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Data Annotation Project Manager Jobs in Mountain View, CA

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 ...

YO AI Labs

AI Data Science Expert - Remote

San Jose, CA · Remote

$100 - $200/hr

Collaborate remotely with project teams to improve AI models and workflows. Required Skills ... Data Annotation * Data Interpretation * Fact Checking * Independent Research * Problem-Solving

campus4tech

Technical Program Manager III

Mountain View, CA · On-site

$152K - $197K/yr

... project management, or operations in data-centric or AI/ML environments. * Strong understanding of ML development workflows, data pipelines, and annotation lifecycle. * Experience managing large ...

Maxinsights

Technical Project Manager

Santa Clara, CA · On-site

$120 - $170/hr

Own the full lifecycle of complex robotics data collection and annotation projects--from initial ... Ensure dataset production meets strict client SLAs and accuracy standards while managing internal ...

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All JobsData Annotation Project JobsData Annotation Project Manager JobsData Annotation Project Manager Jobs in Mountain View, CA

Data Annotation Project Manager information

See Mountain View, CA salary details

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

As of Sep 4, 2026, the average hourly pay for data annotation project manager in Mountain View, CA is $67.84, according to ZipRecruiter salary data. Most workers in this role earn between $58.70 and $79.42 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 Mountain View, CA?

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

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

What cities near Mountain View, CA are hiring for Data Annotation Project Manager jobs?

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

Data Annotation Project Manager jobs near you
Infographic showing various Data Annotation Project Manager job openings in Mountain View, CA as of August 2026, with employment types broken down into 79% Full Time, 9% Part Time, and 12% Contract. Highlights an 76% In-person, 4% Hybrid, and 20% Remote job distribution, with an average salary of $141,110 per year, or $67.8 per hour.

Data Engine & Annotation Systems Engineer

Simbe Robotics

San Francisco, CA

$120K - $155K/yr

Full-time

Re-posted 11 hours ago

Job description

Simbe is building the AI powered operating system for physical retail. Our autonomous robots and multimodal computer vision platform turn complex, constantly changing stores into accurate, actionable intelligence for leading retailers around the world. Simbe combines robotics, computer vision, machine learning, data infrastructure, and customer focused product design to help retailers improve shelf availability, price and promo execution, inventory accuracy, and store team productivity.

Simbe is looking for a Data Engine & Annotation Systems Engineer to own the systems, workflows, and tooling that power high quality training data for our computer vision models. This role goes beyond annotation coordination. You will help build the data engine behind Simbe's AI platform: model assisted labeling, data quality checks, annotation guidelines, error mining, dataset versioning, active learning, and evaluation workflows that improve model performance and accelerate customer value.

Why This Role Is High Impact
  • You will help create the feedback loop that makes Simbe's AI systems better every week.
  • You will improve the quality, speed, and reliability of data used to train and evaluate production models.
  • You will work across human annotation, automation, model outputs, QA, and customer impact.
Responsibilities
  • Own annotation systems and workflows. Oversee and improve image and video annotation workflows, including task setup, guideline creation, quality control, edge case handling, and throughput monitoring.
  • Build data engine tooling. Develop Python, web, and automation tools that make it easier to request annotations, review results, clean data, export datasets, and evaluate model performance.
  • Improve data quality. Design checks that identify inconsistent labels, oversized or undersized boxes, missing annotations, duplicate data, class imbalance, and other issues that can degrade model performance.
  • Integrate model assisted workflows. Evaluate and integrate auto annotation, pre labeling, active learning, hard case mining, and model in the loop review systems to improve annotation efficiency.
  • Support dataset versioning and evaluation. Partner with CV engineers to maintain reliable datasets, evaluation splits, benchmark views, and release readiness workflows.
  • Coordinate across teams. Work with annotation teams, Computer Vision, Product, Customer Success, and Data teams to make sure annotation systems support current customer priorities and future product needs.
  • Measure and improve performance. Track annotation quality, turnaround time, capacity, cost, rework rates, and model impact to improve the operating system for data creation.
Required Qualifications
  • 3+ years of experience in software engineering, data tooling, ML data operations, annotation systems, data QA, or related technical work.
  • Strong Python skills, including experience building scripts, data workflows, APIs, or internal tools.
  • Comfort with Bash, Linux, Git, and production debugging workflows.
  • Experience with web frontend or full stack development for internal tools.
  • Strong communication skills and ability to create clear annotation guidelines, documentation, and process improvements.
  • High attention to detail and a strong understanding of how data quality affects model quality.
  • Ability to coordinate across technical and operational teams while still writing code and improving systems.
Bonus Qualifications
  • Experience with CVAT, FiftyOne, Labelbox, Scale, Roboflow, Supervisely, or similar annotation and dataset tools.
  • Experience with image, video, robotics, retail, autonomous vehicle, industrial inspection, or sensor data annotation.
  • Experience with active learning, auto labeling, synthetic data, evaluation dashboards, or dataset versioning.
  • Experience with object detection, segmentation, OCR, barcode localization, or other computer vision workflows.
  • Experience managing external annotation vendors or distributed annotation teams.
$120,000 - $155,000 a year

The base salary offered is based on market location and may vary depending on individualized factors for job candidates, including job related knowledge, skills, experience, and other objective business considerations. Subject to those same considerations, the total compensation package for this position may also include equity compensation, in addition to a full range of medical, financial, and other benefits. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.

Simbe Values: R. E. T. A. I. L.

  • Result Driven - We are customer centric and results driven. We strive to create immense value for our team, partners, customers, and investors.

  • Empathetic - We are sensitive and mindful. We support each other in challenging times, both professionally and personally.

  • Transparent - We value open communication internally, and with our partners and customers. We are receptive to feedback.

  • Agile - We are eager to learn and adapt quickly to changes and customer needs.

  • Innovative - We are bold and innovative, with an intense focus on product design, user experience, and customer value.

  • Leaders - We strive for excellence. We are accountable, the best at what we do, and leaders in our field.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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