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Manager Annotation Jobs in California (NOW HIRING)

Sunday

Data Annotation Lead

Redwood City, CA · On-site

This role involves managing the people side of data annotations, creating processes, and ensuring quality in data annotation efforts. Responsibilities : • Build a data annotation team • Manage ...

Sunday Inc

Data Annotation Lead

Redwood City, CA · On-site

Manage the people side of data annotations * Create documentation * Be in the weeds and annotate data yourself anytime something new is being designed * Create data annotation processes * Vibe code ...

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Manager Annotation information

What are the key skills and qualifications needed to thrive as a Manager Annotation, and why are they important?

To thrive as a Manager Annotation, you need expertise in data annotation processes, team leadership, and quality assurance, often supported by a relevant degree and experience in data labeling or AI/ML projects. Familiarity with annotation tools (such as Labelbox, Supervisely, or AWS SageMaker Ground Truth), project management software, and sometimes certifications in project management or data science are valuable. Strong communication, problem-solving abilities, and attention to detail help ensure effective team coordination and high-quality data outputs. These skills are crucial for delivering accurate training data, meeting project deadlines, and supporting the success of machine learning initiatives.

What are Manager Annotation jobs?

Manager Annotation jobs involve overseeing teams responsible for labeling and annotating data, which is critical for training machine learning models. These managers coordinate workflows, ensure quality control, and facilitate communication between annotators and data scientists. They are responsible for setting guidelines, managing deadlines, and addressing any issues that arise during the annotation process. Manager Annotation roles often require a combination of leadership skills and an understanding of data annotation tools and processes.

What is the difference between Manager Annotation vs Data Annotator?

AspectManager AnnotationData Annotator
Required CredentialsHigh school diploma or equivalent; experience in data labeling; leadership skillsHigh school diploma or equivalent; attention to detail; basic computer skills
Work EnvironmentOffice or remote management setting overseeing annotation teamsRemote or on-site data labeling tasks
Employer & Industry UsageTech companies, AI firms, data service providersAI, machine learning, data processing companies

The main difference is that a Manager Annotation oversees annotation teams and manages projects, requiring leadership and management skills, while a Data Annotator performs the actual data labeling work, focusing on accuracy and attention to detail. Managers coordinate workflows, whereas Annotators execute labeling tasks.

What are some common challenges faced by a Manager Annotation and how can they be addressed?

A Manager Annotation often encounters challenges such as ensuring high-quality data labeling, managing tight project deadlines, and maintaining effective communication across diverse annotation teams. Balancing quality control with efficiency can be demanding, especially when working with large datasets or remote teams. To address these challenges, it is helpful to establish clear annotation guidelines, implement robust quality assurance processes, and foster open communication channels for feedback and support. Regular training and performance reviews also play a key role in maintaining team standards and project consistency.
What are the most commonly searched types of Annotation jobs in California? The most popular types of Annotation jobs in California are:
What are popular job titles related to Manager Annotation jobs in California? For Manager Annotation jobs in California, the most frequently searched job titles are:
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What cities in California are hiring for Manager Annotation jobs? Cities in California with the most Manager Annotation job openings:
Manager Annotation jobs near you
Infographic showing various Manager Annotation job openings in California as of July 2026, with employment types broken down into 1% Locum Tenens, 34% Full Time, 19% Part Time, 19% Contract, 25% Nights, and 2% Summer. Highlights an 34% Physical, and 66% Remote job distribution.
Data Annotation Lead

Data Annotation Lead

Physical Intelligence

San Francisco, CA • On-site

Full-time

Posted 8 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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