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Data Annotation Manager Jobs in Oakman, AL (NOW HIRING)

All JobsData Annotation JobsData Annotation Manager JobsData Annotation Manager Jobs in Oakman, AL

Data Annotation Manager information

See Oakman, AL salary details

$27.4K

$85.9K

$152K

How much do data annotation manager jobs pay per year?

As of Sep 13, 2026, the average yearly pay for data annotation manager in Oakman, AL is $85,863.00, according to ZipRecruiter salary data. Most workers in this role earn between $58,300.00 and $110,900.00 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.

Data Annotation Manager jobs near you
Infographic showing various Data Annotation Manager job openings in Oakman, AL as of August 2026, with employment types broken down into 85% Full Time, 7% Part Time, and 8% Contract. Highlights an 72% In-person, 7% Hybrid, and 21% Remote job distribution, with an average salary of $85,863 per year, or $41.3 per hour.

Senior AI Test Automation Engineer

Birmingham, AL • On-site

Jobtailor
See more jobs at Jobtailorexternal

Other

Posted 6 days ago

Job description


  • Engage directly with users and stakeholders to collect feedback, observe real usage patterns, and identify consistent user flows through LLM-powered features

  • Translate user feedback and production traces into curated evaluation datasets in LangSmith

  • Design, build, and maintain offline evaluation suites for regression, benchmarking, and backtesting

  • Develop and calibrate heuristic/code-based checks, LLM-as-judge evaluators, and pairwise comparisons

  • Validate evaluator reliability against human review

  • Instrument and maintain end-to-end tracing across LangGraph agents and workflows using LangSmith

  • Manage annotation queues and human-in-the-loop feedback workflows

  • Analyze agent trajectories and multi-step LangGraph executions to identify failure points and distinguish LLM variance from product defects

  • Integrate evaluation runs and automated tests into CI/CD pipelines as quality gates

  • Support production auditing, online evaluations, quality drift detection, and alerting

  • Design, develop, and implement automated UI, API, integration, regression, and deterministic E2E tests for LLM-powered application surfaces

  • Collaborate with QA, development, product, and business teams to translate requirements, acceptance criteria, and expected agent behaviors into test and evaluation coverage

  • Analyze and triage automation test failures

  • Support test data management and test-environment stability

  • Report test results, evaluation outcomes, coverage, quality trends, and risks

  • Participate in code reviews and contribute to automation and evaluation standards, reusable components, and best practices

  • Engage with the QA Center of Excellence to align automation and AI evaluation practices with enterprise standards


Requirements

  • Must be eligible to work in the US without Visa Sponsorship

  • 5+ years of experience in test automation engineering, software quality assurance, or a related role

  • 1–2+ years of hands‑on experience testing or evaluating LLM-powered applications

  • Hands‑on experience with LangSmith for tracing, evaluation, and observability of LLM applications

  • Hands‑on experience with LangGraph, including graph‑based agent workflows, state and context management, and tool calling

  • Hands‑on experience building and maintaining automated test suites using Playwright, preferably with TypeScript

  • Working proficiency in Python and/or TypeScript

  • Experience automating UI and API testing, and experience with API testing tools or frameworks

  • Familiarity with CI/CD tools such as Azure DevOps, Jenkins, GitHub Actions, or equivalent

  • Working knowledge of Git

  • Understanding of Agile/Scrum delivery practices and participation in Agile ceremonies

  • Ability to analyze requirements and user feedback, identify test and eval scenarios, and create maintainable automated coverage

  • Strong problem‑solving, troubleshooting, communication, and collaboration skills

  • Ability to understand and document end‑to‑end business processes, user journeys, and operational workflows

  • Experience with online evaluations, production monitoring, and quality drift detection for LLM applications

  • Experience with agent trajectory evaluation, RAG evaluation, or guardrails validation

  • Familiarity with prompt engineering and prompt versioning workflows

  • Understanding of statistical approaches to nondeterministic testing

  • Experience authoring BDD/Gherkin scenarios using Cucumber or a similar framework

  • Experience with contract testing tools

  • Familiarity with SAFe and Agile Release Train (ART) practices

  • Experience with AI‑assisted engineering practices

  • Experience with performance, accessibility, mobile, or security test automation

  • Experience with test management and defect‑tracking tools, such as Azure DevOps


Core Competencies

Demonstrates expertise in test automation engineering and quality assurance for LLM-powered applications, with a strong focus on developing and maintaining automated test suites, analyzing user feedback, and ensuring product quality through rigorous evaluation practices.


Highest-signal resume keywords

  • Test Automation Engineering

  • LangSmith Experience

  • LangGraph Workflows

  • Automated Test Suites Development

  • CI/CD Tools Familiarity


Hard Skills

  • Test Automation Engineering

  • LLM Application Evaluation

  • Automated UI Testing

  • API Testing

  • Python Programming

  • TypeScript Programming

  • BDD/Gherkin Scenarios

  • Statistical Testing Approaches

  • Performance Test Automation

  • Accessibility Test Automation


Soft Skills

  • Problem‑Solving

  • Troubleshooting

  • Communication

  • Collaboration


Industry Keywords

  • Agile Practices

  • Scrum

  • Quality Drift Detection

  • Human‑in‑the‑Loop Workflows

  • Production Monitoring


Tools & Technologies

  • LangSmith

  • LangGraph
  • Playwright

  • Azure DevOps

  • Jenkins

  • GitHub Actions

  • Cucumber

  • Test Management Tools

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