Quality Engineer (SDET)

Prudentia Sciences•San Francisco, CA

1d•$120,000 - $145,000•Hybrid

About The Position

Prudentia Sciences is hiring its first dedicated QA Engineer to build quality into its AI-powered platform for biopharma, biotech, and life sciences investors. This hands-on engineering role will establish and maintain automated testing, with an emphasis on API and data layers, while supporting reliable, secure, and accurate platform behavior as the company scales. The engineer will work closely with engineering, product, and machine-learning teams to protect customer trust and enable confident releases. The role includes owning automated quality gates in CI/CD, API and contract testing, and load, performance, and reliability testing. It also involves validating ML-driven outputs, helping verify secure handling and separation of sensitive customer data, and translating scientific and business requirements into testable acceptance criteria. The successful candidate will help build a quality-first culture by advocating for testability, improving coverage, and continuously refining QA practices. This is a hybrid role requiring onsite work two days per week, Tuesday and Thursday, at one of the U.S. hubs in Boston, New York City, or San Francisco.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience.
Proven experience testing modern web applications and designing and building automated test suites, ideally with ownership of the suites.
Hands-on experience with automated testing frameworks such as Playwright or Cypress and API testing tools such as pytest, REST-assured, or pactflow.
Experience owning automated tests in CI/CD pipelines such as GitHub Actions, including quality gates and flaky-test management.
Daily hands-on use of AI coding tools, such as Claude Code, Cursor, or Copilot, with a practice of verifying their output.
Experience designing load or performance tests and identifying real bottlenecks.
Comfort reading code and working with engineers to understand system architecture, APIs, and data flows well enough to design meaningful tests.
Experience testing applications with complex, data-driven workflows.
Ability to write clear, reproducible bug reports and communicate quality risks and release readiness.
Familiarity with secure data handling and testing in regulated or sensitive-data environments.
Strong communication and collaboration skills, including working with engineers, product managers, and scientific domain experts.

Nice To Haves

Experience in healthcare, life sciences, or biopharma.
Curiosity about life sciences, the drug development process, and dealmaking in the sector.

Responsibilities

Design, build, and maintain automated test suites across UI, API, and data pipelines, with most coverage at the API and data layers.
Use AI coding tools to build and maintain tests, verify their output, and keep sensitive data out of those tools.
Own automated quality gates in CI/CD pipelines, determine what blocks releases, and reduce flaky or long-running tests.
Perform API and contract testing to catch breaking changes between services before production.
Own load, performance, and reliability testing to support availability and responsiveness expectations.
Identify, document, and triage defects; provide clear reproduction steps; collaborate with engineers on prioritization; and make release go/no-go calls.
Validate the accuracy and consistency of ML-driven outputs as they move from the orchestration layer into user-facing views.
Help verify secure handling of sensitive scientific and deal data, including separation of customer data.
Collaborate with engineers, product leads, and domain experts to turn scientific and business requirements into testable acceptance criteria.
Promote quality-first practices, advocate for testability in design reviews, improve test coverage, and stay current on QA tools and practices.