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Data Annotation Project Manager Jobs in Oakland, CA

Rivian

Sr. Software Analyst, Data, Autonomy

Palo Alto, CA · On-site

$132.10 - $165.10/hr

Experience analyzing LiDAR point clouds, video, and image annotation data. * Ability to manage multiple projects, prioritize effectively, and deliver under tight timelines. * Strong written and ...

Encord

Strategic Projects Lead

San Francisco, CA · On-site

The role As a Strategic Projects Lead, you'll fully own and optimize the data annotation and ... data annotation platforms or quality management frameworks; experience coaching or managing ...

Xdof

Technical Program Manager

San Francisco, CA · On-site

$152K - $196K/yr

Ensure data annotation projects meet client requirements on accuracy and SLA, while satisfying ... Who You Are * Background: 2-5 years of project management experience; background in AI data ...

Abaka AI

Project Manager

Palo Alto, CA · On-site

They are seeking a Project Manager to work closely with clients to confirm project requirements ... data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA ...

Abaka AI

Project Manager

Mountain View, CA · On-site

They are seeking a Project Manager who will ensure the successful delivery of data projects for ... data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA ...

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Data Annotation Project Manager information

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$19

$66

$92

How much do data annotation project manager jobs pay per hour?

As of Aug 29, 2026, the average hourly pay for data annotation project manager in Oakland, CA is $66.05, according to ZipRecruiter salary data. Most workers in this role earn between $57.16 and $77.31 per hour, depending on experience, location, and employer.

What is a data annotation project manager?

A Data Annotation Project Manager is responsible for overseeing projects that involve labeling and categorizing data, such as images, text, or audio, to train machine learning models. They coordinate teams of annotators, manage project timelines, and ensure the quality and accuracy of the annotated data. This role often acts as a bridge between data scientists, clients, and annotation teams, ensuring project requirements are met efficiently and effectively.

What are the key skills and qualifications needed to thrive as a data annotation project manager?

To thrive as a Data Annotation Project Manager, you need strong project management skills, a solid understanding of data annotation processes, and experience with quality assurance, often supported by a degree in a relevant field. Familiarity with annotation tools (like Labelbox or Supervisely), workflow management platforms, and sometimes agile or PMP certification is highly beneficial. Exceptional communication, attention to detail, and leadership abilities help you effectively coordinate teams and ensure project deliverables meet quality standards. These skills are essential for managing complex annotation projects efficiently, maintaining data integrity, and supporting successful machine learning outcomes.

What are some common challenges faced by data annotation project managers, and how can they be managed effectively?

One of the primary challenges Data Annotation Project Managers face is ensuring high-quality, consistent labeling across large and sometimes distributed annotation teams. Managing tight deadlines while maintaining annotation accuracy requires effective training, clear guidelines, and regular quality checks. Additionally, balancing communication between data scientists, clients, and annotators is crucial to align expectations and resolve ambiguities quickly. Successful managers often implement robust feedback loops, leverage annotation tools with built-in quality control features, and foster an open environment for continuous improvement.

What is the difference between Data Annotation Project Manager vs Data Labeling Specialist?

AspectData Annotation Project ManagerData Labeling Specialist
CredentialsTypically requires project management experience, certifications in data management or related fieldsOften requires basic technical skills, familiarity with labeling tools, sometimes certifications in data annotation
Work EnvironmentOversees teams, manages projects, coordinates workflows in office or remote settingsPerforms labeling tasks, often in a remote or on-site environment, focused on data tagging
Employer & Industry UsageUsed by tech companies, AI firms, and data service providers for managing annotation projectsEmployed within similar industries, focusing on executing labeling tasks under supervision

The main difference is that the Data Annotation Project Manager oversees and coordinates annotation projects, ensuring quality and deadlines, while the Data Labeling Specialist focuses on executing the labeling tasks themselves. Both roles are essential in the data annotation process but differ in responsibilities and scope.

What are popular job titles related to Data Annotation Project Manager jobs in Oakland, CA?

For Data Annotation Project Manager jobs in Oakland, CA, the most frequently searched job titles are:

What job categories do people searching Data Annotation Project Manager jobs in Oakland, CA look for?

The top searched job categories for Data Annotation Project Manager jobs in Oakland, CA are:

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Cities near Oakland, CA with the most Data Annotation Project Manager job openings:

Data Annotation Project Manager jobs near you

Human Data Operations Strategist

Encord

San Francisco, CA • On-site

Full-time

This job post has expired 4 days ago. Applications are no longer accepted.

Job description

Job Summary:
Encord is a leading company in AI data management, supporting over 300 AI teams in optimizing their data workflows. The Human Data Operations Strategist will manage data annotation projects, ensuring high-quality data is available for AI models while collaborating with various teams and clients to refine processes and implement effective workflows.
Responsibilities:
• Oversee data annotation projects, translating complex AI and machine learning requirements into clear workflows and instructions for data annotation teams
• Ensure the highest standards of data quality by designing and refining annotation processes, auditing results, and implementing feedback loops
• Act as a trusted advisor to clients, helping them design and implement the best data annotation workflow for their human annotation process
• Provide guidance and feedback to the annotation team, ensuring team members are equipped with the context and skills needed to perform high-quality work aligned with project requirements and best practices
• Work closely with product and engineering teams to drive improvements in AI training data processes, tools, and methodologies
Qualifications:
Required:
• 3–7 years of professional experience, with a strong preference for backgrounds in top-tier strategy consulting and/or operations or data roles at leading AI or technology companies
• Proven ability to own complex, multi-stakeholder workflows end-to-end — from scoping and planning through execution, quality assurance, and iteration
• Working proficiency in Python or SQL, with the ability to query data, automate workflows, or audit annotation outputs; broader familiarity with relational databases or data annotation tooling equally valued
• Experience designing or optimising data operations processes with a strong eye for quality, consistency, and scalability — ideally in a context involving human-in-the-loop workflows or structured labelling tasks
• Demonstrated ability to engage effectively with both technical stakeholders (ML engineers, data scientists) and non-technical clients, translating requirements clearly in both directions
• A sharp, execution-oriented operator with a consulting or AI company pedigree — you bring structured thinking, strong project management instincts, and a bias for getting things done
• Analytically rigorous and comfortable with ambiguity — you break down complex operational challenges from first principles and build clear, actionable plans to solve them
• Technically fluent enough to get hands-on with data — whether that's querying a database, auditing annotation outputs, or automating a workflow in Python
• Passionate about AI and machine learning, with genuine curiosity about how data quality and operations underpin model performance
• A natural communicator who can translate fluidly between ML engineers and non-technical clients, keeping complex multi-stakeholder projects on track
• Entrepreneurial and collaborative — you thrive in fast-paced environments and take ownership without waiting to be told what to do
Preferred:
• Bonus: hands-on experience with computer vision, generative AI, or multimodal data workflows; prior exposure to data annotation platforms or quality management frameworks; experience coaching or managing operational teams
Company:
Encord is the AI native data Infrastructure company. We help teams curate, manage, and annotate the data needed to train and run AI Founded in 2021, the company is headquartered in San Francisco, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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