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Entry Level Ai Data Annotation Jobs in California

Transformcap

Clinical Data Abstractor

San Mateo, CA · On-site

By uniquely combining data, AI, and community, Citizen is building a personalized AI advocate ... Support quality assurance initiatives, pilot annotation projects, and guidance and training ...

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Entry Level Ai Data Annotation information

What is an entry level AI data annotation?

An Entry Level AI Data Annotation job involves labeling and categorizing data such as images, text, audio, or video to help train artificial intelligence (AI) and machine learning models. Annotators follow specific guidelines to tag data accurately, ensuring that AI systems learn to recognize patterns correctly. These positions typically require attention to detail, basic computer skills, and the ability to follow instructions. No advanced technical knowledge is usually required, making it a great way to start a career in the AI or tech industry.

What are the key skills and qualifications needed to thrive as an entry level AI data annotation specialist?

To thrive as an Entry Level AI Data Annotation Specialist, attention to detail, basic computer literacy, and a high school diploma or equivalent are typically required. Familiarity with data labeling platforms, annotation tools, and spreadsheet software is often expected. Strong organizational skills, focus, and the ability to work independently help individuals excel in this role. These skills ensure accurate and efficient data labeling, which is crucial for developing reliable AI and machine learning models.

What are some common challenges faced by entry level AI data annotation specialists, and how can they be addressed?

Entry-level AI data annotation specialists often encounter challenges such as maintaining consistency and accuracy while labeling large volumes of data, understanding nuanced instructions, and adapting to changing project requirements. These challenges can be addressed by actively seeking clarification from team leads, participating in training sessions, and regularly reviewing annotation guidelines. Collaborating with teammates and using quality assurance feedback also helps improve accuracy and ensures alignment with project standards.

What are the most commonly searched types of Ai Data Annotation jobs in California?

The most popular types of Ai Data Annotation jobs in California are:

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For Entry Level Ai Data Annotation jobs in California, the most frequently searched job titles are:

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Entry Level Ai Data Annotation jobs near you
Infographic showing various Entry Level Ai Data Annotation job openings in California as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 82% Full Time, 13% Part Time, and 3% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution.

Senior Annotation and Data Pipeline Manager

San Francisco, CA • On-site

Genesis AI
See more jobs at Genesis AIexternal

Other

Posted 11 days ago

Job description

The role

We have built a frontier model and put Eno in front of the world, fast. Behind that is a data engine: the machine that turns a raw human demonstration into data the model is measurably better for. This role owns that engine.

A worn glove and a camera produce a raw demonstration, not training data. You will build the pipeline and the annotation operation that turn raw demonstrations into clean, labeled, training-ready data, and make it scale with automation rather than headcount. You will own the datasets, what gets annotated, and the ontology, how it gets labeled, bring vision-language models to bear on trajectory labeling and language grounding, and close the loop so the engine keeps making the model better. This role serves the whole operation, our own floors and our partner-funded collection.

What you'll do

  • Run the data engine. Own the loop from raw trajectory and video to training-ready datasets, with validation steps that guarantee clean, correctly labeled data.

  • Own datasets and ontology. Decide what gets annotated and how, designing the ontology with the model team for its training implications.

  • Automate with models. Use vision-language models for automated trajectory annotation, language grounding, and data synthesis, so the pipeline scales without linear headcount, while holding the quality bar.

  • Run the annotation operation. Stand up and scale labeling, internal and vendor, against a clear quality bar and a delivery schedule the model team can plan around.

  • Close the loop. Turn real-robot eval failures into targeted collection and annotation jobs, and prove the new data improves the model.

  • Own the metrics. Track inter-annotator agreement, label error rate, and throughput per annotator-hour, and drive them the right way.

What we're looking for

  • You have scaled an annotation or data pipeline at a serious operation. Four or more years in data or ML pipelines, including time leading the work. At a frontier AI lab or a top data operation, you have taken raw robot or embodied data to training-ready at volume and you know exactly where it breaks. The people who have done this are a small group. If you are one, we want to talk.

  • You can build, not just manage. Strong Python (Pandas, NumPy, PyTorch) and SQL. You write the automation that shrinks the pipeline.

  • ML literacy. You understand training versus test, precision and recall, and overfitting well enough to design an ontology that helps the model, not just labels data.

  • Hands-on technical leadership. You can run a labeling operation and stay a hands-on contributor at the same time.

  • Comfortable with ambiguity and speed. You move fast in a research-paced environment and bring order to it.

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