Quality Assurance Engineer
Prudentia Sciences
Quality Assurance Engineer
Prudentia Sciences
AI-driven life sciences due diligence platform
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 dealmaking
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 hiring our first dedicated QA Engineer: a hands-on engineer who writes code, builds quality into our platform from the ground up, and cares as much about performance and reliability as about correctness.
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 Automation: Build and maintain automated test suites (UI, API, and data pipeline) to enable fast, confident releases as the platform and engineering team scale.
AI-Assisted Engineering: Use AI coding tools (like Claude Code) daily to build and maintain tests, and know when to question their output. You understand why the code works, not just that it runs.
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.
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.
Core Qualifications
Education: Bachelor's degree in Computer Science, Software Engineering, or a related technical field (or equivalent practical experience).
QA Experience: Proven experience testing modern web applications and building automated test suites.
Test Automation Tools: Hands-on experience with automated testing frameworks (e.g., Playwright, Cypress) and API testing tools (e.g., pytest, REST-assured, pactflow).
CI/CD Integration: Experience integrating automated tests into CI/CD pipelines (e.g., GitHub Actions) and establishing quality gates.
Technical Fluency: Comfort reading code and working directly with engineers to understand system architecture, APIs, and data flows well enough to design meaningful tests.
Data-Driven Applications: Experience testing applications with complex, data-driven workflows.
Bug Tracking & Documentation: Skilled at writing clear, reproducible bug reports and test plans; comfortable using issue trackers (e.g., Jira, Linear) to manage and communicate quality status.
Collaboration & Product Mindset: Strong communication and collaboration skills; ability to work closely with engineers, product managers, and scientific domain experts to deliver a high-quality user experience.
Soft Skills
Strong problem-solving skills and an analytical mindset.
Meticulous attention to detail and a genuine passion for quality.
Passion for continuous learning and improving testing practices as the platform evolves.
Autonomous, self-starter attitude with a strong sense of ownership.
Excellent communication skills. Able to explain technical issues clearly to non-technical audiences.
Collaborative team player with a desire to build things that truly matter.
Impact: Your work directly enables breakthrough therapies to reach patients faster
Ownership: Build and lead the platform as a foundational engineering leader
Growth: Shape our product experience during a critical scaling phase
Compensation: Competitive salary and equity
Team: Collaborative, mission-driven colleagues passionate about transforming biopharma
We are prioritizing candidates who can be flexibly hybrid twice per week (Tues/Thurs) from our Boston, NYC, or San Francisco office. If we find truly exceptional talent, we may consider fully remote in the United States.
AI-driven life sciences due diligence platform
Prudentia Sciences provides an AI-native platform for due diligence and dealmaking in the life sciences sector, serving biopharma, biotech, and financial buyers such as venture capital and private equity firms. The platform merges artificial intelligence (including large language models and deep learning) with scientific expertise and financial modeling to speed asset evaluation, offering rapid data-room synthesis, real-time valuation simulations, and scenario planning with a human-in-the-loop for expert input. It differentiates itself by tightly integrating AI analytics with domain knowledge and financial modeling to identify risks and optimize trade-offs across time, cost, probability of success, and revenue, enabling faster go/no-go decisions. Its goal is to de-risk investments, shorten deal cycles, and scale the platform globally, expanding into Asia-Pacific and Europe with backing from seed and Series A funding led by GV, Iaso Ventures, Virtue, and McKesson Ventures.
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