QA Engineer

About Prudentia Sciences

Prudentia Sciences is an AI-powered technology platform transforming how biopharma, biotech, and life sciences investors approach portfolio management, due diligence, and value/risk simulation. Our platform accelerates investment in breakthrough therapies by:

• Empowering biopharma to accelerate drug pipelines, maximize ROI, and achieve clinical and commercial success for greater patient impact
• Enabling strategic positioning of asset value during portfolio planning and deal making
• Equipping investors with data-driven insights to optimize capital allocation in drug asset transactions

• Empowering biopharma to accelerate drug pipelines, maximize ROI, and achieve clinical and commercial success for greater patient impact
• Enabling strategic positioning of asset value during portfolio planning and deal making
• Equipping investors with data-driven insights to optimize capital allocation in drug asset transactions

Backed by GV (Google Ventures), McKesson, Signal Fire, Iaso Ventures, and Virtue, we're on a mission to unlock the full potential of pharmaceutical R&D by empowering decision-makers with real-time, data-driven insights.

We're seeking a detail-oriented QA Engineer to help ensure the quality, reliability, and accuracy of our platform as we scale. This is a rare opportunity to join a high-impact technology company in a major growth stage, where you'll partner closely with engineering, product, and ML teams to safeguard the trust our pharma and biopharma customers place in our platform. Our platform is quickly seeing exceptional demand in its sector, and is backed by top-tier life science investors who have an informed perspective on where AI can have a profound impact in the industry.

• Test Strategy & Planning:
  Design and own the test strategy for our web platform, covering functional, regression, integration, and end-to-end testing across deal pipelines, document ingestion, and LLM-driven insight delivery.
• Test Automation:
  Build and maintain automated test suites (UI, API, and data pipeline) to enable fast, confident releases as the platform and engineering team scale.
• Manual & Exploratory Testing:
  Conduct hands-on exploratory testing of complex, data-rich workflows to catch edge cases automation alone won't surface.
• Quality Gates in CI/CD:
  Partner with engineering to embed automated testing into CI/CD pipelines, establishing clear quality gates before code reaches staging and production. You should be ruthless at eliminating flaky or long-running tests from CI gates too.
• Bug Tracking & Triage:
  Identify, document, and triage defects with clear reproduction steps; work with engineers to prioritize fixes and verify resolutions.
• Data & Output Validation:
  Validate the accuracy and consistency of ML-driven outputs (risk analyses, valuations, scientific assessments) as they move from the orchestration layer into user-facing views.
• Performance & Reliability Testing:
  Support load, performance, and reliability testing to ensure the platform meets the availability and responsiveness expectations of deal teams working with sensitive, time-sensitive data.
• Security & Compliance Awareness:
  Help verify secure handling of sensitive scientific and deal data, and support testing practices aligned with best practices for healthcare and life sciences data.
• Collaboration & Process:
  Work cross-functionally with engineers, product leads, and domain experts to translate scientific and business requirements into clear, testable acceptance criteria.
• Continuous Improvement:
  Champion a quality-first culture>
  Advocating for testability in design reviews, improving test coverage over time, and staying current on modern QA tooling and practices.