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QA Engineering Lead

Job in Richmond, Henrico County, Virginia, 23214, USA
Listing for: Unboxed Training & Technology
Full Time position
Listed on 2026-08-06
Job specializations:
  • Quality Assurance - QA/QC
    IT QA Tester / Automation, Quality Engineering, QA Specialist - Analyst/Manager
Salary/Wage Range or Industry Benchmark: 110000 - 160000 USD Yearly USD 110000.00 160000.00 YEAR
Job Description & How to Apply Below

The QA Engineering Lead is the senior hands-on quality engineer for the Unboxed platform and is personally accountable for the quality of what ships to enterprise clients. This is primarily an individual contributor role: the Lead spends most of their time in the product, designing and executing test strategy, running manual and automated testing, owning release verification, and diagnosing defects. Quality spans four surfaces: a robust learning management system, AI-generated roleplay and practice, coaching workflows, and client-facing analytics and reporting.

The role also includes directly managing two full-time QA Engineers with a coaching-first approach and partnering with contract engineering and QA teams to establish and maintain their ways of working. The role owns the quality signal used to decide whether a release is ready.

Key Responsibilities Test Strategy and Execution:
  • Own and execute the end-to-end test strategy across the platform, including exploratory testing, regression coverage, and release verification.
  • Maintain critical-path coverage for the workflows with the highest client impact and SLA exposure and extend automated testing where it produces durable value.
  • Serve as the final quality gate before release, owning go/no-go recommendations and flagging risk to Product and Engineering rather than making ship decisions unilaterally.
Quality Of AI-Generated Outputs
  • Define how quality is measured for AI-generated scoring, feedback, and roleplay, using evaluation approaches that account for output variability rather than exact-match expectations.
  • Build test sets, rubrics, and monitoring that surface regressions in AI quality, and partner with AI Engineering to validate changes to models, prompts, and scoring logic.
  • Translate client trust concerns into concrete, testable acceptance criteria the team can verify before and after release.
Quality Metrics And Reporting
  • Own the QA metrics that measure quality health, including Defect Escape Rate, Critical Path Coverage, and MTTD/MTTR, and report on them on a regular cadence.
  • Maintain and apply a defect severity rubric that produces consistent triage across products.
  • Provide quality reporting to leadership, including trends, risk areas, and consciously accepted coverage gaps.
Contract Team Partnership
  • Partner with contract engineering and QA teams, establishing and maintaining the process, test conventions, and ways of working they operate within, and keeping those standards current as the platform evolves.
  • Set clear acceptance criteria and definitions of done, and review output so it meets the same bar as in-house work.
  • Serve as the quality point of accountability across in-house and contract contributions, so external capacity does not dilute the quality signal.
Team Leadership
  • Directly manage two QA Engineers with a coaching-first approach, owning their prioritization, technical growth, and performance.
  • Set and enforce standards for how tests are written, defects are documented, and coverage decisions are made, and grow each engineer's ability to reason about quality independently, so the team's judgment scales without requiring the Lead in every decision.
Cross-functional Collaboration
  • Partner with Engineering to shift quality earlier, embedding testability and clear acceptance criteria into development rather than inspecting quality in at the end.
  • Work with Support and Implementation to close the loop on client-reported defects, using field escalations from strategic accounts to sharpen coverage.
Key Skills
  • Deep hands-on QA engineering skill: test design, exploratory testing, regression strategy, and defect diagnosis in B2B SaaS. This is the primary skill the role is hired for.
  • Test automation proficiency and the judgment to know where automation pays off versus where it becomes maintenance burden.
  • Practical experience testing AI/ML or non-deterministic features, including how to evaluate scored outputs where there is no single correct answer.
  • Fluency with quality metrics and the ability to use them to drive decisions, not just report them.
  • Experience establishing and maintaining process in partnership with contract or vendor engineering and QA teams.
  • Abil…
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