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Data Annotation Supervisor Jobs in California (NOW HIRING)

AIcadium

Sr. Research Data Scientist

San Diego, CA · On-site

$150K - $180K/yr

Own the full data lifecycle for visual tasks: dataset curation, annotation strategy, augmentation ... Solid grasp of deep learning fundamentals: supervised and self-supervised learning, representation ...

Aicadium

Sr. Research Data Scientist

San Diego, CA

Own the full data lifecycle for visual tasks: dataset curation, annotation strategy, augmentation ... Solid grasp of deep learning fundamentals: supervised and self-supervised learning, representation ...

Walmart

(USA) Staff, Data Scientist

Fremont, CA · On-site

Enhance data feedback/annotation workflows to improve efficiency of our labeling team * Demonstrate ... Experience in machine learning, supervised and unsupervised and deep learning. * Experience in ...

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Data Annotation Supervisor information

What is a data annotation supervisor?

Data Annotation Supervisors are professionals responsible for overseeing teams that label, tag, or annotate data used to train machine learning and artificial intelligence models. They ensure the accuracy, quality, and consistency of annotated data, manage workflow, and provide feedback or training to data annotators. Their role is crucial for maintaining data integrity, meeting project deadlines, and supporting the development of reliable AI systems. Data Annotation Supervisors often collaborate with data scientists and project managers to align annotation tasks with project goals.

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

To thrive as a Data Annotation Supervisor, you need expertise in data labeling processes, quality assurance, and team leadership, often supported by a bachelor’s degree in a relevant field. Familiarity with annotation tools, data management systems, and project tracking software is typically required. Strong communication, attention to detail, and problem-solving abilities are crucial soft skills for ensuring high-quality outcomes and effective team management. These skills enable supervisors to maintain data accuracy, meet project deadlines, and support the development of reliable machine learning models.

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

Data Annotation Supervisors often encounter challenges such as maintaining annotation quality across a diverse team, meeting tight project deadlines, and ensuring clear communication of guidelines. To manage these effectively, supervisors typically implement regular quality checks, provide ongoing training, and foster open communication channels for feedback and clarification. Leveraging annotation tools and establishing clear performance metrics also help in maintaining consistency and efficiency within the team.

What is the difference between Data Annotation Supervisor vs Data Labeling Specialist?

AspectData Annotation SupervisorData Labeling Specialist
CredentialsHigh school diploma or equivalent; experience in data annotationHigh school diploma or equivalent; training in labeling tools
Work EnvironmentSupervisory role overseeing teams in office or remote settingsHands-on labeling work, often in a collaborative environment
ResponsibilitiesManaging annotation teams, quality control, workflow coordinationPerforming data labeling tasks, following guidelines, ensuring accuracy

The Data Annotation Supervisor oversees and manages data annotation teams, focusing on quality and workflow, while Data Labeling Specialists perform the actual labeling tasks. Both roles require familiarity with annotation tools, but the supervisor has additional responsibilities in team management and quality assurance.

What cities in California are hiring for Data Annotation Supervisor jobs?

Cities in California with the most Data Annotation Supervisor job openings:

Infographic showing various Data Annotation Supervisor job openings in California as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 12% Part Time, 2% Temporary, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution.

Annotation Operations Manager

Redwood City, CA • On-site

DYNA Robotics Inc
See more jobs at DYNA Robotics Incexternal

Full-time

Re-posted 5 days ago

Job description

Dyna Robotics trains robots to do real world manipulation tasks, and every one of those behaviors is learned from precisely labeled robot episodes. Our Data Annotation team turns raw teleoperation footage into the labels our imitation learning models depend on, so throughput and quality here are a direct input to how fast the company ships.
Dyna Robotics has raised over $140M, backed by top investors including CRV, First Round Capital, Robostrategy, Salesforce Ventures, NVentures, Amazon, Samsung Next, and LG Technology Ventures. Our team brings together engineers and researchers from Google, Meta, Apple, Amazon, Cruise, Aurora, NVIDIA, along with academic roots at Stanford, Berkeley, MIT, UPenn, and beyond. We're positioned to redefine the landscape of robotic automation.
The Role
This role owns the operational layer of that work. You will run the operation from day one, covering process, reporting, and third party relationships, and step into people management progressively, starting with a small pod of labelers and growing into the full team as you prove yourself. Our Annotation Lead keeps the technical judgment calls: model calibration and promotion, ontology and failure taxonomy design, and cost and planning strategy. You are the person who makes the annotation engine run predictably every single day.
What You'll Do
Operations and reporting
  • Own the reporting rhythm. Run daily and weekly throughput reporting across all active datasets, covering episode totals, review stage progress, and ETAs, and distribute scorecards to the labeling team and stakeholders.
  • Keep the pipeline moving. Own the Encord pipeline end to end: create and configure projects, set up SOPs and ontologies, move datasets through annotate, review, and complete stages, handle grade splits, assignments, and resyncs, and keep dataset updates flowing as new footage lands.
  • Measure and unblock. Track and continuously improve operational metrics such as throughput per headcount, per dataset cycle times, and expected versus actual labeling time, and flag bottlenecks before they turn into blockers.
  • Automate the mechanical. Extend and maintain the automation and runbooks behind these tasks, like the scripted daily totals send, so manual toil shrinks over time.

People and team management
  • Start with a pod, then scale up. You will begin by leading a small pod of labelers, owning their day to day assignments and first line supervision. As that proves out, you will transition into managing the full annotation team, about 12 people today and growing, and formalize sub leads as it scales.
  • Handle approvals. Own timecard, payroll hours, and approval workflows for the team.
  • Grow the team. Run hiring for the annotation team end to end, including sourcing, interviewing, onboarding, and ramp.
  • Develop people. Own performance management: regular 1:1s, performance reviews, coaching, and quality feedback loops.

Third party and vendor management
  • Own the relationships. Be the primary point of contact for our annotation platform (Encord) and for external labeling vendors and partners.
  • Coordinate partners. Run external workstreams by provisioning access and invites, standing up projects and SOPs for partners, and managing scope and priorities with them.
  • Manage the handoffs. Own the scale and vendor upload runbook, and make sure data moves cleanly between our systems and external tooling.
  • Absorb the overhead. Represent annotation ops in vendor and external syncs so the rest of the team stays focused.

What You'll Bring
  • 3+ years running operations, program management, or team management, ideally in data annotation, data operations, ML data pipelines, BPO or vendor management, or a comparable high throughput environment.
  • Direct people management experience. You have supervised a team, run hiring, and handled performance and approvals.
  • Strong operational instincts. You build repeatable processes, track the right metrics, and are comfortable owning dashboards, scorecards, and reporting.
  • Comfort with annotation and labeling tooling such as Encord, plus enough technical fluency to work in spreadsheets, basic SQL or scripts, and pipeline tools without hand holding.
  • Vendor and third party management experience. You can be the accountable point of contact and keep external partners on scope and on schedule.
  • A high tolerance for fragmentation. You can hold roughly 20 small threads a week and still keep the operation predictable.
  • Clear, proactive communication. You surface risks early and keep stakeholders aligned.
Bonus points for
  • Exposure to robotics, computer vision, or imitation learning data.
  • Experience automating manual ops workflows with scripts or lightweight tooling.
  • Experience scaling an annotation team through a growth phase and standing up sub leads.

At Dyna Robotics, we build technology for the real world, which requires a team as diverse as the environments our robots inhabit. We are an equal opportunity employer committed to technical rigor and mutual respect.
Don't let a checklist stop you. Data shows that underrepresented groups often only apply if they meet 100% of the criteria. We value problem-solving and grit over keyword matching. If you're passionate about closing the loop between deployed robots and better models, we want to hear from you, even if you don't check every box.

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