Oncologic AI is building the next generation of AI solutions for community oncology. Our cloud-native Oncology Process Orchestration Platform automates complex clinical workflows — from patient intake and EHR summarization to care coordination and exception detection. Hosted on Google Cloud Platform (GCP) and leveraging frontier LLMs (Claude, GPT, Gemini), our platform takes a model-agnostic approach to streamlining oncology operations. Quality is foundational to everything we build — our products operate in high-stakes clinical environments where reliability is non-negotiable.
Role Overview
We are seeking a seasoned Senior QA Manager to lead quality engineering across Oncologic AI's platform and establish the QA function as a strategic pillar of our Engineering organization. This is a leadership role with broad scope: you will set the quality vision, build and mentor a QA team, and own the end-to-end test strategy across our web platform, AI-powered features, and GCP-based infrastructure.
This role is uniquely positioned at the intersection of traditional QA leadership and AI/LLM quality engineering. You will partner closely with AI Engineers to define how we test RAG pipelines, agentic workflows, and LLM outputs — establishing standards for AI feature quality that don't yet exist as industry norms. You will also serve as the quality advocate across product squads, embedding quality thinking from the earliest stages of design through production.
If you are a proven QA leader who is excited by the challenge of building quality frameworks for AI-driven products in a high-stakes clinical environment, this role was built for you.
Key Responsibilities
QA Leadership & Team Development
- Own and lead the QA function at Oncologic AI — setting team direction, hiring, and developing QA talent as the organization scales
- Define, implement, and continuously improve the company's overall quality strategy, standards, and processes
- Serve as the senior quality voice in Engineering leadership discussions, representing QA in roadmap planning, architecture reviews, and release decisions
- Mentor and grow QA Analysts and Engineers, fostering a culture of quality ownership across all squads
- Establish and track quality KPIs — defect escape rate, automation coverage, test cycle time — and report on quality health to engineering leadership
Test Strategy & Automation Architecture
- Define and own the end-to-end test automation strategy across web, API, and AI feature layers of the Oncologic platform
- Architect scalable, maintainable automated test frameworks using Playwright and/or Selenium WebDriver in JavaScript/TypeScript
- Lead integration of automated test suites into GCP-based CI/CD pipelines (Cloud Build, GitHub Actions, or equivalent), ensuring quality gates are enforced at every stage
- Drive test coverage across functional, regression, integration, performance, and exploratory testing disciplines
- Evaluate, select, and implement QA tooling — test management platforms, defect tracking, reporting dashboards — to support team effectiveness
AI & LLM Quality Engineering
- Partner with AI Engineers to establish quality standards and testability criteria for RAG pipelines, LLM-as-judge evaluation frameworks, and agentic workflows built on LangGraph and CrewAI
- Lead development of AI evaluation strategies — defining how the organization tests for LLM output accuracy, groundedness, hallucination risk, and clinical safety against oncology-specific test cases
- Oversee quality validation for multi-step agentic workflows that orchestrate across EHR data (FHIR/HL7), LLM providers (Claude, GPT, Gemini), and GCP services (Vertex AI, Cloud Run, GKE)
- Build repeatable processes for monitoring and regression testing AI feature behavior as models and pipelines evolve
Cross-Functional Partnership
- Embed quality leadership across product squads — from story refinement and design review through release and post-production monitoring
- Collaborate with clinical stakeholders to deeply understand oncology workflows, high-risk edge cases, and the downstream impact of quality failures on care teams and patients
- Work with AI Engineers, data engineers, and infrastructure teams to ensure quality considerations are built into GCP architecture and data pipeline design
- Drive adoption of shift-left quality practices across the Engineering organization
Release Quality & Risk Management
- Own the release quality process — defining go/no-go criteria, leading test completion reviews, and managing risk sign-off for production deployments
- Produce executive-level quality reports covering test status, defect trends, automation ROI, and release readiness
- Establish and maintain QA documentation, runbooks, and compliance-relevant test records appropriate for a HIPAA-regulated environment
Required Qualifications
- 7+ years of experience in software QA, with at least 3 years in a QA management or lead role
- Proven track record building or scaling a QA function — hiring, mentoring, and leading QA teams
- Deep hands-on expertise with test automation frameworks using Playwright and/or Selenium WebDriver
- Strong JavaScript or TypeScript skills — able to architect, review, and guide test code at a team level
- Experience defining and owning end-to-end test strategy across web, API, and integration layers
- Demonstrated ability to partner with engineering leadership and influence quality culture across an organization
- Experience integrating automated testing into CI/CD pipelines and enforcing quality gates in a DevOps environment
- Excellent communication skills — able to translate technical quality metrics into clear, actionable narrative for engineering and business stakeholders
Preferred Qualifications
- Experience establishing quality frameworks for AI-powered or LLM-driven product features — RAG pipelines, agentic workflows, or LLM output evaluation
- Background in healthcare or regulated software environments (HIPAA, EHR integrations, FHIR/HL7 familiarity a strong plus)
- Hands-on familiarity with GCP services — Vertex AI, Cloud Run, GKE, or Cloud Build
- Experience with AI evaluation tooling or LLM-as-judge frameworks
- REST API testing expertise (Postman or equivalent)
- Familiarity with test management platforms such as Testrail, Zephyr, or Xray
- ISTQB Advanced or equivalent quality engineering certification
- Genuine interest in oncology and an appreciation for the operational complexity of clinical environments
Why Oncologic AI
- Build and lead the QA function from the ground up — rare opportunity to define quality engineering standards at an AI-first company
- Mission-driven work — the quality bar you set directly protects the reliability of tools used by oncology care teams and their patients
- Cutting-edge stack — frontier LLMs, agentic frameworks (LangGraph, CrewAI), and cloud-native GCP infrastructure
- Collaborative, high-ownership engineering culture where your leadership will shape the product
- Competitive base salary with equity participation
- Comprehensive medical, dental, and vision benefits
- Flexible hybrid work arrangement based in Orlando, FL
- Professional development budget and access to industry conferences
Pay: $85,000.00 - $100,000.00 per year
Benefits:
- 401(k)
- Dental insurance
- Flexible schedule
- Health insurance
- Paid time off
- Vision insurance
Willingness to travel:
Work Location: Hybrid remote in Orlando, FL 32819
