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Machine Learning Operations Jobs in California (NOW HIRING)

Apple

Machine Learning Engineer

Cupertino, CA · On-site

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 ...

TEKsystems

Machine Learning Engineer

San Diego, CA · Hybrid

$70 - $95/hr

Production Machine Learning Deployments * Model Monitoring, Observability, and Optimization ... Build Python-based tools and automation supporting ML operations and orchestration * Implement ...

Apple

Machine Learning (MLOps) Engineer

Cupertino, CA · On-site

You will bridge the gap between data science and engineering, driving operational excellence across ... Machine Learning, Operations Research, or related field. Proven track record of shipping and ...

Apple

Machine Learning Engineer

Cupertino, CA

$216K - $324K/yr

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 ...

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

$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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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 Operations Engineer

Machine Learning Operations Engineer

WindBorne Systems

Palo Alto, CA • On-site

$81K - $110K/yr

Full-time

Posted 6 days ago

Job description

Job Summary:
WindBorne Systems is supercharging weather forecasts with a unique proprietary data source: a global constellation of next-generation smart weather balloons. They are seeking a Machine Learning Operations Engineer to streamline operational processes and improve the reliability of their AI weather models.
Responsibilities:
• Research to Operations pipelines — Our models serve real-time forecasts to customers with strict latency requirements. You'd own uptime end-to-end: build health monitoring, improve logging, diagnose failures across nodes.
• Inference scaling & compute strategy — We have an on-prem cluster but also use cloud providers, especially for production deployments. You'd evaluate cost/performance tradeoffs across cloud options as we scale, and also help manage growing on-prem resources for compute and storage.
• Data pipelines & upstream reliability — Weather data comes from dozens of sources (satellites, government agencies, our own balloon observations) with varying schedules, incomplete documentation and sometimes failing or changing quality. You'd build pipelines for training and realtime data that gracefully handle upstream delays, do QC checks on data, and add logging and alerting for a zoo of edge cases.
• Training infrastructure — Make distributed training runs reliable. They die from silent OOMs, network faults, and storage issues. Build monitoring, auto-recovery, and job scheduling so researchers can launch experiments with less need for babysitting them.
Qualifications:
Required:
• Have run production systems end-to-end — you've been paged at 2am because a pipeline stopped serving and you know how to build systems so that stops happening
• Experience with large datasets spanning petabytes
• Understand cloud GPU economics and how to balance workloads across on-prem and cloud
• Comfortable keeping up with fast-paced model releases and building reliable custom deployments for them
• Experience with PyTorch, Docker, cursed memory management, compression and debugging network saturation.
Company:
WindBorne collects atmospheric data using smart weather balloons for weather forecasting, climate research, and environmental monitoring. Founded in 2015, the company is headquartered in Stanford, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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