1

Ai Data Annotation Jobs in California (NOW HIRING)

This role is about building and leading a world class in-house data annotation team that is able to ... Ability to leverage AI to help improve productivity At Sunday Robotics, we're building technology ...

... data annotation • Ability to leverage AI to help improve productivity Company : Sunday is a robotics and artificial intelligence company that develops an autonomous home robot to assist with ...

AI Data Software Engineer

San Francisco, CA · On-site

$134K - $162K/yr

Tiki AI provides end-to-end data annotation and intelligence solutions that transform raw information into high-quality, actionable datasets. Founded in , the company is headquartered in San ...

Data Solutions Engineer

Mountain View, CA · On-site

$136K - $163K/yr

Abaka AI provides accurate and efficient AI data services, including data collection, data cleaning, data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA ...

Data Solutions Engineer

Mountain View, CA · On-site

$136K - $163K/yr

Abaka AI provides accurate and efficient AI data services, including data collection, data cleaning, data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA ...

Data Solutions Engineer

Mountain View, CA · On-site

$136K - $163K/yr

Abaka AI provides accurate and efficient AI data services, including data collection, data cleaning, data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA ...

Technical Program Manager, Data Engine

Redwood City, CA · On-site

$157K - $204K/yr

... AI labs/data vendors • Technical skills to build tools for data annotation or collection • Ability to leverage AI to help improve productivity Company : Sunday is a robotics and artificial ...

Data Operations Engineer

Mountain View, CA · On-site

$136K - $163K/yr

Abaka AI provides accurate and efficient AI data services, including data collection, data cleaning, data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA ...

Data Operations Engineer

Mountain View, CA · On-site

$136K - $163K/yr

Abaka AI provides accurate and efficient AI data services, including data collection, data cleaning, data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA ...

Data Operations Engineer

Mountain View, CA · On-site

$136K - $163K/yr

Abaka AI provides accurate and efficient AI data services, including data collection, data cleaning, data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA ...

next page

Showing results 1-20

Ai Data Annotation information

What are the key skills and qualifications needed to thrive in the Ai Data Annotation position, and why are they important?

To excel in AI Data Annotation, you need strong attention to detail, data accuracy, and a basic understanding of data labeling concepts, typically supported by a high school diploma or equivalent. Familiarity with annotation tools such as Labelbox, Supervisely, or similar platforms is often required, and some employers may value basic programming or machine learning course certifications. Excellent communication, the ability to follow detailed guidelines, and time management are valuable soft skills in this role. These skills ensure the production of high-quality annotated datasets, which are critical for training reliable AI and machine learning models.

What are the typical daily tasks and team dynamics for an AI Data Annotation position?

As an AI Data Annotator, your typical day involves labeling and tagging data such as images, audio, or text according to specific project guidelines, often using specialized annotation software. You may work independently or as part of a remote or on-site team, collaborating with data scientists and quality assurance specialists to ensure consistency and accuracy. Regular feedback sessions and quality checks are common to maintain high annotation standards. The role can be repetitive, but attention to detail and clear communication with team members help create datasets that are crucial for training effective AI systems.

What is an AI Data Annotation job?

An AI Data Annotation job involves labeling or tagging data, such as text, images, audio, or video, to train machine learning models. Annotators ensure that data is accurately categorized so AI systems can learn to recognize patterns and make predictions. This work is crucial for improving AI applications like self-driving cars, chatbots, and image recognition software. It often requires attention to detail and familiarity with specific annotation tools.

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 job categories do people searching Ai Data Annotation jobs in California look for? The top searched job categories for Ai Data Annotation jobs in California are:
What cities in California are hiring for Ai Data Annotation jobs? Cities in California with the most Ai Data Annotation job openings:
Infographic showing various Ai Data Annotation job openings in California as of July 2026, with employment types broken down into 66% Full Time, 15% Part Time, and 19% Contract. Highlights an 57% In-person, and 43% Remote job distribution.
Data Annotation Lead

Data Annotation Lead

Physical Intelligence

San Francisco, CA • On-site

Full-time

Posted 17 days ago


Job description

Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future.
The role
We're looking for a Data Annotation Lead to own annotation operations and scale the team behind it. Annotation is core to how our models improve, and demand is growing fast. You will scale the annotation workforce from 100s to 1,000s while raising the quality bar - designing the org, the training pipeline, the quality system, and the metrics that let it scale efficiently.
You will own the people and the operation: throughput, quality, cost, and delivery across every annotation type.
In this role you will
  • Own annotation operations end-to-end: throughput, quality, cost, and on-time delivery across all annotation types.
  • Scale the annotation workforce from 100s to 1,000s: workforce planning, org design, and the hiring and onboarding funnel.
  • Build and lead a multi-layer management structure; hire, develop, and manage managers and team leads.
  • Scale throughput with autolabeling and model-based annotation: design human-in-the-loop workflows where models pre-label and annotators review, correct, and escalate, so output grows faster than headcount.
  • Stand up the training and certification pipeline that brings new annotators and teams to the quality bar quickly and consistently.
  • Define and continuously raise the quality bar: rubrics, calibration, audit/QA loops, and quality-adjusted productivity.
  • Establish operational metrics and reporting (presence, throughput, acceptance/rejection, rework) and drive week-over-week improvement.
  • Run capacity planning and prioritization against competing demand; allocate teams to the highest-impact work.
  • Manage performance at scale with clear standards, feedback, and a fair improvement/exit process.
  • Partner with product and engineering to define annotation tooling that unlocks throughput and quality.
  • Partner with research and project leads to translate annotation needs into clear instructions, rubrics, and SLAs.
  • Own the in-house vs. vendor mix and manage external partners where used.
  • Own the annotation operating budget and unit economics; improve cost-per-annotation while protecting quality.

What you'll bring
  • 7+ years leading scaled data or annotation operations, including teams in the 100s+.
  • 3+ years as a manager of managers.
  • Track record standing up 0→1 annotation programs.
  • Deep command of annotation best practices, operations, and strategy.
  • Experience integrating autolabeling and model-based annotation into human workflows; building human-in-the-loop pipelines that raise throughput without sacrificing quality.
  • Fluency with operational and quality metrics; data-driven management of large workforces.
  • Strong cross-functional partnership with product, engineering, and research/ML.
  • Clear written and verbal communication; able to set and hold standards across a large, distributed team.
  • Working understanding of ML and why annotation quality drives model performance.

Nice to have
  • Experience in robotics, autonomous vehicles, or frontier-AI data pipelines.
  • Experience managing distributed/global and/or vendor workforces.
  • Built annotation tooling or partnered tightly with a tooling team.
  • Experience training or fine-tuning autolabeling models, or partnering closely with the ML teams that do.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.