QA/AQA Engineer (Middle+)
Summary
Leads QA for an AI-driven drug discovery SaaS platform, building testing processes and ensuring quality across molecular screening, ADMET analysis, and peptide optimization tools—collaborating with developers, product owners, and biologists.
About the role
We are looking for a QA Engineer to join our team working on an AI-powered drug discovery SaaS platform. The platform combines molecular screening, ADMET analysis, peptide optimization, and Datagrok integration. You will work closely with developers, product owner, and biologists to ensure quality across all platform features.
This is not an execution role — it is a leadership role. You will be the first dedicated QA Lead at Receptor.AI, responsible for building QA processes from the ground up, defining testing strategy, and setting quality standards across the platform. We expect you to take full ownership of quality and drive it forward independently.
Must have
3+ years of QA experience in a software product company, with at least 1 year in a lead or senior role
Strong experience with manual testing — functional, regression, smoke, exploratory
Proven experience building or significantly improving QA processes from scratch
Hands-on experience with test automation — Python, Playwright, Pytest
Experience with API testing — Postman, Swagger
Basic understanding of REST API and HTTP
Ability to work autonomously and make decisions without constant guidance
Expeiliarity with Jira or similar issue trackers
Upper-Intermediate English (written)
Tech Stack
Test automation: Python, Playwright, Pytest, Pydantic
API testing: Requests / HTTPX, Postman, Swagger
CI/CD & Reporting: GitLab CI, Allure Reports
Basic SQL knowledge
Experience with Page Object Model architecture
Test Management: QASE, Jira
Domain knowledge (our differentiator)
Basic understanding of molecular biology — proteins, DNA/RNA, small molecules
Understanding of protein structure from primary to quaternary (critical for understanding docking and screening results)
Familiarity with the drug development process — from target identification to clinical trials
Familiarity with concepts: protein, molecule, SMILES, docking, ADMET — or willingness to learn quickly
Ability to distinguish between a UI bug and a scientific/business logic issue
Experience in biotech, pharma, or life sciences software is a strong plus
Soft skills
Attention to detail
Proactive communication — if something is unclear, asks immediately
Comfortable working with scientists and engineers in the same team
Ability to work independently and manage own workload
Will be a plus
Experience testing data-heavy or scientific platforms
Experience with Datagrok or similar analytical tools
Familiarity with drug discovery workflows