability to collaborate effectively across engineering and data science teams.
Familiarity with LLM evaluation practices including output quality assessment, hallucination detection, and latency benchmarking in production environments.

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Apple logo

About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

View All Apple Jobs

Apply