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Machine Learning Data Associate Jobs in California

PROPRIUS

Machine Learning / Data Scientist

Bodega Bay, CA · On-site

$110K - $140K/yr

Job Summary Our client is looking for a machine learning engineer to join our existing ML team in developing and refining a predictive application. The ideal candidate is adept at using large data ...

Apple

Machine Learning - Data Scientist

Sunnyvale, CA · On-site

The Video Engineering Data Analytics and Quality group is seeking an expert in evaluating machine learning and deep learning models, including foundation models and multimodal systems. This role will ...

Apple

Machine Learning - Data Scientist

Sunnyvale, CA

$150K - $277K/yr

  • Medical

  • Dental

  • Retirement

The Video Engineering Data Analytics and Quality group is seeking an expert in evaluating machine learning and deep learning models, including foundation models and multimodal systems. This role will ...

Apple Inc.

Machine Learning - Data Scientist

Sunnyvale, CA · On-site

$150.40 - $277.60/hr

  • Medical

  • Dental

  • Retirement

The Video Engineering Data Analytics and Quality group is seeking an expert in evaluating machine learning and deep learning models, including foundation models and multimodal systems. This role will ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Coordinate data collection and annotation efforts. * Work with real-time data and content coming from various data sources. * Manage machine learning data pipelines. * Design tests for machine ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Coordinate data collection and annotation efforts. * Work with real-time data and content coming from various data sources. * Manage machine learning data pipelines. * Design tests for machine ...

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All JobsMachine Learning Data Associate JobsMachine Learning Data Associate Jobs in California

Machine Learning Data Associate information

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How much do machine learning data associate jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for machine learning data associate in California is $18.49, according to ZipRecruiter salary data. Most workers in this role earn between $15.19 and $19.71 per hour, depending on experience, location, and employer.

What is a machine learning data associate?

Machine Learning Data Associates are professionals who support the development of machine learning models by preparing, labeling, and validating data sets. Their work ensures that data used for training algorithms is accurate, consistent, and properly annotated. They may also assist with data cleaning, quality checks, and sometimes basic data analysis tasks. This role is crucial in industries where high-quality labeled data is essential for building effective AI systems.

What are the key skills and qualifications needed to thrive as a machine learning data associate?

To thrive as a Machine Learning Data Associate, you need strong analytical skills, attention to detail, and a basic understanding of data annotation and labeling processes, often supported by a degree in computer science or a related field. Familiarity with data management tools, annotation platforms, and sometimes scripting languages like Python is typically required. Strong communication, collaboration, and problem-solving abilities help you work efficiently with data science teams and ensure high-quality outcomes. These skills and qualities are crucial for producing accurate datasets that directly impact the effectiveness of machine learning models.

How does a machine learning data associate typically collaborate with data scientists and engineers within a project team?

As a Machine Learning Data Associate, you play a vital role in supporting data scientists and engineers by annotating, cleaning, and organizing large datasets to ensure high data quality. You'll frequently communicate with team members to clarify labeling guidelines, provide feedback on data inconsistencies, and report any edge cases encountered during annotation. This collaboration ensures that the datasets used for training machine learning models are accurate and comprehensive, directly impacting the success of the project. Expect regular team meetings and ongoing feedback loops to maintain alignment with evolving project requirements.

What is the difference between Machine Learning Data Associate vs Data Analyst?

AspectMachine Learning Data AssociateData Analyst
Required SkillsData cleaning, labeling, basic programming, understanding of ML workflowsData interpretation, visualization, statistical analysis
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, marketing, healthcare sectors
Common CertificationsData Science certifications, Python, SQLExcel, Tableau, SQL certifications

The main difference is that Machine Learning Data Associates focus on preparing and labeling data specifically for machine learning models, while Data Analysts interpret data to generate insights for business decisions. Both roles require strong data skills and often overlap, but their primary objectives and work environments differ.

How do I become a machine learning data associate?

To become a machine learning data associate, candidates typically need a high school diploma or equivalent, with some roles preferring a bachelor's degree in computer science, data science, or related fields. Relevant skills include data annotation, understanding of machine learning concepts, and proficiency with tools like Excel, SQL, or data labeling platforms. Gaining experience through internships or certifications can improve job prospects in this field.

Is a Machine Learning Data Associate a good job?

A Machine Learning Data Associate role involves preparing and managing data for machine learning models, often requiring skills in data cleaning, annotation, and familiarity with tools like Python or SQL. It can be a good entry-level position for those interested in AI and data science, offering opportunities to develop technical skills and gain industry experience. Compensation and job satisfaction vary depending on the employer and location, but it generally provides a solid foundation for a career in machine learning or data analysis.

What cities in California are hiring for Machine Learning Data Associate jobs?

Cities in California with the most Machine Learning Data Associate job openings:

Machine Learning Data Associate jobs near you
Infographic showing various Machine Learning Data Associate job openings in California as of August 2026, with employment types broken down into 1% As Needed, 88% Full Time, 9% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $38,464 per year, or $18.5 per hour.

Machine Learning / Data Scientist

PROPRIUS

Bodega Bay, CA • On-site

$110K - $140K/yr

Full-time

Re-posted 3 days ago

Job description

Machine Learning Engineer

Location: San Francisco, CA

Sponsorship: No

Relocation: No

Industry: Machine Learning

Our client is a digital invention agency focused on machine learning methodologies, enterprise mobile and web applications, eCommerce, augmented reality and IoT. They look to innovatively make this world a better place with each and every product, system, idea and app they release.

Job Summary

Our client is looking for a machine learning engineer to join our existing ML team in developing and refining a predictive application.

The ideal candidate is adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action.

You must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations. You must have a proven ability to drive business results with their data-based insights. You must be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.

As a ML Engineer, you will:

  • Work with stakeholders throughout the organization to identify opportunities for leveraging data to drive business solutions
  • Mine and analyze data from databases to drive optimization and improvement of product development, marketing techniques and business strategies
  • Assess the effectiveness and accuracy of new data sources and data gathering techniques
  • Develop custom data models and algorithms to apply to data sets
  • Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes
  • Coordinate with different functional teams to implement models and monitor outcomes
  • Develop processes and tools to monitor and analyze model performance and data accuracy

For this role you will need:

  • Strong with Statistics and can code in either R, Python, Java and Scala
  • Experience with designing and building using micro-services architectural pattern, web APIs using dotnet core & C#
  • Experience and passion for simulations, optimization, neural networks, artificial intelligence (deep learning and machine learning)
  • Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, GIT, SQL, etc.
  • Able to understand statistical solutions and execute similar activities
  • Experience in data wrangling and advanced analytic modeling
  • Strong communication and organizational skills and has the ability to deal with ambiguity while juggling multiple priorities and projects at the same time
  • Experience visualizing/presenting data for stakeholders using: Seaborn, Business Objects, D3, ggplot, etc.
  • Ability to investigate the feasibility and data requirements necessary to develop an ML solution for a given problem
  • Ability to design, build and test production ready ML-based products while interpreting and explaining the basis for predictions generated by ML models

The perfect candidate will have:

  • Knowledge and experience using one or more of the following, or similar, machine learning software frameworks: CAFFE, Torch 7, Keras and Tensorflow
  • Experience building production-ready NLP or information retrieval systems
  • Hands-on experience with NLP tools, libraries and corpora (e.g. NLTK, Stanford CoreNLP, Wikipedia corpus, etc.)

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