Receptor.AI

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

About the role
We are looking for a Software Engineer to join our team working on AI-powered pharma industry products. You will work across two core platform products — building backend services, APIs, and AI-powered features, and shipping the frontend side of your work in React + TypeScript. You will work closely with the product owner, frontend developers, QA, DevOps, and biologists.

Must-haves

• A completed university degree in Computer Science, Software Engineering, or a related technical field (Bachelor’s or higher).
• Strong algorithmic thinking — solid data-structures foundation, able to reason about complexity and design correct, efficient solutions rather than only wiring frameworks.
• ~4+ years of production experience building backend services in Python (FastAPI or a similar async framework) with PostgreSQL / SQLAlchemy.
• Hands-on experience with AI-assisted / agentic development (Claude Code or similar) — and the ability to configure and tune these tools, not just use them out of the box; you can explain where you trust them and how you verify their output.
• Practical experience with MCP (Model Context Protocol) and RAG (retrieval-augmented generation, embeddings, vector search).
• Comfortable shipping features in React + TypeScript — you can build and wire up UI without a frontend engineer holding your hand.
• Solid with Docker and Git-based workflows; understand how services run in Kubernetes.

• English(B1-B2) sufficient to work in an English-language codebase and team.

   

Tech Stack

• Core: Python 3.10+, FastAPI, async/await, SQLAlchemy 2.0, Alembic
• Frontend: React 18, TypeScript, Vite
• Database: PostgreSQL, DuckDB, vector search (embeddings)
• AI/LLM: Claude Code, MCP (Model Context Protocol), RAG pipelines
• Infrastructure: Docker, Kubernetes, Redis (FastStream)
• Storage: Parquet
• Orchestration: Temporal, Argo (nice to have)
• CI/CD: GitOps, ArgoCD, GitLab CI
• Observability: structured logging, metrics, distributed tracing

• Tools: Git, Jira, Confluence

   

Nice-to-have (not required)

• Experience with workflow orchestration (Temporal, Argo, Airflow) or message-driven architectures.
• Building your own MCP servers, or productionising LLM apps (evals, guardrails, cost/latency tuning).
• Data/analytics tooling (DuckDB, Parquet) or a scientific/chemistry background.

• GitOps / CI experience (ArgoCD, GitLab CI).

   

What you’ll do

• Design and ship async REST APIs in FastAPI so researchers can run screening, manage projects, and explore results.
• Integrate backend services with our compute-orchestration layer so long-running docking and screening jobs are durable and observable.
• Drive features with an AI-first workflow: plan and implement with Claude Code, then critically review, test, and verify what the agent produces before it ships.
• Model and evolve the PostgreSQL schema (SQLAlchemy 2.0, Alembic), shipping safe migrations as the product grows — with the team’s support on the trickier zero-downtime changes.
• Build and integrate the AI layer — MCP tools and RAG pipelines (embeddings, vector search) that power our in-product AI Agent — with evals, guardrails, and retrieval quality treated as part of the feature, because the agent informs scientific decisions.
• Build async, event-driven services: Redis/FastStream for lightweight service-to-service events
• Ship the frontend side of your features in React 18 + TypeScript (Vite) — wiring UI to the APIs you build
• Keep your services observable and reliable in production: structured logging, metrics, and distributed tracing across async services and Temporal workflows
• Review peers’ code — including AI-generated code — as part of how the team keeps quality high.
   

Domain knowledge 

• Basic understanding of molecular biology — proteins, DNA/RNA, small molecules
• 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
• Ability to work independently and manage own workload