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

HumanSignal

Delivery Lead

Austin, TX · Remote

$110K - $140K/yr

... data creation to annotation to delivery. We design and create datasets from scratch, recruit and ... Partner with Product and Engineering to evolve internal tooling, automation, and operational ...

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Autonomize Inc

Business Analyst

Austin, TX · On-site

... data models, and repeatable processes that scale. You'll partner with Product, Engineering ... annotation workflows. * Validate data quality, lineage, and mappings across EHR, claims, and ...

Autonomize Inc

Business Analyst

Austin, TX · On-site

... data models, and repeatable processes that scale. You'll partner with Product, Engineering ... annotation workflows. * Validate data quality, lineage, and mappings across EHR, claims, and ...

WGNSTAR

BIM Modeler

Austin, TX · On-site

$38 - $45/hr

Collaborate with Coordinators and Leads regarding routing conflicts or missing data. * Execute ... Engineering or equivalent experience. * Strong experience in detailed annotation and drawing ...

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

Data Annotation Engineer information

See Austin, TX salary details

$51K

$146.2K

$195.3K

How much do data annotation engineer jobs pay per year?

As of Jul 6, 2026, the average yearly pay for data annotation engineer in Austin, TX is $146,165.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,300.00 and $194,300.00 per year, depending on experience, location, and employer.

What are the main challenges faced by Data Annotation Engineers in their daily work?

One of the main challenges Data Annotation Engineers face is ensuring consistent accuracy and quality in labeling large and often complex datasets. Attention to detail is critical, as even small errors can significantly affect machine learning model performance. Additionally, engineers must frequently adapt to evolving annotation guidelines and emerging data types, which requires ongoing learning and flexibility. Collaboration with data scientists and project managers is common to clarify requirements and resolve ambiguities, making strong communication skills essential for success.

What are the key skills and qualifications needed to thrive in the Data Annotation Engineer position, and why are they important?

To thrive as a Data Annotation Engineer, you need a strong background in data analysis, attention to detail, and familiarity with annotation processes, often supported by a degree in computer science or a related field. Proficiency with annotation tools like Labelbox, CVAT, or VIA, and understanding of data formats used in machine learning, is commonly required. Excellent communication, collaboration, and organizational skills help you effectively manage projects and cooperate with cross-functional teams. These abilities are crucial for delivering high-quality labeled data, which directly impacts the performance of AI and machine learning models.

Does data annotation really pay?

Data annotation engineers can earn competitive wages, often paid hourly or per task, with pay rates varying based on experience, complexity of annotations, and the platform or employer. Entry-level roles may start at minimum wage, while experienced annotators or those with specialized skills can earn higher salaries or freelance rates. Overall, data annotation can provide a reliable income, especially for remote or flexible work arrangements.

What is the highest salary for data annotator?

The highest salary for a data annotation engineer can reach up to $80,000 to $100,000 annually, depending on experience, location, and the complexity of annotation tasks. Senior roles or those with specialized skills in tools like Labelbox or CVAT may earn higher compensation. Salaries vary widely across companies and regions but generally reflect the technical skills required for high-quality data labeling.

What is a data annotation engineer?

A data annotation engineer is a professional responsible for labeling and annotating data, such as images, text, or videos, to prepare it for machine learning models. They often use specialized tools and follow guidelines to ensure data quality, supporting the development of AI systems.

How hard is it to get hired by data annotation?

Getting hired as a data annotation engineer typically requires basic computer skills, attention to detail, and familiarity with annotation tools. Many positions are entry-level and may not require advanced degrees, but strong accuracy and consistency are important for success in the role.

What is a Data Annotation Engineer job?

A Data Annotation Engineer is responsible for labeling and annotating data—such as text, images, audio, or video—to train machine learning models. They ensure that data is accurately categorized and structured to improve model performance. This role often involves using specialized annotation tools, following detailed guidelines, and working closely with data scientists and AI teams. Data Annotation Engineers play a crucial role in the development of AI applications by providing high-quality labeled datasets for supervised learning.

What are popular job titles related to Data Annotation Engineer jobs in Austin, TX? For Data Annotation Engineer jobs in Austin, TX, the most frequently searched job titles are:
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Data Annotation Engineer jobs near you
Software Engineering Manager, AI Evaluation Platform

Software Engineering Manager, AI Evaluation Platform

Procore Technologies, Inc.

Austin, TX • On-site

Full-time

Posted 8 days ago

Job description

We're looking for a Software Engineering Manager for our AI Evaluation Platform team to join Procore's Construction Intelligence organization. In this role, you'll build the infrastructure and tooling that enables users and internal teams to measure, benchmark, and improve the quality of AI agents - including Search Agent, RFI Create Agent, Invoice Agent, and future agentic products. You will own the end-to-end evaluation lifecycle: from defining quality metrics and building evaluation frameworks, to delivering intuitive interfaces that surface actionable insights about agent performance.
This position reports into Sr Director of the Procore AI Engineering team and will be 2 days per week hybrid role in our Austin office. We're looking for someone to join us immediately.
What you'll do:
  • Lead and grow a team of engineers focused on evaluation infrastructure, quality measurement, and developer tooling for AI agents.
  • Define the technical vision and roadmap for the Evaluation Platform - covering offline evaluations (batch benchmarks, regression suites) and online evaluations (live traffic quality monitoring, A/B testing).
  • Partner with AI/ML, Product, and Agent teams to define quality metrics for agents (relevance, accuracy, latency, safety, user satisfaction, token usage) and build automated pipelines to compute them at scale.
  • Design and deliver user-facing evaluation tools that allow customers and internal teams to assess agent output quality, compare model versions, and identify regressions.
  • Build frameworks for human-in-the-loop evaluation - annotation workflows, rating interfaces, and inter-rater reliability measurement.
  • Establish CI/CD quality gates so that new agent versions cannot ship without passing evaluation thresholds.
  • Drive engineering excellence: code quality, system reliability, test coverage, on-call health, and technical debt management.
  • Recruit, mentor, and develop engineers - fostering a culture of ownership, curiosity, and rigorous experimentation.

What we're looking for:
  • 2+ years managing engineering teams or technical leads, with 7+ years total in software engineering.
  • Experience building evaluation, quality measurement, or observability platforms for LLM-based or agentic systems (RAG pipelines, multi-step agents, tool-use agents).
  • Strong understanding of evaluation methodologies: precision/recall, LLM-as-judge, human annotation, A/B testing, and statistical significance frameworks.
  • Proven ability to translate ambiguous problem spaces into clear technical strategies and executable roadmaps.
  • Hands-on technical depth in backend systems, data pipelines, or distributed infrastructure (Python, Go, or similar)
  • Familiarity with evaluation frameworks such as RAGAS, DeepEval, LangFuse, or custom eval harnesses.
  • Background in search relevance (NDCG, MRR) or information retrieval quality systems.
  • Experience with construction-tech, procurement, or enterprise B2B SaaS domains.

Additional Information
Base Pay Range:
168,560.00 - 231,770.00 USD Annual
This role may also be eligible for Equity Compensation and/or Bonus Incentive Compensation. Procore is committed to offering competitive, fair, and commensurate compensation. Actual compensation will be based on a candidate's job-related skills, experience, education or training, and location.
For Los Angeles County (unincorporated) Candidates:
Procore will consider for employment all qualified applicants, including those with arrest or conviction records, in accordance with the requirements of applicable federal, state, and local laws, including the City of Los Angeles' Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.
A criminal history may have a direct, adverse, and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: 1. appropriately managing, accessing, and handling confidential information including proprietary and trade secret information, as well as accessing Procore's information technology systems and platforms; 2. interacting with and occasionally having unsupervised contact with internal/external customers, stakeholders, and/or colleagues; and 3. exercising sound judgment.

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