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Russian Data Annotation Manager Jobs in California

YO AI Labs

Video Annotation Expert - Remote

San Jose, CA · Remote

$50 - $90/hr

The role involves accurate video labeling and structured data annotation to support AI and machine ... Strong time-management and organizational abilities. * Experience with AI, machine learning, or ...

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Russian Data Annotation Manager information

What is a Russian data annotation manager?

A Russian Data Annotation Manager oversees the process of labeling and annotating data in the Russian language for machine learning and AI projects. They manage teams of annotators, ensure data quality, and optimize workflows to meet project requirements. This role requires fluency in Russian, attention to detail, and experience with annotation tools. Additionally, they collaborate with engineers and linguists to refine annotation guidelines for accurate model training.

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

To thrive as a Russian Data Annotation Manager, you need fluency in Russian, experience with data annotation processes, and strong organizational abilities, often supported by a background in linguistics, computer science, or a related field. Familiarity with annotation platforms, data labeling tools, and project management software is commonly required. Leadership, attention to detail, and effective communication are key soft skills that help excel in managing diverse annotation teams. These skills are essential for ensuring high-quality data outputs and efficient project delivery in multilingual technology environments.

What are some common challenges faced by Russian data annotation managers, and how do they overcome them?

Russian Data Annotation Managers often encounter challenges related to maintaining consistency and accuracy across large, multilingual annotation teams, especially when dealing with nuanced language data. They address these issues by developing clear guidelines, conducting regular quality checks, and providing ongoing training to annotators. Collaboration with data scientists, project managers, and quality assurance personnel is also important to quickly resolve ambiguities and implement feedback. By fostering open communication and setting clear expectations, managers help ensure project standards are met and team members feel supported.

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Russian Data Annotation Manager jobs near you
Infographic showing various Russian Data Annotation Manager job openings in California as of September 2026, with employment types broken down into 72% Full Time, and 28% Contract. Highlights an 73% In-person, and 27% Remote job distribution.

Data Annotation Lead

San Francisco, CA • On-site

Physical Intelligence
Education Programs Administration • 1 - 10 employees
Learn more about Physical Intelligenceexternal

Full-time

Re-posted 16 days ago

Job description

Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future.
The role
We're looking for a Data Annotation Lead to own annotation operations and scale the team behind it. Annotation is core to how our models improve, and demand is growing fast. You will scale the annotation workforce from 100s to 1,000s while raising the quality bar - designing the org, the training pipeline, the quality system, and the metrics that let it scale efficiently.
You will own the people and the operation: throughput, quality, cost, and delivery across every annotation type.
In this role you will
  • Own annotation operations end-to-end: throughput, quality, cost, and on-time delivery across all annotation types.
  • Scale the annotation workforce from 100s to 1,000s: workforce planning, org design, and the hiring and onboarding funnel.
  • Build and lead a multi-layer management structure; hire, develop, and manage managers and team leads.
  • Scale throughput with autolabeling and model-based annotation: design human-in-the-loop workflows where models pre-label and annotators review, correct, and escalate, so output grows faster than headcount.
  • Stand up the training and certification pipeline that brings new annotators and teams to the quality bar quickly and consistently.
  • Define and continuously raise the quality bar: rubrics, calibration, audit/QA loops, and quality-adjusted productivity.
  • Establish operational metrics and reporting (presence, throughput, acceptance/rejection, rework) and drive week-over-week improvement.
  • Run capacity planning and prioritization against competing demand; allocate teams to the highest-impact work.
  • Manage performance at scale with clear standards, feedback, and a fair improvement/exit process.
  • Partner with product and engineering to define annotation tooling that unlocks throughput and quality.
  • Partner with research and project leads to translate annotation needs into clear instructions, rubrics, and SLAs.
  • Own the in-house vs. vendor mix and manage external partners where used.
  • Own the annotation operating budget and unit economics; improve cost-per-annotation while protecting quality.

What you'll bring
  • 7+ years leading scaled data or annotation operations, including teams in the 100s+.
  • 3+ years as a manager of managers.
  • Track record standing up 0→1 annotation programs.
  • Deep command of annotation best practices, operations, and strategy.
  • Experience integrating autolabeling and model-based annotation into human workflows; building human-in-the-loop pipelines that raise throughput without sacrificing quality.
  • Fluency with operational and quality metrics; data-driven management of large workforces.
  • Strong cross-functional partnership with product, engineering, and research/ML.
  • Clear written and verbal communication; able to set and hold standards across a large, distributed team.
  • Working understanding of ML and why annotation quality drives model performance.

Nice to have
  • Experience in robotics, autonomous vehicles, or frontier-AI data pipelines.
  • Experience managing distributed/global and/or vendor workforces.
  • Built annotation tooling or partnered tightly with a tooling team.
  • Experience training or fine-tuning autolabeling models, or partnering closely with the ML teams that do.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Apply