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Data Annotation Manager Jobs in Austin, TX (NOW HIRING)

Allen Control Systems

Platform Engineer, Data

Austin, TX · On-site

$113K - $136K/yr

Develop and use data quality tooling: metrics for balance, drift, and annotation error; active ... Implement and own dataset versioning, release management, and lineage and metadata cataloging. What ...

Allen Control Systems

Platform Engineer, Data

Austin, TX

$113K - $136K/yr

Develop and use data quality tooling: metrics for balance, drift, and annotation error; active ... Implement and own dataset versioning, release management, and lineage and metadata cataloging. What ...

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All JobsData Annotation JobsData Annotation Manager JobsData Annotation Manager Jobs in Austin, TX

Data Annotation Manager information

See Austin, TX salary details

$30.7K

$96.3K

$170.5K

How much do data annotation manager jobs pay per year?

As of Jun 13, 2026, the average yearly pay for data annotation manager in Austin, TX is $96,291.00, according to ZipRecruiter salary data. Most workers in this role earn between $65,400.00 and $124,400.00 per year, depending on experience, location, and employer.

What is the salary of data annotation manager?

The salary of a Data Annotation Manager typically ranges from $60,000 to $120,000 annually, depending on experience, location, and company size. Senior roles or those in high-cost areas may offer higher compensation, and proficiency with annotation tools and team management can influence pay levels.

Is data annotation high paying?

Data annotation managers typically earn higher salaries than entry-level annotators due to their supervisory responsibilities and expertise in labeling tools and processes. Salaries vary based on experience, location, and company size, but the role generally offers competitive pay within the data labeling industry.

Is data annotation real or fake?

Data annotation is a real and essential process in machine learning and AI development, involving labeling data such as images, text, or audio to train algorithms. Data annotation managers oversee this work, ensuring accuracy and quality using tools like labeling platforms and quality control procedures.

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 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.

Is it hard to get a job with data annotation?

Securing a job as a data annotation manager typically requires experience in data labeling, familiarity with annotation tools, and understanding of data quality standards. While entry-level roles may be accessible with basic skills, advancing to managerial positions often demands relevant experience and leadership abilities.

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

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 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 Austin, TX? The most popular types of Data Annotation jobs in Austin, TX are:
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What job categories do people searching Data Annotation Manager jobs in Austin, TX look for? The top searched job categories for Data Annotation Manager jobs in Austin, TX are:
What cities near Austin, TX are hiring for Data Annotation Manager jobs? Cities near Austin, TX with the most Data Annotation Manager job openings:
Data Annotation Manager jobs near you
Infographic showing various Data Annotation Manager job openings in Austin, TX as of June 2026, with employment types broken down into 100% Full Time. Highlights an 74% In-person, and 26% Remote job distribution, with an average salary of $96,291 per year, or $46.3 per hour.

Platform Engineer, Data

Allen Control Systems

Austin, TX • On-site

$113K - $136K/yr

Full-time

Medical, Dental, Vision, PTO

Posted 26 days ago

Job description

Allen Control Systems (ACS) is a cutting-edge defense startup founded by two former Navy electrical engineers with a proven track record in robotics and software. We are developing an autonomous gun turret using advanced computer vision and control systems to precisely detect, track, and neutralize enemy drones.

With an engineering-first culture, ACS values technical excellence and continuous learning. Backed by our founders' successful exits from two previous ventures acquired for a combined $180M in 2022, we are committed to ensuring that the groundbreaking technologies we develop have a real-world impact. 

Position Overview:  

We are seeking a Data Platform Engineer who combines expert-level data infrastructure skills with a strong knowledge of AI & Machine Learning principles. In this role, you will go beyond simple data validation scripts; you will apply your understanding of model training dynamics to design and implement existing and novel approaches to optimize our datasets. 

You will build and maintain large-scale image and video pipelines, but with a focus on data curation strategies—such as coreset selection, embedding-based filtering, and automated complexity scoring. You’ll partner closely with our ML engineers to orchestrate ingestion, synthetic data generation, and versioned releases, ensuring that every dataset is not only high-integrity and available but strictly optimized to maximize model performance. 

What You’ll Do: 

  • Design and develop a scalable data infastructure, focusing on organization and curation to support continuing increases in data volume and complexity

  • Design and implement existing and novel approaches to optimize datasets for model training (e.g., hard example mining, class balancing, de-duplication, embedded-based filtering).

  • Support the data infrastructure required for optimal ingestion, transformation, and storing of datasets

  • Develop and use synthetic data generation workflows to create realistic synthetic training data for computer vision models.

  • Design and own end-to-end image and video pipelines for computer vision model training: multi-source ingestion, QA and visualization, standardization, and organization.

  • Coordinate collection of real-world data; coordinate label creation and QA with labelers.

  • Develop and use data quality tooling: metrics for balance, drift, and annotation error; active-learning sampling to target gaps; feedback loops from production back to curation. 

  • Implement and own dataset versioning, release management, and lineage and metadata cataloging.

What You’ll Need: 

  • 3+ years of experience in data engineering or equivalent fields. 

  • Solid understanding of data structures and systems design for orchestrating data-related workflows in a rapidly growing environment.

  • Proficient in using AWS for data management and processing.

  • Proficient in Python for scripting and data processing; proficient with SQL and Linux.

  • Educational Background: Bachelor’s or Master’s degree in Computer Science or a related field.

  • Proven ability to communicate well across engineering teams, and write and maintain effective documentation. 

You’ll Stand Out: 

  • 5+ years of industry experience.

  • Experience in image/video data engineering for computer vision projects.

  • Experience with PyTorch DeepCore.

  • Experience with Unreal Engine. 

What We Offer: 

  • Competitive salary

  • ACS Equity Package

  • Health, Dental, Vision Insurance

  • Paid Time Off 

Allen Control Systems is an Equal Opportunity Employer, providing equal employment opportunities to all employees and applicants for employment. Allen Control Systems prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.