Sr. QA Automation Engineer

Amplifire; Boulder (CO); On-site; USD 120,000 - 160,000; Full time

Job summary

Amplifire, headquartered in Boulder, CO, seeks a Senior QA Automation Engineer to raise software quality through automation, thoughtful test strategy, and AI-assisted workflows. You will build and maintain automated regression suites and contribute to testing strategy across complex features and workflows.

You will perform manual and exploratory testing, collaborate with engineering and product teams, and help apply AI-assisted testing approaches to increase coverage and release confidence while

Qualifications

• 5+ years of experience in Quality Assurance with automated test frameworks in production environments.
• Strong QA fundamentals including test coverage, risk assessment, defect writing, and regression planning/execution.
• Experience owning QA work for complex features or systems with minimal oversight.

Responsibilities

• Build and maintain automated regression suites using Cypress and Playwright.
• Own test planning and execution for complex features and workflows.
• Perform manual and exploratory testing where automation alone is not sufficient.
• Partner with engineering and product to embed quality throughout the development process.
• Maintain high-quality test cases, documentation, and defect reporting.

Skills

QA automation

Test frameworks

Automation tooling

Strong debugging

Tools

Cypress

Playwright

Copilot

Claude Code

Jira

React

TypeScript

Java

Python

Job description

Amplifire, headquartered in Boulder, CO, is the leading adaptive eLearning platform built from discoveries in brain science. It detects and corrects knowledge gaps and misinformation that exist in the minds of all humans. It allows people to master faster, retain longer, and perform better. Healthcare, education, and Fortune 500 companies use Amplifire’s patented learning algorithms, knowledge analytics, and diagnostic capabilities to drive improved outcomes with significant returns on investment.

Description

Amplifire is seeking a Senior QA Automation Engineer to help improve software quality through strong automation practices, thoughtful test strategy, and modern AI-assisted workflows. This role combines automated testing, manual and exploratory testing, and practical use of AI tools to improve test coverage, system reliability, and release confidence.

You will build and maintain automated regression suites, contribute to testing strategy, perform exploratory testing for complex workflows, and partner closely with engineering and product teams to ensure high-quality releases. You will also help apply AI-assisted testing approaches where they can reduce repetitive work, improve coverage, or make testing workflows more efficient.

While AI-assisted QA is still evolving within Amplifire, this role is not expected to own AI strategy across QA. Instead, you will use modern tools pragmatically, contribute to emerging practices, and help ensure that automation and AI-assisted workflows are reliable, auditable, and aligned with Amplifire’s quality standards.

Amplifire operates in a regulated environment (e.g., FedRAMP, SOC 2), where auditability, correctness, and reliability matter alongside development speed.

Requirements

What You’ll Do

Automation, Manual Testing & Quality Fundamentals

• Build and maintain automated regression suites using Cypress and Playwright.
• Own test planning and execution for complex features and workflows.
• Perform manual and exploratory testing where automation alone is not sufficient.
• Partner with engineering and product to identify risk early and embed quality throughout the development process.
• Maintain high-quality test cases, documentation, and defect reporting.
• Track defects in Jira and contribute to quality metrics.
• Conduct root cause analysis on production issues and help identify prevention strategies.

AI-Assisted Testing & Modern Tooling

• Use AI tools to support test design, analysis, automation, and coverage improvement.
• Contribute to AI-assisted testing workflows that help reduce repetitive work and improve release confidence.
• Help evaluate AI-driven features for quality, reliability, and consistency.
• Follow and contribute to evolving best practices for responsible AI-assisted QA workflows.
• Use modern AI tools pragmatically while validating outputs and maintaining strong QA standards.

Qualifications

We care less about titles and more about demonstrated capability. The right person brings a strong foundation in QA automation, strong testing judgment, and practical experience using modern tools to improve quality and release confidence.

Required Experience

• 5+ years of experience in Quality Assurance, including designing and maintaining automated test frameworks in production environments.
• Strong QA fundamentals, including test coverage strategy, risk assessment, defect writing, and regression planning / execution.
• Experience owning QA work for complex features or systems with minimal oversight.

AI & Modern Tooling

• Experience using AI tools to support testing workflows, such as generating test cases, analyzing results, improving coverage, or accelerating repetitive QA work.
• Exposure to testing AI/LLM-driven features or interest in developing stronger evaluation practices for AI-assisted functionality.
• Familiarity with modern AI development tools and workflows, such as Copilot, Claude Code, or similar.

Technical & Collaboration Skills

• Comfortable working across a modern web stack (e.g., React, TypeScript, Java, Python) and combining automation, manual, and exploratory testing for full system coverage.
• Strong debugging, problem-solving, and communication skills, with the ability to influence quality practices through clear thinking, execution, and collaboration.

What Success Looks Like

Within your first year, success in this role looks like:

• Automated test coverage is more stable, targeted, and easier to maintain.
• Regression testing is more efficient without reducing release confidence.
• Complex workflows are tested consistently through a thoughtful mix of automation, manual testing, and exploratory testing.
• AI-assisted workflows are used pragmatically where they improve efficiency, coverage, or repeatability.
• Quality issues are identified earlier, documented clearly, and used to improve future testing practices.
• Have a proactive approach to problem solving and not taking the current approach at face value just because that is the way it is being done.