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Full Time Annotation Specialist Jobs (NOW HIRING)

Hark

Data Collection

San Jose, CA

$150K - $250K/yr

Familiarity with data annotation platforms or labeling pipelines. * Experience with synthetic data ... Compensation The US base salary range for this full-time position is between $150,000 - $250,000 ...

Hark

Data Collection

San Jose, CA · On-site

$150K - $250K/yr

Familiarity with data annotation platforms or labeling pipelines. * Experience with synthetic data ... Compensation The US base salary range for this full-time position is between $150,000 - $250,000 ...

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Full Time Annotation Specialist information

What is the difference between Full Time Annotation Specialist vs Data Labeler?

AspectFull Time Annotation SpecialistData Labeler
CredentialsHigh school diploma or equivalent; some roles prefer certifications in data annotationTypically high school diploma; minimal formal certifications
Work EnvironmentOffice or remote; collaborative teams; specialized toolsRemote or on-site; basic software tools; repetitive tasks
Industry UsageAI, machine learning, autonomous vehicles, healthcareAI, machine learning, data processing
Search & Comparison IntentUnderstanding roles, responsibilities, and career pathBasic data labeling tasks, entry-level position

The main difference is that a Full Time Annotation Specialist typically has more responsibilities, may require some certifications, and works in a more collaborative environment focused on complex annotation tasks. Data Labelers usually perform simpler, repetitive labeling tasks, often with minimal qualifications. Both roles are essential in AI development but differ in scope and complexity.

What is a full time annotation specialist?

Full Time Annotation Specialists are professionals who label, categorize, and tag data such as images, audio, video, or text to help train machine learning and artificial intelligence systems. Their work is crucial for ensuring that AI models receive high-quality, accurate data during the training phase. These specialists typically follow detailed guidelines to accurately identify and annotate relevant features in large datasets. They may work in various industries, including technology, healthcare, automotive, and more, depending on the application of the AI system.

What are the key skills and qualifications needed to thrive as a full time annotation specialist?

To thrive as a Full Time Annotation Specialist, you need strong attention to detail, analytical thinking, and a high school diploma or equivalent, with some roles requiring domain-specific knowledge. Familiarity with annotation platforms, data labeling tools, and basic office software is typically required, and knowledge of machine learning concepts can be beneficial. Excellent communication, time management, and the ability to work independently or collaboratively are standout soft skills. These competencies ensure high-quality, accurate data labeling that directly impacts the effectiveness of AI and machine learning models.

What are the main challenges full time annotation specialists face when working with large datasets?

Full Time Annotation Specialists often encounter challenges related to maintaining consistency and accuracy across large and complex datasets. The repetitive nature of the work can lead to fatigue, increasing the risk of errors, so attention to detail and regular breaks are important. Additionally, they may need to adapt quickly to shifting project guidelines and collaborate closely with data scientists or engineers to clarify ambiguous cases. Staying up to date with annotation tools and best practices also helps ensure high-quality results.
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Infographic showing various Full Time Annotation Specialist job openings in the United States as of July 2026, with employment types broken down into 14% Locum Tenens, 18% Full Time, 16% Part Time, 18% Contract, 32% Nights, and 2% Summer. Highlights an 34% Physical, and 66% Remote job distribution.

Policy & Quality Specialist- ML Perception Data

Waymo

Mountain View, CA • On-site

$152K - $160K/yr

Full-time

Re-posted yesterday

Job description

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver-The World's Most Experienced Driver™-to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo's fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.
As an L4 Policy & Quality Specialist, you will serve as the Mountain View operational backbone of the Labeling Policy Program. You will play a key role in accelerating MTV Perception Engineering velocity by translating complex machine learning data requirements into consistent, high-quality, and scalable labeling policies. Operating in the same time zone as our core engineering partners, you will drive rapid policy iteration, create critical golden datasets to enable fast labeling queue setup. This is an execution-focused role for a technical, detail-oriented specialist who thrives on driving clarity, alignment, and operational excellence in labeling workflows.
You will:
  • Translate requirements to policies: Collaborate directly with MTV-based Perception Engineers/ ML model owners to understand their specific data goals, translate their ambiguous machine learning requirements into precise labeling instructions, and publish clear, actionable labeling policies (with support from the HYD Policy Specialist and vendor partners).
  • Drive queue readiness & golden datasets: Speed up the initial labeling queue setup process by executing rapid policy iterations and hand-crafting golden datasets (small-scale baseline datasets of 10s of examples) to establish quality baselines before launching full-scale operations.
  • Direct vendor teams: Provide technical guidance and operational direction to vendor labeling experts to enable rapid policy setup and ensure that the ~10 active labeling queues under your purview run smoothly and meet safety and performance objectives.
  • Address edge cases & regional nuances: Provide critical, detailed inputs on long-tail edge cases and coordinate with regional country specialists to ensure country-specific driving rules and local nuances are accurately captured and validated, ahead of Waymo's deployment in these new countries.
  • Enable quality and process improvements: Monitor labeling pipelines, conduct targeted technical analyses to identify data quality trends, and build/maintain automated data analysis tools to proactively identify improvements in the broader labeling workflow.
  • Facilitate cross-functional knowledge sharing: Act as the primary technical interface between requesters and operations, ensuring on-ground dissipation of policies, managing policy amendments, and resolving complex escalations from requestors or vendor teams.

You have:
  • 4-5+ years of experience in data analysis, operations, or program management with a focus on machine learning data annotation, taxonomy design, or human-in-the-loop workflows.
  • Operational project management: Demonstrated ability to work independently on operational workflows and successfully project manage small sub-working groups or vendor squads.
  • Core ML data lifecycle understanding: Practical knowledge of dataset curation, labeling pipelines, data quality control metrics, and baseline model evaluation concepts.
  • Analytical aptitude: Experience conducting technical data analyses using pre-established tools (or building simple automation scripts) to diagnose pipeline issues, track vendor quality, and generate actionable insights.
  • Adaptable & detail-oriented: Comfort working within a dynamic environment, translating vague technical needs into clear documentation, and maintaining a high standard of attention to detail.

We prefer:
  • Experience with scripting languages (e.g., Python, SQL) or basic automation techniques to parse high volumes of critical data.
  • Experience collaborating with software engineering stakeholders to gather structured requirements and explain technical operational policies.
  • Prior experience working across multiple geographic locations and managing vendor-hosted operations.
  • Familiarity with prompt engineering and evaluation of model outputs (AI-generated workflows) is a plus.

The expected base salary range for this full-time position is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Waymo employees are also eligible to participate in Waymo's discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.
Salary Range
$152,000-$160,000 USD

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