ML Engineer

About QuoIntelligence

Founded in Germany in 2020, QuoIntelligence is Europe’s leading provider of Unified Risk Intelligence – a strategic fusion of Threat Intelligence, Digital Risk Protection, and Risk Intelligence services. We enable organizations to proactively identify and mitigate cyber, geopolitical, and physical risks with intelligence tailored to their unique threat landscape.

Unlike traditional feed-based solutions, every client benefits from our analysts' work supported by Agent Karla’s automation and our proprietary Mercury platform, ensuring high-quality intelligence with low operational friction.

Deeply embedded in the European regulatory and operational context, and with legal entities in Germany, Italy, and Spain, QuoIntelligence is the trusted partner to critical infrastructure operators, significant financial institutions, government agencies and enterprises across the EU.

The Opportunity

QuoIntelligence turns millions of raw signals into finished Cyber Threat Intelligence (CTI) that security teams across Europe act on every day. The ML layer is what makes that possible: classification and enrichment today, AI-powered analysis through Agent Karla next.

The ML team is small (2 people today), and the infrastructure is lean. You will own end-to-end production systems, from improving existing NLP pipelines, fine-tuning LLMs, building evaluation frameworks, and orchestrating AI agents in the cyber domain. What you ship, customers see.

What You'll Do

• Improve the production ML stack. The enrichment and classification pipelines work and serve real customers. They were built for speed, not longevity, so there's room to improve them. You'll ship at least one measurable improvement in your first 90 days.
• Own model evaluation end-to-end. Quality metrics, ground truth labeling, offline/online evaluation: you'll design the framework the team uses to measure whether models are working in production.
• Ship something the stack can't do today. The existing pipelines handle classification and enrichment. What comes next is open. You'll propose your first project in your first quarter, build it, and measure whether it works.
• Expand agent capabilities. Help grow Agent Karla's intelligence by building new orchestration patterns and retrieval strategies using open-source frameworks like LangGraph.
• Work directly with the IntelOps team. Your models serve the intelligence operations team; you'll validate performance against real-world threat scenarios, not benchmarks.

AI-First in Engineering

AI fluency is a company-wide standard at QI, not a department initiative. For engineering, three principles define the bar:

• You build with AI-assisted tools daily (Cursor, Claude, whatever makes you faster). But you also know when AI-generated code introduces risk. You can evaluate whether an AI suggestion is reliable in a security-critical codebase, and you understand the difference between shipping fast and shipping recklessly. At a cybersecurity company, that judgment matters more than speed.
• You evaluate new AI tools critically, adopt what works, and drop what doesn't. You have opinions on which tools are good and why, grounded in your own usage, not in what you read on LinkedIn.
• Every model and pipeline has a clear definition of success before it ships. AI accelerates the iteration loop. Without clear success criteria, that speed is wasted.

What You'll Bring

Must-haves:

• Production ML deployment. You've taken models from notebooks to production and maintained them over time, as part of systems that serve real users.
• NLP and LLM grounding. Text classification, NER, summarization, embeddings, transformer-based models. You understand the fundamentals well enough to choose the right approach for a given problem, not just the newest one.
• Comfort with messy data. Unstructured text with noisy, inconsistent signals. If your ML experience is limited to clean benchmark datasets, this role will frustrate you.
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