QA Engineer

About this role

About Neuroscale AI 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. Our ARBI platform supports sourcing, screening, evaluation, sequencing, scheduling, recruiter workflow automation, and explainable candidate assessment for commercial enterprises, public-sector agencies, higher education, staffing organizations, and workforce-development teams. Neuroscale is also building Athena, an AI-powered career readiness and candidate-assistance experience that helps users improve resumes, prepare for interviews, receive rubric-based feedback, and navigate the job search process with personalized AI support. We are a fast-moving startup operating at the intersection of GenAI, HR technology, recruiting operations, career services, workflow automation, and enterprise AI deployment. As ARBI and Athena scale across customers, we need a senior quality leader who can make product quality measurable, automated, auditable, and trusted. Role Overview We are hiring a Senior QA Engineer / Quality Engineering Lead to own quality assurance, testing strategy, product validation, and release readiness across ARBI and Athena. This is a hands-on senior role for someone who can test deeply, build automated quality systems, validate AI workflows, improve developer quality practices, and become the central authority for product quality. This role is not limited to executing test cases. You will audit the current QA landscape, define the testing strategy, build regression and automation frameworks, validate AI behavior, pressure-test edge cases, and create the release confidence needed for a rapidly evolving SaaS platform. You will work closely with engineering, product, design, customer success, and founders to make sure new features, bug fixes, experiments, integrations, data workflows, and AI-driven experiences ship with clarity, reliability, auditability, accessibility, and user trust. What You’ll Do (Key Responsibilities) 1) Build the Quality Strategy and QA Operating System • Perform a deep audit of the current QA setup across ARBI, Athena, frontend flows, backend services, APIs, data workflows, integrations, AI pipelines, and release processes. • Define a company-wide QA strategy across short-term stabilization, mid-term automation, and long-term quality engineering maturity. • Design a scalable test architecture using test pyramid principles, shift-left testing, smoke testing, regression testing, release gates, exploratory testing, and risk-based coverage. • Define clear QA responsibilities between developers, QA, product, design, customer success, and release owners. • Establish a practical quality operating rhythm: test plans, release checklists, defect triage, severity definitions, sign-off workflows, and quality metrics. 2) Own Product QA for ARBI and Athena • Validate implementation against requirements, designs, copy, acceptance criteria, user stories, and customer-specific workflows. • Test UI, UX, business logic, responsiveness, edge cases, error states, empty states, loading states, accessibility, and validation messages. • Perform exploratory, smoke, regression, and release-candidate testing before launches. • Validate recruiter workflows including candidate search, matching, scoring, resume parsing, outreach sequencing, scheduling, recruiter dashboards, candidate profiles, and ATS/CRM-style workflows. • Validate Athena workflows including resume support, interview preparation, rubric-based feedback, candidate assistance, AI-generated recommendations, and user-facing guidance. 3) Build and Modernize Test Automation • Take ownership of automated frontend, API, integration, and end-to-end test coverage using Cypress, Playwright, Pytest, Postman/Newman, or equivalent tools. • Create reliable automated regression suites for critical ARBI and Athena workflows, including authentication, permissions, candidate pipelines, analytics, notifications, integrations, and admin experiences. • Integrate tests deeply into CI/CD pipelines so failures are visible, actionable, and tied to release confidence. • Improve test reliability, execution speed, data setup, fixture management, and maintainability. • Introduce AI-assisted testing practices where useful, while maintaining clear human judgment and repeatable test evidence. 4) Validate Backend, API, Data, and Workflow Reliability • Test robust REST APIs, Python/FastAPI services, backend business logic, asynchronous workflows, and data-processing pipelines. • Validate PostgreSQL, Redis, OpenSearch, Celery, Temporal, containerized services, deployment pipelines, and AWS-hosted environments from a QA perspective. • Create API and integration test coverage for imports, exports, webhooks, permissions, search, scoring, candidate data, customer-specific configuration, and workflow automation. • Test with realistic and large-scale datasets to uncover performance, latency, search relevance, data integrity, and resilience issues. • Establish baseline performance, load, and reliability testing using JMeter, k6, Locust, or similar tools. 5) QA AI, LLM, and Evaluation Workflows • Validate AI-assisted recruiting workflows for accuracy, consistency, explainability, hallucination risk, bias risk, prompt adherence, rubric alignment, and human-in-the-loop behavior. • Test AI scoring, candidate summaries, recommendations, interview feedback, resume analysis, and knowledge-retrieval experiences across normal, adversarial, and edge-case inputs. • Create repeatable evaluation datasets and test harnesses to measure AI quality over time. • Validate guardrails, fallback behavior, citations, confidence indicators, data boundaries, audit trails, and customer-specific configuration. • Partner with product and engineering to define what “good” means for AI-generated outputs and how release readiness should be measured. 6