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Full Time Machine Learning Data Annotation Jobs in San Diego, CA

Waymo

Senior Machine Learning Engineer, Robotics

San Diego, CA · On-site

$110K - $152K/yr

The Sensor Foundations Team's purpose is to provide cleaned-up sensor data through highly optimized ... For this role, we are looking for a strong Software Engineer with robotics and machine learning ...

Qualcomm

Senior Engineer - Machine Learning

San Diego, CA · On-site

$110K - $152K/yr

Improve data quality and model reliability through systematic evaluation Cross-functional ... Machine learning fundamentals (supervised, unsupervised, deep learning) * Transformer architectures ...

Qualcomm

Senior Engineer - Machine Learning

San Diego, CA · On-site

$110K - $152K/yr

Improve data quality and model reliability through systematic evaluation Cross-functional ... Machine learning fundamentals (supervised, unsupervised, deep learning) * Transformer architectures ...

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Showing results 21-40

All JobsMachine Learning Data Annotation JobsFull Time Machine Learning Data Annotation JobsFull Time Machine Learning Data Annotation Jobs in San Diego, CA

Full Time Machine Learning Data Annotation information

See San Diego, CA salary details

$39.8K

$130.3K

$208.6K

How much do full time machine learning data annotation jobs pay per year?

As of Sep 7, 2026, the average yearly pay for full time machine learning data annotation in San Diego, CA is $130,313.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,600.00 and $144,400.00 per year, depending on experience, location, and employer.

What is a full time machine learning data annotation job?

Full time machine learning data annotation jobs involve labeling, tagging, or categorizing data such as images, text, audio, or video to help train machine learning models. Data annotators play a crucial role in ensuring that AI systems learn from high-quality, accurately labeled datasets. These positions often require attention to detail, consistency, and sometimes familiarity with the subject matter or specialized tools. Full-time roles may be remote or onsite and can span industries like autonomous vehicles, healthcare, retail, and more.

What are some common challenges faced by machine learning data annotators, and how are these typically addressed within a team?

Machine learning data annotators often encounter challenges such as maintaining consistency in labeling, handling ambiguous data, and meeting tight deadlines for large datasets. Teams usually address these by establishing clear annotation guidelines, conducting regular training sessions, and implementing quality assurance processes like peer reviews and spot checks. Collaboration with data scientists and project managers is also common, ensuring that annotators can ask questions and clarify uncertainties, leading to higher-quality labeled data and a supportive work environment.

What are the key skills and qualifications needed to thrive as a full time machine learning data annotation specialist, and why are they important?

To thrive as a Full Time Machine Learning Data Annotation Specialist, you need strong attention to detail, basic data literacy, and familiarity with data labeling concepts, often supported by a high school diploma or equivalent. Proficiency in specialized annotation platforms, spreadsheet tools, and sometimes knowledge of Python or labeling frameworks is typically required. Reliability, patience, and effective communication are valuable soft skills for ensuring accuracy and collaborating with team members. These skills and qualities are crucial because they directly impact the quality of training data, which is essential for developing effective machine learning models.

What is the difference between Full Time Machine Learning Data Annotation vs Data Labeling Specialist?

AspectFull Time Machine Learning Data AnnotationData Labeling Specialist
CredentialsHigh school diploma or equivalent; some roles prefer technical certificationsHigh school diploma or equivalent; training often provided on the job
Work EnvironmentOffice or remote; collaborative with data science teamsRemote or office; focused on labeling tasks
Industry UsageUsed across AI/ML companies, tech firms, and startupsCommon in AI/ML, data services, and outsourcing companies
Job FocusCreating labeled datasets for machine learning modelsAnnotating data such as images, videos, or text for AI training

Full Time Machine Learning Data Annotation involves creating high-quality labeled datasets for AI models, often requiring technical understanding. Data Labeling Specialists focus on annotating data accurately, typically with less emphasis on technical skills. Both roles are essential in AI development but differ mainly in scope and technical complexity.

What are popular job titles related to Full Time Machine Learning Data Annotation jobs in San Diego, CA?

For Full Time Machine Learning Data Annotation jobs in San Diego, CA, the most frequently searched job titles are:

What job categories do people searching Full Time Machine Learning Data Annotation jobs in San Diego, CA look for?

The top searched job categories for Full Time Machine Learning Data Annotation jobs in San Diego, CA are:

What cities near San Diego, CA are hiring for Full Time Machine Learning Data Annotation jobs?

Cities near San Diego, CA with the most Full Time Machine Learning Data Annotation job openings:

Full Time Machine Learning Data Annotation jobs near you
Infographic showing various Full Time Machine Learning Data Annotation job openings in San Diego, CA as of July 2026, with employment types broken down into 16% Full Time, 9% Part Time, 1% Temporary, 68% Contract, and 6% Nights. Highlights an 4% Physical, and 96% Remote job distribution, with an average salary of $130,313 per year, or $62.7 per hour.

Principal Software Engineer, AI & Data Platform (Xora Portfolio Company)

Xora Innovation

San Diego, CA • On-site

$143K - $191K/yr

Full-time

Re-posted 5 days ago

Job description

ABOUT ELEMYNT
Elemynt builds secure AI infrastructure for scientific and engineering R&D teams. Our platform helps organizations connect data, models, compute, and expert workflows in environments where reliability, traceability, and data control matter.
We are building a small, ambitious engineering team across Singapore and the United States to turn advanced scientific computing into production software that real technical teams can use.
ABOUT THE ROLE
Elemynt's platform turns scientific and engineering data into reusable assets for analysis, model training, and automated workflows.
This role owns the data and AI engineering foundation that makes those systems reliable, scalable, and measurable. You will define core patterns for data modeling, training pipelines, evaluation systems, and intelligent workflow interfaces, then prove those patterns in production code.
This is a hands on principal role for someone who can set technical direction and still build the hardest parts themselves.
WHAT YOU WILL DO
  • Architect the data foundation for large scale scientific and engineering output, keeping results clean, queryable, reusable, and ready for model training.
  • Model domain specific scientific data so the same datasets can support interactive analysis, automation, and downstream machine learning workflows.
  • Build scalable data processing patterns across object storage, analytical stores, and training optimized formats.
  • Create machine learning data pipelines for curation, deduplication, formatting, evaluation sets, and regression tracking.
  • Build and operate training and fine tuning pipelines for models used in scientific and workflow driven products.
  • Develop intelligent workflow interfaces that connect user intent, structured platform capabilities and executable workflows without exposing unnecessary complexity to users.
  • Own model evaluation, benchmarking, automated scoring, and quality tracking so each iteration is measurable.
  • Set data and AI engineering standards for the team and turn them into code, documentation, and reusable patterns.

WHAT WE ARE LOOKING FOR
  • Bachelor's or Master's degree in Computer Science or a related engineering field, with 10 plus years building and shipping production software.
  • Expert Python and a strong record of shipping systems end to end.
  • Deep experience with large scale data systems, including object storage, analytical processing, training optimized formats, and production data pipelines.
  • Hands on experience building data pipelines for model training, fine tuning, evaluation, and continuous improvement.
  • Direct experience training or fine tuning models for structured outputs, tool use, workflow automation, or domain specific applications.
  • Strong understanding of relational, document, and columnar data models, with judgment about where each belongs.
  • Comfort operating in cloud, enterprise, and technical compute environments, including distributed training or large scale batch processing.
  • Ability to set technical direction in ambiguous early stage environments and carry it through implementation.

NICE TO HAVE
  • Experience applying machine learning to scientific data, such as property prediction, generative models, graph based methods, or simulation data.
  • Experience with atomistic, materials, chemistry, or engineering data systems.
  • Experience with retrieval over structured data, knowledge graphs, or hybrid search systems.
  • Experience designing APIs or tool interfaces that intelligent systems can call reliably.
  • Experience building complex data and machine learning workflows on production orchestrators.
  • Contributions to open source machine learning, data infrastructure, or scientific computing tools.

LOCATION
Singapore or United States. Work model is on site or hybrid, depending on location.
CLOSING NOTE
You do not need to tick every box. If this is clearly your kind of work, we would like to hear from you.

Apply