Senior MLAI Engineer (mwd)
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Job Location:
Frankfurt am Main - Germany
Yearly Salary:
m 80000 - 110000
Posted on:
2 hours ago
Vacancies:
1 Vacancy
Job Summary
The Opportunity
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 Youll Do
Improve the production ML stack. The enrichment and classification pipelines work and serve real customers. They were built for speed not longevity so theres room to improve them. Youll 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: youll design the framework the team uses to measure whether models are working in production.
Ship something the stack cant do today. The existing pipelines handle classification and enrichment. What comes next is open. Youll propose your first project in your first quarter build it and measure whether it works.
Expand agent capabilities. Help grow Agent Karlas 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; youll 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 doesnt. 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 Youll Bring
Must-haves:
Production ML deployment. Youve 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.
Python: Its the teams language.
Open-source mindset. Youve worked with Hugging Face spaCy OpenNMT or similar. If your entire career has been inside proprietary ecosystems with no exposure to open-source equivalents thats a blocker.
AI fluency. Active daily use of AI-assisted development tools. You can show something you built or completed using AI not just tell us youre interested.
Nice-to-haves:
Experience with agent frameworks (LangGraph LangChain or similar) and orchestration patterns (ReAct tool-calling multi-agent systems)
Fine-tuning experience with open-source models (Qwen LLaMA Mistral)
Experience with inference servers and popular backends (e.g. NVIDIA Triton vLLM etc)
Familiarity with data orchestration tools (Kestra Airflow Prefect)
Cybersecurity or threat intelligence domain knowledge (genuinely not required; curious ML engineers ramp fast)
What We Offer
A small team where your work hits customers. No layers between your model and the intelligence product that clients rely on.
Ownership not just tickets. Your team lead defines priorities; you own how to solve them. Youll review the full service stack and model portfolio then decide what to change and execute independently.
Constraints that force creativity. We believe in using the simplest solution that gets the job done. We keep things lean and pick tools carefully. The interesting problems here come from making that work: squeezing more out of distilled models and designing pipelines smart enough to ship on the available hardware.
Ethical red lines. QI holds the Cybersecurity Made in Europe label and serves as ENISA (the EUs cybersecurity agency) partner. Were upfront about what our AI can and cant do and our compliance record backs it a market full of AI hype that credibility is a real advantage.
Growth trajectory. Pre-Series A revenue growing fast Series A planning underway. Youd be shaping the ML direction for a company that still has 40 people.
The Process
Recruiter Screen
AI fluency screen
Take-home task: a real evaluation problem from the teams work. You can use AI tools but youll defend your reasoning and choices in the next round.
Hiring manager interview: walk through the task then broader technical and behavioral assessment. A team member may join.
CEO/CTO Interview
Offer and Background Check
We welcome applications regardless of gender nationality ethnic origin religion disability age or sexual identity. Diversity is key to producing high-quality intelligence.
About Company
Founded in Germany in 2020 QuoIntelligence is Europes 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 ph ... View more
