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Machine Learning Data Associate Jobs in Santa Clara, CA

MM International

Machine Learning Engineer

Fremont, CA · On-site

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 ...

Adobe

Machine Learning Engineer

San Jose, CA

Work closely with data scientists, AI models to take them to production, scale and make them ... Good understanding of machine learning, deep learning, or data analytics concepts. * Excellent ...

RIT Solutions, Inc.

Machine Learning Engineer

Fremont, CA · On-site

Required : • Strong proficiency in Python, particularly for high-performance and data-intensive ... machine learning solutions in production environments. • Strong commitment to writing clean ...

Advatix Inc.

Machine Learning Engineer

San Mateo, CA · On-site

$110 - $165/hr

Machine Learning Engineer / Research Engineer Pay: $$110,000 - $165,000 Base Salary + Equity Shift ... Data & Model Infrastructure * Build and maintain scalable Python‑based training, evaluation, and ...

shefsolutionsllc

Machine Learning Engineer

Cupertino, CA · On-site

$143 - $264/hr

Your responsibilities will extend to the design and development of a comprehensive data selection ... D/MS degree in Machine Learning, Natural Language Processing, Computer Vision, Data Science ...

New

Articul8

Machine Learning Engineer

Dublin, CA · On-site

$90 - $130/hr

You will own all work related to acquiring high-quality data to power the training of our domain ... Experience in machine learning projects in text or vision, e.g., has trained machine learning ...

Articul8

Machine Learning Engineer

Dublin, CA · On-site

You will own all work related to acquiring high-quality data to power the training of our domain ... Experience in machine learning projects in text or vision, e.g., has trained machine learning ...

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All JobsMachine Learning Data Associate JobsMachine Learning Data Associate Jobs in Santa Clara, CA

Machine Learning Data Associate information

See Santa Clara, CA salary details

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

As of Aug 14, 2026, the average hourly pay for machine learning data associate in Santa Clara, CA is $22.01, according to ZipRecruiter salary data. Most workers in this role earn between $18.08 and $23.41 per hour, depending on experience, location, and employer.

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.

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 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 much do machine learning data associates make?

Machine Learning Data Associates typically earn between $40,000 and $70,000 annually, depending on experience, location, and the complexity of data tasks. Entry-level positions may start lower, while experienced associates working with large datasets or specialized tools can earn higher salaries.

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, with opportunities for skill development and career growth in the tech industry.

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 cities near Santa Clara, CA are hiring for Machine Learning Data Associate jobs?

Cities near Santa Clara, CA 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 Santa Clara, CA as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 18% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $45,773 per year, or $22 per hour.

AI & Machine Learning Engineer I

Gen Digital Inc.

Mountain View, CA • On-site

$120 - $150/hr

Other

Re-posted 3 days ago

Job description

About Gen:

Gen is a global company dedicated to powering Digital Freedom through its trusted consumer brands including Norton, Avast, LifeLock, MoneyLion and more. Our combined heritage is rooted in financial empowerment and cyber safety for the first digital generations, and today we deliver award-winning cybersecurity, online privacy, identity protection and financial wellness solutions to nearly 500 million users in more than 150 countries.

Together, we share a collective passion and vision to protect consumers and help them grow, manage and secure their digital and financial lives. We’re always looking for smart, fearless and high‑impact talent who see AI as a teammate – leveraging it to move faster and deliver meaningful results.

When you’re part of Gen, you’ll have the flexibility, tools and support to do your best work and grow your career – from flexible working options and time off to competitive pay, benefits and well‑being programs.

At Gen, we are scrappy and relentlessly customer driven. We create room for healthy debate, experimentation and continuous learning, and we seek out people with different experiences, identities and ideas to join our team. You’ll work with people who back each other, respect each other and understand that our differences are a competitive advantage.

If this sounds like you, we’d love you to be part of Gen.

About the Role:

Our team is a core part of Gen’s AI transformation. We build machine learning solutions that improve customer growth, retention, personalization, pricing, recommendations, billing success, and long‑term customer value.

We are looking for a hands‑on AI / Machine Learning Engineer I to build models, analyze customer and product data, evaluate experiments, and help deploy practical ML solutions. You will own well‑scoped projects and collaborate with experienced team members and cross‑functional partners.

Experience with recommender systems, uplift modeling, contextual bandits, pricing, or lifecycle personalization is a plus.

Key Responsibilities:

  • Applied ML ownership: Own well‑defined machine learning projects from data exploration and model development through validation, deployment, and iteration.

  • Model development: Build and improve predictive, recommendation, ranking, segmentation, uplift, and customer‑value models for customer personalization and decisioning.

  • Data and feature development: Prepare datasets, define modeling targets, develop features, and ensure data quality for training and evaluation.

  • Experimentation and measurement: Design and analyze A/B tests, holdouts, and offline evaluations to measure model performance and business impact.

  • Deployment and collaboration: Work with engineering, product, analytics, and business partners to integrate models into production and improve them based on results and feedback.

  • AI‑first development: Use AI coding assistants, automation, and reusable tools to improve the speed, quality, and consistency of modeling and analytical workflows.

About You:

  • Degree requirements are flexible. A technical degree in Computer Science, Data Science, Statistics, Mathematics, Operations Research, Economics, Engineering, or a related field is helpful, but equivalent practical experience is equally valued. A Master’s or PhD in a quantitative field is a plus, but not required.

  • Applied ML and model development: Two or more years of professional experience in applied machine learning, data science, ML engineering, applied statistics, or a related field, including experience building and evaluating models with real‑world data.

  • Data analytics: Experience analyzing behavioral, transactional, product, marketing, or customer data and translating findings into practical insights or recommendations.

  • Experimentation: Experience defining success metrics, analyzing experiments, evaluating model performance, and interpreting business impact.

  • Collaborative delivery: Experience working with engineering, product, analytics, or business partners to deploy or apply data‑driven solutions.

  • Relevant specialization: Experience with personalization, recommendation, ranking, uplift modeling, causal inference, contextual bandits, pricing, or lifecycle decisioning is a plus.

  • Machine learning and modeling: Strong Python skills and practical knowledge of supervised learning, model selection, hyperparameter tuning, evaluation, and performance analysis.

  • Data processing and feature engineering: Strong SQL skills and experience using platforms such as BigQuery, Spark, or similar tools for data extraction, cleaning, preprocessing, exploration, and feature development.

  • Analytics and experimentation: Strong analytical and statistical reasoning, including A/B testing, holdout design, statistical significance, incrementally, and business‑impact measurement.

  • Technical tools and workflows: Familiarity with common ML libraries, cloud data or ML platforms, version control, and AI‑assisted development tools.

  • Ownership mindset: Takes responsibility for assigned work, follows through on commitments, and proactively addresses issues.

  • Business‑impact orientation: Connects modeling and analysis to customer experience and measurable outcomes.

  • AI‑first builder mindset: Enjoys modeling, analyzing, automating, and shipping while using AI tools to improve productivity and quality.

  • Growth mindset: Learns quickly, seeks feedback, and continuously develops technical and business knowledge.

  • Clear, collaborative communication: Communicates ideas, assumptions, results, and challenges effectively with technical and non‑technical partners.

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