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Machine Learning Qa Jobs (NOW HIRING)

North Texas Tollway Authority

QA QC Supervisor

Plano, TX · On-site

$58K - $76K/yr

The QA/QC Supervisor leads a team of Quality Analysts and Coordinators to ensure high service ... Ensure consistency in evaluation through calibration sessions and machine-learning-informed quality ...

Brilent

QA Engineer Intern

San Mateo, CA · On-site

The Brilent team brings together deep experience in machine learning, and data science from leading ... As a QA Engineer Intern, you will work alongside our small team of engineers to develop new ...

AEG

QA Engineer

Charlotte, NC · On-site

Exposure to AI-powered testing tools or machine learning concepts in QA Benefits * Competitive salary and comprehensive benefits. * Company stock options. * Health, dental, and vision insurance to ...

PDF Solutions

Senior QA Automation Engineer

Deer Park, IL · On-site

$80K - $125K/yr

The Senior QA Automation Engineer will play a critical role in ensuring the accuracy, reliability ... Validate machine learning models by evaluating prediction accuracy against historical and real ...

Agero, Inc.

Sr. QA Engineer

They are seeking a Senior QA Engineer to ensure high-quality software delivery by developing ... Machine Learning and AI. • Knowledge of the 'Shift left in testing' paradigm. • Experience with ...

Microsoft

Machine Learning

Mountain View, CA · On-site

$220K - $331K/yr

Overview As a Member of Technical Staff - Machine Learning, you will work to create LLM models for ... quality code, and creating new user-facing features. You should be comfortable creating ...

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How much do machine learning qa jobs pay per hour?

As of Jun 10, 2026, the average hourly pay for machine learning qa in the United States is $44.87, according to ZipRecruiter salary data. Most workers in this role earn between $36.54 and $54.57 per hour, depending on experience, location, and employer.

What is a Machine Learning QA job?

A Machine Learning QA (Quality Assurance) professional is responsible for testing and validating machine learning models to ensure accuracy, reliability, and performance. They design test cases, create automated testing pipelines, and identify biases or errors in datasets and model outputs. Their role bridges software testing and data science, ensuring that ML systems function correctly in production.

What are the typical daily responsibilities of a Machine Learning QA professional?

A Machine Learning QA professional is primarily responsible for designing, implementing, and executing test plans to ensure the quality and performance of machine learning models and their integration into software products. This often involves developing automated tests, validating dataset integrity, monitoring model outputs, and collaborating closely with data scientists and developers to resolve issues. Additionally, you may participate in code reviews, maintain testing documentation, and contribute to continuous improvement of testing processes. The role is collaborative and requires balancing technical rigor with practical problem-solving to help deliver robust AI-powered applications.

What are the key skills and qualifications needed to thrive in the Machine Learning Qa position, and why are they important?

Success as a Machine Learning QA requires a solid understanding of software testing principles, machine learning concepts, and programming skills, typically supported by a degree in computer science or a related field. Familiarity with tools like Python, TensorFlow or PyTorch, and QA automation frameworks, as well as relevant certifications in software testing or ML, are often advantageous. Strong analytical thinking, attention to detail, and effective communication are standout soft skills in this role. These competencies are essential for ensuring machine learning models function as intended, meet quality standards, and integrate smoothly into production environments.

More about Machine Learning Qa jobs
What cities are hiring for Machine Learning Qa jobs? Cities with the most Machine Learning Qa job openings:
What are the most commonly searched types of Machine Learning Qa jobs? The most popular types of Machine Learning Qa jobs are:
What states have the most Machine Learning Qa jobs? States with the most job openings for Machine Learning Qa jobs include:
What job categories do people searching Machine Learning Qa jobs look for? The top searched job categories for Machine Learning Qa jobs are:
Machine Learning Qa jobs near you
Infographic showing various Machine Learning Qa job openings in the United States as of June 2026, with employment types broken down into 39% Full Time, 58% Part Time, and 3% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $93,338 per year, or $44.9 per hour.
Quality Assurance Engineer

Quality Assurance Engineer

Anika Systems

Leesburg, VA • On-site, Remote

Full-time

Posted 18 days ago

Job description

Anika Systems is seeking a highly technical Quality Assurance Engineer with strong development, SQL, and Python expertise to support enterprise data platforms for federal clients. This is not a traditional manual QA role and this position requires a developer mindset, focused on automation, data validation, and platform reliability across modern cloud-based architectures.
The ideal candidate will design and implement automated testing frameworks for ETL pipelines, Apache Iceberg data architectures, XBRL datasets, and performance-optimized structures such as materialized views-ensuring data accuracy, integrity, and trust across the enterprise. This role also requires proficiency in AI tools and AI-driven workflows, leveraging automation and intelligent testing techniques to improve quality and delivery speed.
This opportunity is 100% remote.
Key Responsibilities
Test Automation & QA Engineering
  • Design, develop, and maintain automated QA frameworks for data pipelines, APIs, and analytics platforms using Python and SQL.
  • Build reusable testing utilities for data validation, regression testing, and pipeline certification.
  • Integrate automated tests into CI/CD pipelines to support continuous testing and deployment.
  • Develop unit, integration, and end-to-end test cases for complex data workflows.
  • Leverage AI-assisted testing tools to generate test cases, identify edge cases, and improve test coverage.
Data Validation & ETL Testing
  • Validate ETL/ELT pipelines to ensure accurate ingestion, transformation, and delivery of data.
  • Create automated checks for data completeness, consistency, accuracy, and timeliness.
  • Test ingestion and transformation of complex datasets, including XBRL financial data.
  • Implement reconciliation and audit mechanisms across source-to-target mappings.
  • Apply AI-driven anomaly detection to identify data quality issues and pipeline failures.
Iceberg & Materialized View Testing
  • Develop and execute test strategies for Apache Iceberg-based data lakehouse architectures, including:
    • Schema evolution validation
    • Time travel and versioning accuracy
    • Partitioning and performance behavior
  • Validate and compare materialized views vs. Iceberg table performance and consistency, including:
    • Query performance benchmarking
    • Data freshness and latency
    • Storage efficiency and maintenance overhead
  • Ensure alignment between precomputed datasets (materialized views) and underlying source data.
Data Quality, Metadata & Context Validation
  • Implement automated validation for data quality rules, lineage, and metadata accuracy.
  • Support context engineering by validating that datasets include proper business context, definitions, and relationships.
  • Integrate QA processes with enterprise data catalogs and metadata systems to ensure discoverability and trust.
  • Validate AI-generated metadata, lineage, and transformations for accuracy and traceability.
AI-Driven Quality Engineering
  • Apply AI/ML and generative AI tools to enhance QA processes, including intelligent test generation, defect prediction, and automated root cause analysis.
  • Validate data readiness for AI/ML and generative AI use cases, ensuring datasets meet quality, completeness, and governance standards.
  • Collaborate with data and AI teams to test data pipelines supporting RAG, analytics, and machine learning workflows.
  • Ensure alignment with responsible AI practices, including traceability, explainability, and data integrity.
OCDO & Data Strategy Support
  • Support enterprise data management programs and OCDO initiatives by ensuring data quality and reliability across systems.
  • Contribute to data maturity assessments by evaluating data quality, testing coverage, and governance adherence.
  • Align QA processes with Federal Data Strategy and Evidence Act requirements.
Stakeholder Collaboration & Agile Delivery
  • Work closely with data engineers, data architects, and analysts to define test strategies and acceptance criteria.
  • Participate in stakeholder engagement sessions and listening campaigns to understand data quality expectations and pain points.
  • Document test results, defects, and quality metrics for both technical and non-technical stakeholders.
  • Operate within Agile teams to iteratively improve data quality processes and tooling.
  • Promote adoption of AI-driven efficiencies and automation across QA and data engineering workflows.
Required Qualifications
  • Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.
  • 5+ years of experience in QA engineering, data testing, or software development.
  • Strong programming skills in Python and advanced proficiency in SQL.
  • Experience building automated test frameworks for data platforms and ETL pipelines.
  • Hands-on experience with:
    • AWS data services (S3, Glue, Redshift, Lambda, etc.)
    • Apache Iceberg or similar data lake technologies
  • Experience validating materialized views and performance-optimized data structures.
  • Familiarity with XBRL or complex financial/regulatory datasets.
  • Understanding of data modeling, metadata, and data governance principles.
  • Experience with CI/CD tools and automated testing integration.
  • Demonstrated proficiency with AI tools and AI-assisted development/testing workflows.
  • Understanding of data quality requirements for AI/ML and analytics use cases.
  • U.S. Citizenship required; ability to obtain and maintain a federal clearance.
Preferred Qualifications
  • Experience supporting federal agencies such as SEC, DHS, Treasury, or Federal Reserve System.
  • Familiarity with data catalog and governance tools (e.g., Collibra, Alation, ServiceNow).
  • Experience with Apache Spark or distributed data processing frameworks.
  • Knowledge of data quality tools and observability platforms.
  • Exposure to data maturity frameworks (e.g., EDM DCAM, TDWI).
  • Experience testing large-scale cloud data platforms and lakehouse architectures.
  • Experience validating data pipelines supporting AI/ML, analytics, or generative AI solutions.
  • Familiarity with AI-driven testing tools or frameworks.

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