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Ai Annotation Jobs in Berkeley, CA (NOW HIRING)

Sunday Inc

Data Annotation Lead

Redwood City, CA · On-site

Own annotation operations end-to-end * Manage the people side of data annotations * Create ... Ability to leverage AI to help improve productivity At Sunday Robotics, we're building technology ...

YO AI Labs

AI Finance Expert - Remote

San Francisco, CA · Remote

$100 - $200/hr

AI Finance Domain Expert Job Type: Contractor (Part-Time) Location: Remote Job Overview We are ... Data Annotation * Problem-Solving * Independent Research * Attention to Detail Preferred ...

Skild AI

Computer Vision AI & ML Engineer

San Mateo, CA · On-site

$127K - $150K/yr

... annotation, QA workflows, dataset management, augmentation, and versioning. • Implement ... Skild AI develops artificial intelligence systems that enable robots to act in physical ...

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 ... Design labeling strategies and tooling for automated annotation, QA workflows, dataset management ...

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 ... Design labeling strategies and tooling for automated annotation, QA workflows, dataset management ...

Skild AI

Computer Vision AI & ML Engineer

San Mateo, CA

$127K - $149K/yr

Position Overview We are seeking a Computer Vision AI & ML Engineer to design, build, and deploy ... Design labeling strategies and tooling for automated annotation, QA workflows, dataset management ...

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All JobsAi Annotation JobsAi Annotation Jobs in Berkeley, CA

Ai Annotation information

See Berkeley, CA salary details

$115.1K

$152.5K

$200.6K

How much do ai annotation jobs pay per year?

As of Aug 18, 2026, the average yearly pay for ai annotation in Berkeley, CA is $152,513.00, according to ZipRecruiter salary data. Most workers in this role earn between $126,200.00 and $186,100.00 per year, depending on experience, location, and employer.

What is an AI annotation?

An AI Annotation job involves labeling, tagging, or annotating data, such as images, text, or audio, to train machine learning models. Annotators help improve AI accuracy by providing high-quality, structured data that algorithms use to learn patterns. Tasks may include identifying objects in images, transcribing speech, or classifying text-based content. This job is essential for developing AI applications like self-driving cars, chatbots, and image recognition systems.

What does an AI annotation specialist do?

As an AI Annotation specialist, your typical day will involve accurately labeling, categorizing, or tagging large volumes of images, text, audio, or video data to train AI models according to project guidelines. You may work independently or as part of a team, using specialized annotation platforms and regularly reviewing your work to ensure quality and consistency. Collaboration with data scientists or project managers may be required to clarify ambiguous cases or update labeling criteria. You can expect periodic feedback and performance reviews to help refine your skills and ensure the data meets the project’s standards, making attention to detail and adaptability essential for success.

What are the key skills and qualifications needed to thrive in the AI annotation position?

To thrive as an AI Annotation professional, you need keen attention to detail, strong analytical skills, and a basic understanding of machine learning concepts, often supported by a high school diploma or relevant technical training. Familiarity with data labeling tools, annotation platforms such as Labelbox or Supervisely, and basic spreadsheet or database management is commonly required. Strong communication, time management, and the ability to maintain focus during repetitive tasks are standout soft skills. These abilities are crucial for producing high-quality, consistent data that supports the effective development and accuracy of AI models.

What are popular job titles related to Ai Annotation jobs in Berkeley, CA?

For Ai Annotation jobs in Berkeley, CA, the most frequently searched job titles are:

What job categories do people searching Ai Annotation jobs in Berkeley, CA look for?

The top searched job categories for Ai Annotation jobs in Berkeley, CA are:

What cities near Berkeley, CA are hiring for Ai Annotation jobs?

Cities near Berkeley, CA with the most Ai Annotation job openings:

Ai Annotation jobs near you
Infographic showing various Ai Annotation job openings in Berkeley, CA as of August 2026, with employment types broken down into 53% Full Time, 20% Part Time, and 27% Contract. Highlights an 32% In-person, and 68% Remote job distribution, with an average salary of $152,513 per year, or $73.3 per hour.

Data Annotation Lead

Physical Intelligence

San Francisco, CA • On-site

Full-time

Re-posted 19 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.

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