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Entry Level Ai Data Annotation Jobs in California

Skild AI

Computer Vision AI & ML Engineer

San Mateo, CA · On-site

$127K - $149K/yr

Company Overview At Skild AI, we are building the world's first general purpose robotic ... Experience with data annotation tools, dataset management, and augmentation techniques.

Skild AI

Computer Vision AI & ML Engineer

San Mateo, CA · On-site

$127K - $149K/yr

Company Overview At Skild AI, we are building the world's first general purpose robotic ... Experience with data annotation tools, dataset management, and augmentation techniques.

Specter

Data Operations Engineer

San Francisco, CA · On-site

$80 - $120/hr

Today, we build video sensors with state‑of‑the‑art AI agents that answer any question ... Build and maintain internal tooling for labelers, including annotation interfaces, task pipelines ...

Nimble

AI Robotics Research Engineer

San Francisco, CA · On-site

Nimble is an AI robotics company focused on creating an autonomous supply chain to enhance commerce ... data annotation and labeling • Support implementation of models into production systems ...

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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:

What are popular job titles related to Entry Level Ai Data Annotation jobs in California?

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

What job categories do people searching Entry Level Ai Data Annotation jobs in California look for?

The top searched job categories for Entry Level Ai Data Annotation jobs in California are:

What cities in California are hiring for Entry Level Ai Data Annotation jobs?

Cities in California with the most Entry Level Ai Data Annotation job openings:

Entry Level Ai Data Annotation jobs near you
Infographic showing various Entry Level Ai Data Annotation 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.

Computer Vision AI & ML Engineer

Skild AI

San Mateo, CA • On-site

$127K - $149K/yr

Other

Re-posted 17 days ago

Job description

Company Overview
At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects.
Position Overview
We are seeking a Computer Vision AI & ML Engineer to design, build, and deploy advanced perception systems for real-world robotics and automation. You will work across the full machine learning lifecycle-model development, data strategy, evaluation, and production integration-to deliver robust, high-performance vision capabilities. This role combines applied research with hands-on engineering and offers the opportunity to influence both architecture and roadmap decisions.
Responsibilities
  • Develop and optimize deep learning models for depth estimation, object detection, segmentation, tracking, and 3D scene understanding using multi-modal sensor data.
  • Build scalable pipelines for data processing, training, evaluation, and deployment into real-world and real-time systems.
  • Design labeling strategies and tooling for automated annotation, QA workflows, dataset management, augmentation, and versioning.
  • Implement monitoring and reliability frameworks, including uncertainty estimation, failure detection, and automated performance reporting.
  • Conduct proof-of-concept experiments to evaluate new algorithms and perception techniques; translate research insights into practical prototypes.
  • Collaborate with robotics, systems, and simulation teams to integrate perception models into production pipelines and improve end-to-end performance.
Preferred Qualifications
  • Strong experience with deep learning frameworks (PyTorch, TensorFlow, or JAX).
  • Background in computer vision tasks such as detection, depth estimation, segmentation, tracking, or 3D scene understanding.
  • Proficiency in Python; familiarity with C++ is a plus.
  • Experience building training pipelines, evaluation frameworks, and ML deployment workflows.
  • Knowledge of 3D geometry, sensor processing, or multi-sensor fusion (RGB-D, LiDAR, stereo).
  • Experience with data annotation tools, dataset management, and augmentation techniques.
  • Familiarity with robotics, simulation environments (Isaac Sim, Gazebo, Blender), or real-time systems.
  • Understanding of uncertainty modeling, reliability engineering, or ML monitoring/MLOps practices.

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