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Machine Learning Operations Jobs in California (NOW HIRING)

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 · On-site

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 ...

Apple

Machine Learning (MLOps) Engineer

Cupertino, CA

$216K - $324K/yr

You will bridge the gap between data science and engineering, driving operational excellence across ... machine learning models in a production environment. Familiarity with model monitoring, drift ...

Adobe

Machine Learning Engineer

San Jose, CA · On-site

Our team is dedicated to solving complex business challenges through innovative machine learning solutions, empowering companies to understand their operations better and make data-driven decisions ...

Adobe, Inc.

Machine Learning Engineer

San Jose, CA · On-site

Our team is dedicated to solving complex business challenges through innovative machine learning solutions, empowering companies to understand their operations better and make data-driven decisions ...

Motorola

Machine Learning Engineer

Los Angeles, CA

... and in the toughest operational environments. With deep roots in DARPA research, Silvus ... THE OPPORTUNITY Silvus is seeking a Machine Learning Engineer who will report to the R&D Director ...

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 · 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 ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact. Wehave an opening for a Machine Learning (ML) Bioengineer to conduct ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact. We have an opening for a Machine Learning (ML) Bioengineer to conduct ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

$9.7K/wk

With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact. We have an opening for a Machine Learning (ML) Bioengineer to conduct ...

EarnIn

Head of Machine Learning

Mountain View, CA · Hybrid

$477K - $583K/yr

The Head of ML will drive operational excellence across our machine learning systems by defining and executing a vision that moves models from research to production with strong performance ...

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All JobsMachine Learning Operations JobsMachine Learning Operations Jobs in California

Machine Learning Operations information

Is ML a high paying job?

Machine Learning Operations (MLOps) roles are generally well-paid due to the specialized skills required, such as expertise in cloud platforms, programming, and data management. Salaries tend to be higher than average tech roles and can increase with experience, certifications, and knowledge of tools like TensorFlow or Kubernetes.

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.

What engineer makes $500,000 a year?

Senior machine learning operations engineers with extensive experience, advanced skills in automation, cloud platforms, and deployment pipelines can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or large tech companies. Such roles often require expertise in tools like Kubernetes, Docker, and cloud services, along with strong problem-solving and leadership abilities.

What is a $900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers, AI research directors, or chief AI officers, often requiring advanced skills in machine learning, deep learning, and data science. These positions usually involve leadership responsibilities, extensive experience, and may include stock options or bonuses that contribute to the total compensation. Such roles are rare and highly competitive, often found in large tech companies or innovative startups.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and maintain AI systems, and while AI automation tools can handle certain tasks, MLEs are essential for creating, tuning, and overseeing complex models. AI may automate some routine aspects, but MLEs' expertise in data engineering, model optimization, and deployment remains critical for effective AI solutions.
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 July 2026, with employment types broken down into 1% As Needed, 75% Full Time, 21% Part Time, 2% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution.
Machine Learning Engineer

Machine Learning Engineer

MM International

Fremont, CA • On-site

Contractor

Re-posted 5 days ago

Job description

Role: Machine Learning Engineer

Location: Fremont, CA 

 

once the documents are verified, a Codility assessment will be shared with the candidate, where they need to score a minimum of 70% and post that, a general video screening with PV. Then we send the submission to the client

About the Role:

Our direct client is hiring a Machine Learning Engineer for their software machine learning and computer vision team to design, develop, and implement critical machine learning models supporting factory and warehouse operations. You will transform ambiguous problem statements into robust end-to-end solutions using a variety of machine learning techniques and tools, including supervised learning, convolutional neural networks, and modern frameworks such as PyTorch and Pandas.

You will collaborate closely with partners in production, process, controls, and quality to deliver solutions for the most challenging problems in our operations. Your work will involve evaluating and deploying models in production environments, ensuring rapid and reliable alerting systems, and addressing operational issues as they arise. You must be adept at handling diverse, heterogeneous datasets that span multiple modalities, including images, multi-spectral sensor outputs, voice, text, and tabular data.

Responsibilities

  • Design, develop, and deploy machine learning models for factory and warehouse environments.
  • 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 preprocessing to model deployment and monitoring.
  • Evaluate and compare models using statistical methods to ensure optimal performance and feasibility.
  • Ensure robust alerting and monitoring systems are in place for deployed models to address issues rapidly.
  • Work with diverse datasets, integrating multiple data types such as images, sensor data, voice, text, and tabular information.
  • Write clean, modular, and sustainable code to translate research ideas into production-ready solutions.

Minimum Requirements

  • In-depth knowledge of Python for high-performance, data-intensive applications.
  • Proficiency with at least one modern deep learning framework (e.g., PyTorch, Jax, TensorFlow).
  • Expertise in one or more of the following areas: computer vision, large language models, recommender systems, or operations research.
  • Foundational knowledge of statistics for model comparison and performance assessment.
  • Real-world experience deploying and maintaining machine learning solutions in production environments.
  • Passion for clean, sustainable, and modular code to bring research concepts to practical implementation.

Preferred Qualifications

  • CI/CD, Kubernetes, MLflow, TensorFlow, PyTorch, AWS.
  • Experience working in manufacturing, industrial automation, or warehouse environments.
  • Familiarity with multi-modal data integration and analysis.
  • Strong problem-solving skills and the ability to thrive in ambiguous, fast-paced settings.
  • Excellent communication skills for cross-functional teamwork.