[Remote] QA Engineer

Note: The job is a remote job and is open to candidates in USA. Neuroscale AI is building a next-generation AI recruiting and talent intelligence platform that helps organizations turn hiring into a measurable, repeatable, and intelligent science. They are seeking a Senior QA Engineer / Quality Engineering Lead to own quality assurance, testing strategy, product validation, and release readiness across their platforms ARBI and Athena. The role involves auditing the QA landscape, defining the testing strategy, building automated quality systems, and ensuring product quality across various workflows.ResponsibilitiesBuild the Quality Strategy and QA Operating SystemPerform a deep audit of the current QA setup across ARBI, Athena, frontend flows, backend services, APIs, data workflows, integrations, AI pipelines, and release processesDefine a company-wide QA strategy across short-term stabilization, mid-term automation, and long-term quality engineering maturityDesign a scalable test architecture using test pyramid principles, shift-left testing, smoke testing, regression testing, release gates, exploratory testing, and risk-based coverageDefine clear QA responsibilities between developers, QA, product, design, customer success, and release ownersEstablish a practical quality operating rhythm: test plans, release checklists, defect triage, severity definitions, sign-off workflows, and quality metricsOwn Product QA for ARBI and AthenaValidate implementation against requirements, designs, copy, acceptance criteria, user stories, and customer-specific workflowsTest UI, UX, business logic, responsiveness, edge cases, error states, empty states, loading states, accessibility, and validation messagesPerform exploratory, smoke, regression, and release-candidate testing before launchesValidate recruiter workflows including candidate search, matching, scoring, resume parsing, outreach sequencing, scheduling, recruiter dashboards, candidate profiles, and ATS/CRM-style workflowsValidate Athena workflows including resume support, interview preparation, rubric-based feedback, candidate assistance, AI-generated recommendations, and user-facing guidanceBuild and Modernize Test AutomationTake ownership of automated frontend, API, integration, and end-to-end test coverage using Cypress, Playwright, Pytest, Postman/Newman, or equivalent toolsCreate reliable automated regression suites for critical ARBI and Athena workflows, including authentication, permissions, candidate pipelines, analytics, notifications, integrations, and admin experiencesIntegrate tests deeply into CI/CD pipelines so failures are visible, actionable, and tied to release confidenceImprove test reliability, execution speed, data setup, fixture management, and maintainabilityIntroduce AI-assisted testing practices where useful, while maintaining clear human judgment and repeatable test evidenceValidate Backend, API, Data, and Workflow ReliabilityTest robust REST APIs, Python/FastAPI services, backend business logic, asynchronous workflows, and data-processing pipelinesValidate PostgreSQL, Redis, OpenSearch, Celery, Temporal, containerized services, deployment pipelines, and AWS-hosted environments from a QA perspectiveCreate API and integration test coverage for imports, exports, webhooks, permissions, search, scoring, candidate data, customer-specific configuration, and workflow automationTest with realistic and large-scale datasets to uncover performance, latency, search relevance, data integrity, and resilience issuesEstablish baseline performance, load, and reliability testing using JMeter, k6, Locust, or similar toolsQA AI, LLM, and Evaluation WorkflowsValidate AI-assisted recruiting workflows for accuracy, consistency, explainability, hallucination risk, bias risk, prompt adherence, rubric alignment, and human-in-the-loop behaviorTest AI scoring, candidate summaries, recommendations, interview feedback, resume analysis, and knowledge-retrieval experiences across normal, adversarial, and edge-case inputsCreate repeatable evaluation datasets and test harnesses to measure AI quality over timeValidate guardrails, fallback behavior, citations, confidence indicators, data boundaries, audit trails, and customer-specific configurationPartner with product and engineering to define what 'good' means for AI-generated outputs and how release readiness should be measuredOwn Release Readiness and Quality VisibilityCreate clear, structured, reproducible bug reports with screenshots/videos, environment details, severity, expected behavior, actual behavior, impact, and reproduction stepsRetest fixed issues, validate root-cause resolution, and prevent regressionsCommunicate QA status clearly before release: passed, failed, blocked, passed with known issues, or requires founder/product decisionBuild dashboards and reporting for test coverage, defect trends, regression health, release risk, performance baselines, and customer-impacting quality issuesHelp developers produce testable, high-quality code by establishing testing standards, review practices, and shared quality expectationsSkillsExperience in Quality Assurance, Quality Engineering, Software Test Engineering, or a related product-quality roleExperience testing SaaS web products, B2B platforms, workflow systems, data-heavy applications, or AI-enabled productsStrong manual QA skills: exploratory testing, edge-case discovery, cross-browser validation, responsive web testing, clear bug reporting, and release sign-offHands-on automated testing experience with Cypress, Playwright, Selenium, Pytest, Postman/Newman, RESTAssured, or similar toolsStrong backend engineering fluency, especially with Python, FastAPI, REST APIs, PostgreSQL, Redis, OpenSearch, Celery, Temporal, containers, AWS services, and deployment pipelinesExperience validating large-scale datasets, data pipelines, integrations, asynchronous workflows, search/indexing behavior, and workflow automationExperience integrating automated tests into CI/CD pipelines and improving reliability, speed, observability, and developer feedback loopsStrong systems thinking, sound quality architecture judgment, and the ability to move from ambiguity to an executable QA roadmapAbility to operate in startup-style environments with high ownership, speed, accountability, and mentoring responsibilityAbility and willingness to relocate to the Northern VA / Washington, DC area, or work closely with the team during Eastern Time business hours if remotePrior experience as a Staff QA Engineer, Lead QA Engineer, Quality Engineering Lead, SDET Lead, Founding QA Engineer, or QA ArchitectExperience testing AI/LLM products, evaluation workflows, RAG systems, AI copilots, agentic workflows, or human-in-the-loop decision systemsExperience in HR tech, recruiting platforms, ATS/CRM systems, career services, assessment platforms, or workforce development productsExperience with A/B testing, feature flags, analytics validation, event tracking, access control, subscription/billing flows, or customer-specific configurationsExperience with performance and load testing tools such as JMeter, k6, Locust, or similar platformsFamiliarity with security, privacy, accessibility, SOC 2-style controls, federal deployment expectations, or auditability requirementsComfort working directly with founders, customers, customer success, and implementation teams to translate real-world feedback into quality improvementsBenefitsPotential performance incentives and/or equity participation based on final offer structure.Medical, dental, vision, PTO, and company-supported professional growth.Support for relevant courses, conferences, certifications, technical books, and quality engineering communities.Remote-first / hybrid flexibility with strong preference for Northern VA / DC-area alignment and occasional in-person collaboration.Company OverviewThe only advantage in hiring, is speed. Arbi sources, evaluates, and reaches out to candidates, automatically. It was founded in 2024, and is headquartered in Sterling, Virginia, USA, with a workforce of 11-50 employees. Its website is https://www.neuroscale.ai.