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Data Annotation Program Manager Jobs in California

Abaka AI

Data Operations Engineer

Mountain View, CA · On-site

$136K - $163K/yr

... manage multiple requests and priorities simultaneously. Preferred : • Experience with multimodal datasets (text, image, video, audio, or 3D). • Familiarity with data annotation, labeling ...

Abaka AI

Data Operations Engineer

Mountain View, CA · On-site

$136K - $163K/yr

... manage multiple requests and priorities simultaneously. Preferred : • Experience with multimodal datasets (text, image, video, audio, or 3D). • Familiarity with data annotation, labeling ...

Abaka AI

Data Operations Engineer

Mountain View, CA · On-site

$136K - $163K/yr

... manage multiple requests and priorities simultaneously. Preferred : • Experience with multimodal datasets (text, image, video, audio, or 3D). • Familiarity with data annotation, labeling ...

Sieve

Data Operations Lead

San Francisco, CA · On-site

... manage a distributed human workforce for data annotation, curation, and quality review • Build and improve QA processes to ensure data output meets the standards required by frontier AI labs • ...

Meta

Product Data Operations Program Manager

Menlo Park, CA · On-site

$99K - $144K/yr

  • PTO

As a Program Manager on the PDO team, you are responsible for managing data labeling and annotation programs end-to-end. You will also be responsible for driving data labeling automation via LLMs ...

Abaka AI

Data Solutions Engineer

Mountain View, CA · On-site

$136K - $163K/yr

... Program Management, Analytics, Consulting, AI Operations, or related fields • Strong ... data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA ...

Abaka AI

Data Solutions Engineer

Mountain View, CA · On-site

$136K - $163K/yr

... Program Management, Analytics, Consulting, AI Operations, or related fields • Strong ... data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA ...

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Data Annotation Program Manager information

What are the key skills and qualifications needed to thrive as a data annotation program manager, and why are they important?

To thrive as a Data Annotation Program Manager, you need expertise in project management, data quality assessment, and a solid understanding of machine learning or data annotation processes, typically supported by a relevant degree. Familiarity with annotation platforms, workflow management tools, and data labeling software is essential, along with knowledge of quality assurance frameworks. Strong leadership, problem-solving abilities, and effective communication are crucial soft skills that help manage diverse teams and ensure stakeholder alignment. These skills are important to maintain high data quality, meet project deadlines, and drive successful AI model training initiatives.

How does a data annotation program manager coordinate with cross-functional teams to ensure project success?

A Data Annotation Program Manager regularly collaborates with engineering, data science, and quality assurance teams to align annotation guidelines, project timelines, and quality standards. They often facilitate meetings to clarify requirements, resolve ambiguities in data labeling, and provide feedback on annotation accuracy. This role serves as a bridge between technical teams and annotation staff, ensuring open communication and timely resolution of challenges, which is critical for delivering high-quality datasets essential for machine learning and AI projects.

What is a data annotation program manager?

Data Annotation Program Managers are professionals who oversee and coordinate data labeling projects, ensuring that data used for machine learning and artificial intelligence is accurately tagged and prepared. They manage teams of annotators, set project guidelines, monitor quality, and ensure deadlines are met. Their role is crucial for building high-quality datasets that enable reliable AI model training. Program Managers often collaborate with data scientists, engineers, and stakeholders to define requirements and improve annotation processes.

What are popular job titles related to Data Annotation Program Manager jobs in California?

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Data Annotation Program Manager jobs near you
Infographic showing various Data Annotation Program Manager job openings in California as of August 2026, with employment types broken down into 1% As Needed, 79% Full Time, 16% Part Time, 2% Temporary, and 2% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution.

Data Technical Product Manager

Success Matcher Recruitment

San Francisco, CA

$200K - $350K/yr

Full-time

Re-posted 23 days ago

Job description

About the Opportunity

Our client, a well-funded, early-stage robotics and AI company is seeking a highly technical and execution-oriented Data Technical Product Manager (TPM) to own the end-to-end data infrastructure powering next-generation machine learning systems.


This role sits at the intersection of machine learning, data engineering, operations, and product management. You will be responsible for building and scaling the data flywheel that transforms raw data collected from deployed robotic systems into high-quality training datasets used to improve AI performance.


The ideal candidate has experience managing large-scale data operations, working closely with ML teams, and translating ambiguous technical requirements into structured execution plans.

This is a unique opportunity to join a rapidly growing robotics company building cutting-edge autonomous systems while working directly alongside world-class engineers, researchers, and founders.

What You'll Do

Own the End-to-End Data Platform

  • Drive the roadmap for the central data platform.
  • Manage the complete data lifecycle from capture through ingestion, storage, labeling, curation, and delivery to ML training pipelines.
  • Partner with engineering teams to scale infrastructure supporting large-volume datasets.


Partner with Machine Learning Teams

  • Translate ML research requirements into actionable data collection and annotation specifications.
  • Define dataset requirements and collection strategies to support model development.
  • Ensure researchers have access to reliable, high-quality training data.


Define Data Quality Standards

  • Create QA frameworks, audit processes, and validation workflows.
  • Establish standards for data quality, coverage, consistency, and labeling accuracy.
  • Identify sensor drift, data degradation, and annotation issues before they impact training outcomes.

Manage Data Annotation Operations

  • Source and manage third-party labeling vendors.
  • Define vendor performance expectations and quality metrics.
  • Conduct audits and implement continuous improvement initiatives.


Build Data Discovery & Infrastructure Capabilities

  • Partner with infrastructure and platform engineers to improve:
    • Data ingestion
    • Cataloging
    • Search
    • Versioning
    • Dataset management


Design the Data Flywheel

  • Build systems that automatically surface edge cases and production failures.
  • Create workflows that capture and route valuable operational data back into future training cycles.
  • Improve data collection efficiency and model iteration speed.


Drive Metrics & Operational Excellence

  • Define and monitor critical metrics including:
    • Throughput
    • Label quality
    • Data coverage
    • Dataset freshness
    • Drift detection
    • Operational efficiency


Required Qualifications

  • 4+ years of experience in one or more of the following:
    • Technical Product Management
    • Data Engineering
    • Large-Scale Data Operations
  • Proven experience building and managing end-to-end data pipelines.
  • Experience supporting applied machine learning or AI systems.
  • Strong understanding of data quality management and governance.
  • Ability to operate effectively in highly ambiguous, fast-moving startup environments.
  • Experience coordinating across engineering, research, operations, and external vendors.


Preferred Qualifications

  • Experience with multimodal datasets including:
    • Video
    • Sensor data
    • Point clouds
    • Telemetry
  • Robotics, autonomous systems, computer vision, or industrial AI experience.
  • Familiarity with large-scale data annotation workflows.
  • Experience designing feedback loops that improve model performance over time.