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Data Annotation Manager Jobs in Sunnyvale, CA (NOW HIRING)

As a Data Science Manager, you will act as a pivotal technical leader to bridge the gap between ... Annotation Rigor: Drive a comprehensive and scalable data annotation strategy that prioritizes ...

Data & Annotation: Hands-on experience designing and managing data curation strategies and human-in ... the-loop annotation processes. Data Analysis: Strong analytical skills with the ability to dive ...

... data-annotation pipelines and machine-led training data solutions at foundation-model scale . We partner closely across AI/ML engineers , Product Operations , Product Management , Data Science , and ...

Demonstrated experience managing dataset generation or annotation for machine learning model evaluation and/or training * Familiarity with ML tools and data workflows (e.g., HuggingFace, LangChain ...

Data Annotation * Problem-Solving * Independent Research * Attention to Detail Preferred ... management, or strategic finance. * Experience preparing investment memos, valuation analyses ...

Data Annotation * Problem-Solving * Independent Research * Attention to Detail Preferred ... management, or strategic finance. * Experience preparing investment memos, valuation analyses ...

Data Annotation * Problem-Solving * Independent Research * Attention to Detail Preferred ... management, or strategic finance. * Experience preparing investment memos, valuation analyses ...

Helix AI Engineer, Data Infrastructure

San Jose, CA · On-site

$126K - $165K/yr

... building data annotation and dataset management tools. Company : Figure is an AI robotics company that develops autonomous general-purpose humanoid robots. Founded in 2022, the company is ...

Helix AI Engineer, Data Infrastructure

San Jose, CA · On-site

$126K - $165K/yr

... building data annotation and dataset management tools. Company : Figure is an AI robotics company that develops autonomous general-purpose humanoid robots. Founded in 2022, the company is ...

Helix AI Engineer, Data Infrastructure

San Jose, CA · On-site

$126K - $165K/yr

... building data annotation and dataset management tools. Company : Figure is an AI robotics company that develops autonomous general-purpose humanoid robots. Founded in 2022, the company is ...

Senior Data Engineer / Data Curator

San Jose, CA · On-site

$124K - $168K/yr

... managing large datasets. • Experience with data annotation tools and platforms for manual or semi-automated labeling. • Experience with NLP data formats, such as JSONL, text, or embeddings, and ...

Experience scaling large data operations, managing complex annotation workflows, and working ... directly with external data vendors. Technical Stack: Familiarity with Python, SQL, and ML ...

Experience building data annotation and dataset management tools. The US base salary range for this full-time position is between $150,000 - $400,000 annually. The pay offered for this position may ...

Showing results 41-60

Data Annotation Manager information

See Sunnyvale, CA salary details

$36.9K

$115.8K

$205K

How much do data annotation manager jobs pay per year?

As of Sep 2, 2026, the average yearly pay for data annotation manager in Sunnyvale, CA is $115,785.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,700.00 and $149,600.00 per year, depending on experience, location, and employer.

What does a data annotation manager do?

A Data Annotation Manager oversees the process of labeling and categorizing data used to train machine learning models. They manage teams of annotators, ensure data quality, develop annotation guidelines, and coordinate with data scientists to meet project requirements. Their role is critical in maintaining high standards of accuracy and efficiency, as well as ensuring that datasets are properly prepared for AI and machine learning applications.

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

To thrive as a Data Annotation Manager, you need expertise in data labeling processes, quality control, and a solid understanding of machine learning concepts, usually backed by a degree in computer science or a related field. Proficiency with annotation tools such as Labelbox, Supervisely, or CVAT, as well as experience with project management systems, is commonly required. Exceptional leadership, attention to detail, and strong communication skills help manage teams and ensure high annotation accuracy. These skills are critical for delivering reliable labeled datasets, which are essential for building effective AI and machine learning models.

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

Data Annotation Managers often encounter challenges such as maintaining high annotation quality across large and diverse datasets, managing a distributed team of annotators, and meeting tight project deadlines. To address these, it's important to implement robust quality assurance processes, provide ongoing training for annotators, and establish clear communication channels. Leveraging annotation tools with built-in validation features can also help ensure consistency and accuracy. Building a positive and collaborative team environment further contributes to better outcomes and workflow efficiency.

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

AspectData Annotation ManagerData Labeling Specialist
CredentialsBachelor's degree in related field, experience in data managementHigh school diploma or equivalent, training in labeling tools
Work EnvironmentTeam management, project oversight, collaboration with data scientistsHands-on labeling work, using annotation tools, focused on data tagging
Industry UsageUsed in AI/ML projects for overseeing annotation teamsPerforms the actual data labeling tasks in machine learning workflows

The Data Annotation Manager oversees the entire annotation process, managing teams and ensuring quality, while the Data Labeling Specialist focuses on executing labeling tasks. Both roles are essential in AI/ML data preparation but differ in responsibilities and scope.

What are the most commonly searched types of Data Annotation jobs in Sunnyvale, CA?

The most popular types of Data Annotation jobs in Sunnyvale, CA are:

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

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

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

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

What cities near Sunnyvale, CA are hiring for Data Annotation Manager jobs?

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

Infographic showing various Data Annotation Manager job openings in Sunnyvale, CA as of August 2026, with employment types broken down into 82% Full Time, 9% Part Time, and 9% Contract. Highlights an 73% In-person, 18% Hybrid, and 9% Remote job distribution, with an average salary of $115,785 per year, or $55.7 per hour.

Data Science Manager

Tapestry

Mountain View, CA • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 7 days ago


Tapestry Inc. rating

8.2

Company rating: 8.2 out of 10

Based on 36 frontline employees who took The Breakroom Quiz

1st of 104 rated fashion retailers


Job description

About Tapestry

Tapestry is a group within Google working to build the AI-powered electric grid. We are tackling one of the world's most important infrastructure challenges: helping the energy system become more visible, understandable, reliable, affordable, abundant, and clean.

Originally born at X, Alphabet's moonshot factory, Tapestry brings together experts in energy, AI, software engineering, and products to build tools that help the electricity ecosystem plan smarter, move faster, and operate more efficiently.

This is a global effort. Tapestry supports partners across the U.S., U.K., Chile, New Zealand, Australia, and Brazil as they work toward a cleaner, more resilient energy future.

Joining Tapestry means doing high-impact work with a multidisciplinary team tackling a problem that matters at global scale. Learn more about our team and our mission here.

About the role:

At Tapestry, data drives all our decision-making. Data Scientists work across the organization to help shape our business and technical strategies by processing, analyzing, and interpreting massive datasets. They lead our metrics assessment, analyze massive datasets and derive early insights, and partner with cross functional teams on the right datasets for maximum downstream impact. As a Data Science Manager, you will act as a pivotal technical leader to bridge the gap between complex business questions and advanced technical execution. You will build, mentor, and lead a high-performing team of data scientists to deliver operational excellence, accelerate product advancement, and drive business value.

In this role, you will deeply immerse yourself with the team of data scientists in data collection and analysis, develop compelling, synthesized recommendations for senior leadership, and be involved  to help drive implementation. Ultimately, your team's solutions will fundamentally improve electric grid visibility and resilience.

How you will contribute to the team...

1. Team Leadership and Strategic Delivery

  • People Management: Recruit, mentor, and lead a world-class team of data scientists. Cultivate talent through active technical mentorship and clear career development paths.
  • Cross-Functional Alignment: Collaborate with engineering, product, power system experts, and external partners to translate high-level business goals into rigorous data science roadmaps.
  • Executive Communication: Persuasively communicate your team's findings and strategic recommendations to senior executives and cross functional teams, tracking the long-term business impact of the solutions.

2. Data Integrity and Curation Strategy at Scale

  • Pipeline Oversight: Guide the team in discovering, investigating, and deriving insights from large and complex input datasets, both current and potential, from partners and other sources.
  • Gatekeeping Metrics: Oversee the definition of problem framing, test datasets, and core business, product and performance metrics that machine learning models will aim to optimize for.
  • Multi-Stage Quality Control: Ensure data integrity across the pipeline by establishing frameworks to assess intermediate datasets and metrics within multi-stage machine learning processes.
  • Annotation Rigor: Drive a comprehensive and scalable data annotation strategy that prioritizes quality through statistical rigor, ensuring data reliability for all downstream modeling.

3. Problem Definition and Advanced Analytics

  • Grid Visibility and Innovation: Lead the proactive exploration of new problem spaces to fundamentally improve electric grid visibility and resilience.
  • Experimentation Frameworks: Standardize how the team designs, executes, and analyzes A/B tests and other experiments to validate hypotheses and measure product impact.
  • Engineering Best Practices: Champion modern data science workflows, including the application of GenAI techniques for data analysis, ensuring the team follows robust engineering best practices.
What you should have...
  • PhD or Master's in a quantitative field and 8+ years of tech or energy industry work experience as a statistician, quantitative analyst, or data scientist. 
  • 5+ years of experience directly managing or leading high-performing data science and analytics teams, with a proven track record of delivering production-grade data solutions.
  • A proven track record of identifying where data science can add unique value during early product development, alongside a strong ability to influence other teams to collaborate on critical data science work.
  • Advanced skills in experimental design, including the ability to architect, guide, and validate robust A/B testing methodologies and statistical experiments in ambiguous environments.
  • Experience in Python, SQL, R, Pandas, Scikit-Learn, other ML frameworks as appropriate.
  • Experience with electric power grid data, and physics based understanding of electrical networks and utility data. Ability to bridge the gap between power systems and machine learning.
  • Experience in multivariate analysis, stochastic models, and sampling methods. Able to select the right statistical tool to solve for bias, variance, and data drift.
  • Applied experience with building comprehensive machine learning model evaluation tooling and processes on large datasets.
  • Proven ability to "zoom out" from complex technical details to build a cohesive product strategy, and "zoom in" to unblock technical hurdles.
  • Demonstrate strong collaboration with software engineering and cross functional teams to build ML-powered systems ready for production.
  • Exceptional storytelling abilities, with a knack for turning complex data pipelines and model metrics into clear business value for non-technical stakeholders.
Would be great to have...
  • Ability to thrive in ambiguity, set own goals and effectively delivering to them in a very fast-changing environment
  • Attention to detail, project management, and organizational skills
  • Fast learner with capacity to learn about a wide-spread of different technologies and industries
  • Passion for the energy and climate space
  • Track record of delivering scalable solutions to complex software problems
  • Experience in startup or high-growth environments

Tapestry Values:

  • Take charge: We take initiative and own outcomes that move the mission forward.
  • Transform with purpose: We build solutions that solve real problems and create meaningful impact.
  • Be a Tapestry, not a thread: We collaborate across diverse skills and perspectives to achieve more than we can individually.
  • Always fine-tune: We stay curious, seek feedback, and refine our understanding as we learn.
  • Stay grounded: We listen openly, value different perspectives, and stay focused on what matters most.

What we offer:

A culture that supports growth, ownership, and meaningful impact, along with...

  • Competitive salary and equity
  • Medical, dental, and vision coverage
  • Generous PTO and flexible hybrid work model
  • 401(k) with employer contribution
  • Professional development
  • The ability to work on important real-world problems within an Alphabet-backed environment

The US base salary range for this full-time position is $207,000 - $304,000 + bonus + equity + benefits. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.


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