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Manager Annotation Jobs in California (NOW HIRING)

Human Data Solutions Engineer

San Francisco, CA · On-site

$134K - $162K/yr

Manage end-to-end delivery of small-scale annotation POCs - translating complex AI requirements into clear instructions for annotation specialists, auditing outputs, and iterating on quality until ...

Lead Technical Product Manager - Data

San Carlos, CA · On-site

$201K - $233K/yr

The Lead Technical Product Manager will own the data strategy for the World Model Lab, ensuring ... annotation quality bar and build the labeling processes and metrics that make every dataset ...

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 ...

Senior Bioimage Scientist

San Diego, CA · On-site

$142K - $156K/yr

Provide cross-functional technical leadership and mentoring in bioimage analysis, slide management and annotation, and TME characterization. * Guide operational deployment of workflows and analytical ...

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Showing results 1-20

Manager Annotation information

What are the key skills and qualifications needed to thrive as a Manager Annotation, and why are they important?

To thrive as a Manager Annotation, you need expertise in data annotation processes, team leadership, and quality assurance, often supported by a relevant degree and experience in data labeling or AI/ML projects. Familiarity with annotation tools (such as Labelbox, Supervisely, or AWS SageMaker Ground Truth), project management software, and sometimes certifications in project management or data science are valuable. Strong communication, problem-solving abilities, and attention to detail help ensure effective team coordination and high-quality data outputs. These skills are crucial for delivering accurate training data, meeting project deadlines, and supporting the success of machine learning initiatives.

What are Manager Annotation jobs?

Manager Annotation jobs involve overseeing teams responsible for labeling and annotating data, which is critical for training machine learning models. These managers coordinate workflows, ensure quality control, and facilitate communication between annotators and data scientists. They are responsible for setting guidelines, managing deadlines, and addressing any issues that arise during the annotation process. Manager Annotation roles often require a combination of leadership skills and an understanding of data annotation tools and processes.

What is the difference between Manager Annotation vs Data Annotator?

AspectManager AnnotationData Annotator
Required CredentialsHigh school diploma or equivalent; experience in data labeling; leadership skillsHigh school diploma or equivalent; attention to detail; basic computer skills
Work EnvironmentOffice or remote management setting overseeing annotation teamsRemote or on-site data labeling tasks
Employer & Industry UsageTech companies, AI firms, data service providersAI, machine learning, data processing companies

The main difference is that a Manager Annotation oversees annotation teams and manages projects, requiring leadership and management skills, while a Data Annotator performs the actual data labeling work, focusing on accuracy and attention to detail. Managers coordinate workflows, whereas Annotators execute labeling tasks.

What are some common challenges faced by a Manager Annotation and how can they be addressed?

A Manager Annotation often encounters challenges such as ensuring high-quality data labeling, managing tight project deadlines, and maintaining effective communication across diverse annotation teams. Balancing quality control with efficiency can be demanding, especially when working with large datasets or remote teams. To address these challenges, it is helpful to establish clear annotation guidelines, implement robust quality assurance processes, and foster open communication channels for feedback and support. Regular training and performance reviews also play a key role in maintaining team standards and project consistency.
What are the most commonly searched types of Annotation jobs in California? The most popular types of Annotation jobs in California are:
What cities in California are hiring for Manager Annotation jobs? Cities in California with the most Manager Annotation job openings:
Infographic showing various Manager Annotation job openings in California as of July 2026, with employment types broken down into 1% Locum Tenens, 34% Full Time, 19% Part Time, 19% Contract, 25% Nights, and 2% Summer. Highlights an 34% Physical, and 66% Remote job distribution.

Human Data Solutions Engineer

Encord

San Francisco, CA • On-site

$134K - $162K/yr

Full-time

Medical, Dental, Vision, PTO

Re-posted 18 days ago


Job description

About us
Encord is the universal data layer for AI that helps 300+ AI teams train and run models on the right data. Our platform indexes, curates, annotates, and evaluates data across the full AI lifecycle, from development through production.
Trusted by Woven by Toyota, AXA, UiPath, Zipline, and more. We're an ambitious team of 100+ working at the frontier of AI and have raised $60M in Series C funding from Wellington Management, CRV, Next47 and Y Combinator.
The role
As a Human Data Operations & Solutions Engineer at Encord, you will sit at the intersection of technical sales and hands-on data operations. You are the expert who takes a prospect from first demo to a working proof of concept - not just by showing the platform, but by actually delivering a small-scale, high-quality annotation sample that demonstrates what best-in-class data operations looks like in practice.
You'll own the full arc: leading technical discovery on demo calls, designing the annotation workflow, managing the delivery of sample datasets, and translating the results into a compelling case for the client. With a strong focus on robotics and autonomous driving, you'll be working with some of the most technically complex and data-intensive AI use cases in the industry.
What you'll do
  • Partner with Account Executives to lead the technical and operational strategy for complex enterprise sales cycles, co-owning the path to a successful proof of concept
  • Lead deep technical discovery sessions with ML Engineers, MLOps leaders, and non-technical stakeholders to understand data requirements and design the right annotation workflow
  • Manage end-to-end delivery of small-scale annotation POCs - translating complex AI requirements into clear instructions for annotation specialists, auditing outputs, and iterating on quality until the sample is client-ready
  • Build and deliver tailored demonstrations that combine platform capability with live, real-world annotation results - particularly for robotics, autonomous driving, and multimodal sensor data (LiDAR, camera fusion, etc.)
  • Act as a trusted advisor to clients on annotation workflow design, data quality, and the operational processes that underpin model performance
  • Provide structured feedback and guidance to annotation teams during POC delivery, ensuring outputs meet the quality bar required to win client confidence
  • Translate findings and operational results into clear value propositions for senior, non-technical stakeholders
  • Serve as the voice of the customer to Product and Engineering, channelling detailed technical feedback from enterprise clients to shape the roadmap

Who we're looking for
  • A sharp operator who combines structured, consulting-style thinking with hands-on execution - you're equally comfortable designing a workflow on a whiteboard and auditing annotation outputs in a spreadsheet
  • Technically fluent: you can query a database, write a Python script to automate a workflow, or dig into annotation outputs to identify quality issues - and you know enough about ML pipelines to speak credibly with engineers
  • A natural communicator who can run a compelling demo, walk through a POC delivery, and explain what it all means to a VP in plain language
  • Genuinely passionate about AI, with particular interest in robotics, autonomous driving, and the data operations challenges that come with physical AI
  • Entrepreneurial and collaborative - you take ownership, move fast, and thrive when the work is ambiguous and high-stakes

Experience requirements
  • 1-3 years of professional experience, ideally spanning strategy consulting, AI/technology operations, or customer-facing technical roles (Solutions Engineering, Technical Account Management, or similar)
  • Proven ability to own complex, multi-stakeholder workflows end-to-end - from scoping and planning through execution, quality assurance, and client communication
  • Working proficiency in Python or SQL, with the ability to query data, automate workflows, or audit annotation outputs
  • Experience designing or optimising data operations processes with a strong eye for quality, consistency, and scalability - ideally involving human-in-the-loop or structured labelling workflows
  • Demonstrated ability to engage effectively with both technical stakeholders (ML engineers, data scientists) and non-technical clients
  • Hands-on experience with at least one major cloud platform (GCP, AWS, or Azure), including data storage and ML workflow patterns
  • Bonus: hands-on experience with computer vision, LiDAR, robotics sensor data, or autonomous driving datasets; prior exposure to data annotation platforms or quality management frameworks; experience in a customer-facing technical role at an AI company

Why Encord
  • Competitive salary, commission, and meaningful equity in a high-growth start-up
  • Clear, accelerated growth opportunities as the company scales rapidly
  • Strong in-person culture: 4 days/week in our newly launched North Beach loft office
  • Flexible PTO to fully recharge
  • 18 paid vacation days in the U.S. plus federal holidays
  • Annual learning & development budget
  • Comprehensive health, dental, and vision coverage
  • Frequent travel opportunities across the U.S., London, and Europe
  • Bi-annual company offsites, twice-weekly team lunches, and monthly socials