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Freelance Machine Learning Data Annotation Jobs in San Jose, CA

Vizcom

Machine Learning Data Engineer

San Francisco, CA · On-site

$150 - $200/hr

... data. Your job will be to help turn that trail into a machine. A design session isn't a simple ... Generous annual Learning & Development allowance * Paid parental leave * Weekly catered lunch at ...

YO AI Labs

AI Data Scientist Expert - Remote

Palo Alto, CA · Remote

$100 - $200/hr

... Annotation * Data Interpretation * Fact Checking * Independent Research * Problem-Solving * Attention to Detail Preferred Qualifications * 3+ years of experience in Data Science, Machine Learning ...

YO AI Labs

AI Data Science Expert - Remote

San Jose, CA · Remote

$100 - $200/hr

... Annotation * Data Interpretation * Fact Checking * Independent Research * Problem-Solving * Attention to Detail Preferred Qualifications * 3+ years of experience in Data Science, Machine Learning ...

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All JobsMachine Learning Data Annotation JobsFreelance Machine Learning Data Annotation JobsFreelance Machine Learning Data Annotation Jobs in San Jose, CA

Freelance Machine Learning Data Annotation information

See San Jose, CA salary details

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How much do freelance machine learning data annotation jobs pay per hour?

As of Sep 7, 2026, the average hourly pay for freelance machine learning data annotation in San Jose, CA is $25.63, according to ZipRecruiter salary data. Most workers in this role earn between $20.29 and $29.28 per hour, depending on experience, location, and employer.

What is freelance machine learning data annotation?

Freelance machine learning data annotation involves labeling or tagging data—such as images, text, audio, or video—to help train machine learning models. As a freelancer, you work independently or through platforms, completing specific annotation tasks assigned by companies or researchers. This work is essential because high-quality labeled data is required for AI systems to learn and make accurate predictions. Annotators may categorize images, transcribe speech, or highlight relevant information in documents. The flexibility of freelancing allows you to choose projects and work remotely.

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

To thrive as a Freelance Machine Learning Data Annotation specialist, you need attention to detail, basic knowledge of data labeling concepts, and familiarity with machine learning data types. Experience with annotation tools (such as Labelbox, RectLabel, or CVAT) and understanding of data privacy protocols are commonly required. Strong communication, time management, and the ability to follow complex guidelines are essential soft skills for delivering accurate results. These skills ensure high-quality, consistent data annotation, which is critical for effective machine learning model training and performance.

What are some common challenges faced by freelance machine learning data annotators, and how can they be managed?

Freelance machine learning data annotators often encounter challenges such as maintaining data accuracy, handling repetitive tasks, and understanding complex annotation guidelines. Staying organized and regularly reviewing project instructions can help ensure consistency and quality in annotations. Additionally, communicating proactively with project managers and utilizing annotation tools efficiently can help manage workload and clarify uncertainties. Building expertise in different data types (text, image, audio) also allows annotators to diversify their projects and reduce monotony.

What is the difference between Freelance Machine Learning Data Annotation vs Data Labeler?

AspectFreelance Machine Learning Data AnnotationData Labeler
CredentialsBasic understanding of annotation tools, sometimes with specialized domain knowledgeTypically no formal credentials required
Work EnvironmentRemote, flexible, project-basedOften remote or in-house, depending on employer
Industry UsageUsed in AI/ML development for training datasetsUsed in data preparation for various industries, including AI
Search/Comparison IntentFocuses on freelance opportunities, project scope, and toolsMore general, often employed by companies for data labeling tasks

Freelance Machine Learning Data Annotation involves independently completing annotation tasks for AI models, often with specialized tools and domain knowledge. Data Labelers typically perform similar tasks but may work as employees or contractors within a company. The main difference lies in the freelance nature and project-based work of data annotation roles.

Can I work for freelance machine learning data annotation with no experience?

Freelance machine learning data annotation jobs often do not require prior experience, as many tasks involve simple labeling or categorization that can be learned quickly. Basic computer skills, attention to detail, and familiarity with annotation tools are helpful, and training is usually provided. However, building a portfolio or gaining some familiarity with data annotation platforms can improve job prospects.

What are the most commonly searched types of Machine Learning Data Annotation jobs in San Jose, CA?

The most popular types of Machine Learning Data Annotation jobs in San Jose, CA are:

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For Freelance Machine Learning Data Annotation jobs in San Jose, CA, the most frequently searched job titles are:

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The top searched job categories for Freelance Machine Learning Data Annotation jobs in San Jose, CA are:

What cities near San Jose, CA are hiring for Freelance Machine Learning Data Annotation jobs?

Cities near San Jose, CA with the most Freelance Machine Learning Data Annotation job openings:

Freelance Machine Learning Data Annotation jobs near you
Infographic showing various Freelance Machine Learning Data Annotation job openings in San Jose, CA as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 15% Part Time, and 3% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $53,306 per year, or $25.6 per hour.

Technical Specialist, ML Data

Waymo

Mountain View, CA • On-site

Full-time

Re-posted 22 days ago

Job description

The Labeling Data Program Org owns the execution and creation of curated labeled datasets which are critical for training and evaluation of ML models that power the Waymo Driver.

As a Technical Specialist in this team, you will be the operational backbone of our machine learning initiatives. You will own and drive the complex, cross-functional programs that deliver high-quality data-the lifeblood of our models. You will orchestrate the end-to-end data lifecycle, from defining requirements for new datasets and tooling to scaling data pipelines and ensuring our ML teams have the resources they need to innovate. This is a high-impact role for a technical, detail-oriented leader who thrives on turning ambiguous data needs into tangible, scalable solutions.

You will:

  • Drive the ML Flywheel: Lead the end-to-end lifecycle of ML data, from initial mining and curation to labeling policy definition, validation, and model evaluation. Work cross-functionally to ensure coordination and alignment on objectives and key results.
  • Translate Policy to Code:  Lead the development of sophisticated labeling policies for complex AV domains (e.g., behavior prediction, long-tail edge cases). Convert ambiguous ML quality problems into precise, scalable annotation policies and data taxonomies. 
  • Build Evals & Metrics: Design and implement ML evaluation frameworks. Identify key data-centric drivers of model performance and create the metrics that track ML quality at the data level.
  • Cross-Functional Leadership: Communicate effectively with technical and non-technical audiences at various levels of seniority, including producing analytical write-ups, dashboards, and data visualizations to convey your findings and recommendations to our team and cross-functional stakeholders
  • Influence ML data selection strategies (active learning, hard-mining) to ensure we are labeling the most impactful data to maximize ROI from the labeling effort

You have:

  • 8+ years of experience in data analysis, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data.
  • Deep understanding of the ML data lifecycle: labeling, taxonomy design, quality control, and data curation.
  • Experience with ML Data Flywheel and working understanding of ML development life cycle (e.g., model deployment,model evaluation, data processing, debugging, fine tuning).
  • Background in leading and managing complex programs that span across organizations and functions, with specific experience in Machine Learning data annotation or Human-in-the-Loop initiatives.
  • Strong ability to thrive in a dynamic environment, demonstrating comfort and effectiveness when dealing with ambiguity.
  • Ability to quickly learn and implement new concepts and utilize proprietary tools. Strong understanding of driving rules and regulations.

 

We prefer:

  • Experience with scripting language, machine learning tools, techniques and systems (including prompt engineering and fine-tuning LLMs ) is a strong plus.
  • Demonstrated ability to extract, manipulate, and apply machine learning techniques to high volumes of critical, product-related data.
  • Demonstrated ability in working with a variety of engineering stakeholders to gather requirements, explain models, and iterate to make improvements.
  • Excellent written and verbal communication and ability to describe technical implementations or analyses to a non-tech audience in an effective manner.
  • Excellent problem-solving and critical thinking skills with attention to detail in an ever-changing environment.
  • A greater focus on using your subject matter expertise for results analysis and direct customer consultation in the development of new and improved. solutions.

Apply