Ofertes d'ocupació a QuoIntelligence

• Nivell d'experiència---
• Tipus de contracteA temps complert
• PublicadaNova

Teletreball . React API Python TSQL REST TypeScript SaaS LESS

The OpportunityMercury is QuoIntelligence's Threat Intelligence platform: it processes over 2 billion signals, tracks 400+ threat actors, and delivers finished intelligence to security teams across financial services, energy, and government in Europe. You will join a four-person engineering team, working directly with the engineering lead on Mercury and Agent Karla, an AI-powered threat analyst that runs on the Mercury engine.

You will ship new features and pay down tech debt in the same week. The team treats clean, maintainable code as a prerequisite, not a nice-to-have. If you want to build a product where what you ship reaches real users quickly, where you will shape how the engineering team works as it grows, and where the domain is genuinely interesting, keep reading.

What You'll Do

• Design, build, and ship backend services in Python: REST APIs, web services, data processing. This is roughly 60% of your time. The APIs serve both internal teams and external clients, so performance and reliability matter.
• Build and maintain basic frontend features in React and TypeScript. You will work across the full stack, not just behind the API layer.
• Write and maintain tests at every level: unit, integration, end-to-end (Playwright or similar). Testing is a first-class concern, not an afterthought.
• Own CI/CD pipelines, code quality tooling, and static analysis. You will have direct influence over how the team ships code.
• Work with data: SQL queries, data modeling, domain modeling, and visualization. Mercury processes threat intelligence data; understanding how to query and present it well is part of the job.
• Collaborate across teams. The top priority for this hire is someone who asks for help when stuck, proactively helps others, and communicates clearly with non-engineering stakeholders.
  
  

AI-First in EngineeringWe want AI to be part of QuoIntelligence’s engineering operating model. We expect engineers to use AI tools like Cursor by default across design, coding, debugging, testing, and documentation.

This role is not about casually using AI for convenience. It is about using AI to materially increase speed, leanness, and impact. You know how to turn AI into engineering leverage, shorten delivery cycles, and focus your time on the highest-value problems.

We also expect strong judgment. As a cybersecurity company, we move fast, but we do not use AI blindly. You understand the risks of AI-assisted and agentic coding, know how to validate important outputs, and apply pragmatic safeguards where they matter most.

The best candidates do more than use AI well themselves. They help re-engineer how the team works by building repeatable workflows, lightweight standards, and better tooling that make everyone faster. They can coach teammates who are less familiar with agentic coding, and act as positive drivers of AI adoption across the engineering team and beyond.

What You'll BringMust-haves:

• AI-assisted development: you actively use AI tools (e.g. Cursor) in your daily workflow and can evaluate their output critically
• Strong Python backend experience: you have designed and shipped production APIs, web services, and data processing systems
• Working familiarity in React and TypeScript: you can build and maintain basic frontend features, not just read them
• Solid testing discipline: unit, integration, and e2e testing are part of how you work, not something you add when asked
• CI/CD and code quality tooling experience: you have set up or maintained pipelines and care about keeping them healthy
• Data proficiency: SQL, data modeling, and enough comfort with data analysis and visualization to work with intelligence data
• Clean code habits: you produce code that the next person/agent can read, maintain, and extend. You keep things simple.
  
  

Nice-to-haves:

• Go or additional programming languages
• UX and design principles knowledge
  
  

Your First 90 DaysMonth 1: By day 30, you have a working mental model of Mercury's architecture: where the data flows, where the pain points are, and what you would change first.

Month 2: You own a significant feature end-to-end, from API design through React UI to Playwright tests. You have started reshaping something: the CI pipeline, the test strategy, a slow endpoint, the way the team reviews code. Your commits are changing how the product works, not just adding to it.

Month 3: An engineering practice or system that you built is now part of the team's workflow. You are the go-to person for at least one area of the codebase.

What We Offer

• Full ownership from day one. Four engineers, not forty-seven. Everything you ship is visible in the product.
• Interesting domain. Unified Risk Intelligence solution with an AI-agentic product for enterprise clients across Europe, not another generic SaaS dashboard. Mercury processes 2B+ signals and turns them into understandable risks; Agent Karla is a multilingual AI threat analyst built on top of it.
• Remote with autonomy. Small team, low bureaucracy, high trust. Based in Italy or Spain.
• Shape the engineering culture. As the team grows, your practices and decisions become the foundation.
• AI-native workflow. Cursor is the standard tool. AI is part of the daily work, not an experiment.
  
  

FAQHow small is the engineering team? Four engineers (including the engineering lead), with adjacent engineering teams. QI has ~40 people total. You grow by taking on harder problems and a broader scope. The engineers who joined early shaped the systems that the product runs on today.

Do I need cybersecurity experience? No. The domain is interesting, and you will learn it on the job. What matters is strong engineering fundamentals and the ability to pick up new domains quickly. Several QI engineers came from outside of cybersecurity.

What is the tech stack? Python (backend), React/TypeScript (frontend), Playwright (e2e testing). The product is Mercury, a SaaS intelligence platform, and Agent Karla, a conversational agent (based on an agentic infrastructure). Data pipelines and ML tooling are built by adjacent teams.

The Process

• Recruiter Interview
• AI Fluency Interview
• Team Interview
• Live Coding
• Offer