QuoIntelligence

The Opportunity You'll own the full data stack for cybersecurity intelligence at QuoIntelligence, building and evolving the pipelines that power our Mercury platform and analyst workflows. This is a role where your architectural decisions have real, visible impact, and where you'll collaborate closely with a small, expert team of two engineers on the data side. Our platform runs on DigitalOcean with a custom-built infrastructure: ZMQ message queues, Docker containers, hand-rolled deployment pipelines, and in-house-maintained Python libraries. It was built before today's managed services existed, and it works because engineers who genuinely understood distributed systems built it from the ground up. The first major project is leading our migration to AWS, transitioning from this custom infrastructure to managed services, without disrupting production. It's the kind of challenge that requires both depth and adaptability: keeping what exists healthy while architecting what comes next. If you thrive in that kind of dual mandate, this role was designed for you. You'll Thrive Here If... You enjoy working across the full stack rather than specializing deeply in one layer. You're energised by inheriting a well-built yet unconventional system and improving it. You're comfortable with ambiguity and find it motivating rather than frustrating. And you want your work to matter not just to a ticket queue, but to the analysts and products that rely on it every day. This is probably not the right fit if your data engineering experience is entirely on managed platforms like Databricks, Snowflake, or BigQuery, or if you're looking for a fully provisioned cloud environment from day one. The AWS migration is the destination you'll help us get there. One more thing: this is an AI-native company. Our products run on AI. We expect engineering to run on it, too. If your relationship with AI stops at asking ChatGPT to explain error messages, this isn't the right fit. What You'll Do - Build and design data pipelines for ingestion, processing, and modeling of cybersecurity intelligence that feeds Mercury and analyst workflows - Partner with Threat Intelligence Engineers on data access patterns, tool integration, and evolving data sources as priorities shift - Shape the team's technical direction: with two engineers, your judgment carries weight - Keep the existing DigitalOcean platform running: manage containers, handle library updates, and debug custom services with confidence - Lead the AWS migration: plan and execute the transition to managed services, with measurable benchmarks at every phase AI-First in Data Engineering AI is an operating principle here. We use AI tools as a core part of how we work. On a lean team maintaining hand-built infrastructure, they're what let us move at the pace of a much larger engineering org. AI as an operating system. You use Copilot, Claude, Cursor (or equivalents) daily to navigate Go services you didn't write, debug custom ZMQ queues, and generate Docker configurations as part of your daily workflow. Evaluate, Integrate, Repeat. Not every AI-generated suggestion belongs in a pipeline processing cybersecurity intelligence data. You test tools against QI's actual problems (migrating custom services to AWS, parsing threat feed data, maintaining manually versioned Python libraries) and drop what doesn't hold up in production. Define success first. Every pipeline feeding Mercury has defined criteria before it ships: ingestion throughput, data freshness, and error rates. We run the AWS migration on measurable benchmarks at each phase, not just "it works on staging." AI accelerates the testing loop, but the loop needs a target. What You'll Bring Must-haves: - Python as your primary language for data engineering work - A solid foundation in pipeline design, you can reason about data from ingestion through modeling - Docker fluency: deploying, debugging, and managing containerized services is routine for you - Experience building custom solutions when no off-the-shelf solution fits, writing the infrastructure yourself, not just assembling managed services - Cloud platform experience (AWS preferred, GCP, or Azure also relevant): enough to architect the target state of a managed-services migration - Microservices architecture or distributed systems: you've designed or maintained service-oriented systems - Working familiarity with Go: you'll read, debug, and modify Go code in some production services. Side projects and CLI tools count; we expect a ramp-up period You'll also need professional proficiency in English (the team works across countries). Nice-to-Haves: - Redis or ZMQ experience for message queuing - Legacy system maintenance: keeping aging infrastructure healthy while building its replacement - Cybersecurity or threat intelligence background Recruitment Process We aim to be as transparent as possible throughout the process and will share updates with you whenever we have progress on our end. To manage your expectations transparently, we have structured the recruitment process as follows: 1. Recruiter Screen 2. Take-Home Assignment 3. Technical Interview 4. Culture Add Interview 5. Offer & 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 the Role Most companies in DACH have never bought Threat Intelligence. They know the threat landscape is getting worse. They know NIS2 and DORA compliance deadlines are approaching. But they haven't acted because no one has shown them how intelligence translates into lower financial risk. QuoIntelligence sells Unified Risk Intelligence: Threat Intelligence, Digital Risk Protection, and Risk Intelligence delivered through our Mercury platform and Agent Karla, an AI-powered analyst. You already know the buyer. You've sold cybersecurity to CISOs across DACH for years. What you haven't had is a regulatory window this wide, a product this differentiated, and a territory this open. If you want to own DACH at a company where your deals define the playbook, keep reading. If you're looking for an established sales machine, this is the wrong seat. What You'll Do - Own DACH market, end-to-end. Your CISO network is your first pipeline. You generate at least 50% of your opportunities through your own relationships and prospecting. NIS2 gave 30,000 companies a reason to take your call; you know how to turn that into urgency. SDR support exists, but you drive your territory: who to target, how to sequence, when to bring in partners. - Run complex sales cycles. Typical deal: CISO as champion, CFO as economic buyer, 5 stakeholders total. You coach the champion to build the internal business case and translate threat intelligence into risk-loss avoidance and compliance ROI. - Co-sell with partners. Work alongside DACH resellers and system integrators to reach accounts you can't crack alone. Position our intelligence as the oversight layer for mid-market and enterprise companies managing outsourced security. - Maintain pipeline discipline. Keep HubSpot clean: deal stages, activity notes, forecasts, all current. Maintain 3-4x pipeline coverage. Use LinkedIn and sales tools for account research and multi-threading across buying committees - Get coached and grow. Participate in sales coaching, call reviews, and methodology training. The CRO is building this team to win; you'll be expected to absorb feedback and iterate fast AI-First in Sales AI is an operating principle here. The products are built on it; the sales process should reflect that. AI as your operating system. You use AI tools daily for account research, proposal drafting, call preparation, and competitive analysis. This is how you work, not something you add when convenient. Product fluency, not slide decks. Mercury and Agent Karla are AI-driven intelligence products. You need to run the first 30 minutes of a prospect call without SE support. That means understanding what the AI does, where it's strong, and where it isn't. Responsible positioning. We sell to government agencies and regulated financial institutions. In a market full of AI hype, accurate claims are a competitive advantage. You protect that. What You'll Bring Must-haves: - Native or near-native German and fluent English (English is the internal company language) - An existing CISO network in DACH, built over years of selling into the cybersecurity ecosystem. Day-one relationships, not a plan to build them - Cybersecurity vertical experience: CTI, DRP, GRC, or closely adjacent. You can explain why a company needs proactive intelligence in a conversation with both a CISO and a CFO. - HubSpot and LinkedIn proficiency (you use them daily, not reluctantly) - Coachable, resilient, and hungry. You'll be creating a category in a startup, not running an existing playbook - AI fluency beyond basic ChatGPT usage: you actively use AI tools in your sales process and can explain how Nice-to-haves - DACH partner ecosystem experience: resellers, system integrators, MSPs/MSSPs - Experience at a company under 200 people, where you built a process, not inherited it - Familiarity with MEDDPICC or a similar value-based selling methodology What Success Looks Like Month 1: Territory plan reviewed with the CRO. First qualified meetings booked from your own network. Product fluency at a level where you run prospect calls independently. HubSpot is clean Month 3: Pipeline building toward 3-4x coverage across network-sourced, self-generated, and SDR-supported opportunities. First deals past discovery and into evaluation. Competitive objection handling documented from live deals Month 6: First close(s). Pipeline engine repeatable: your network, outbound, and partner channels are producing consistent opportunities. DACH playbook documented for the team: buyer personas validated, deal progression patterns captured What We Offer - Territory ownership. Full ownership of DACH market. No internal territory splits, no other QI reps in your accounts. You define how we sell here - Product worth selling. Finished intelligence, not raw data feeds. 6+ years of AI-native infrastructure and human analyst curation - Direct access. Small team, CRO access, direct input into commercial strategy. No layers between you and decisions Frequently Asked Questions How experienced do I need to be? This is a mid to senior level AE seat, not an SDR step-up. 3-5+ years closing in cybersecurity or adjacent verticals. You should feel comfortable operating independently from day one. Do I need CTI experience specifically? Cybersecurity vertical experience is required. CTI or DRP product experience is a strong advantage that accelerates ramp, but GRC, endpoint, SIEM, or other cybersecurity sales experience counts if you can credibly engage CISOs on threat intelligence. What's the growth path? You'll be one of the first senior AEs. What that turns into depends on your results and the company's growth. What's certain: you shape the DACH playbook and influence who gets hired next. The Process - Recruiter Screen: Career motivations, role fit, AI fluency. - Team Interview: Meet our CSM who will work alongside you. - CRO Interview: Sales depth, deal walkthroughs, territory planning. - Role-Play / Case Exercise: Live prospecting or discovery scenario. - 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.

AI-first GTM Leadership Why This VP Marketing Role, Why Now QuoIntelligence has the product, the credibility, and a growing base of enterprise clients across Financial Services, Manufacturing, and Critical National Infrastructure. What's missing: a marketing engine. Marketing depertment today is nascent, not established. You'll take what exists and build a full function around it. Strategy, execution, infrastructure, partner programs, and the team to run it all. From the first HubSpot workflow to the first partner co-marketing campaign, you own the function end-to-end. You'll sit alongside the CRO on the revenue leadership team with a clear mandate: turn marketing into a measurable revenue driver in a company where the product already wins when the right buyers see it. One more thing: this is an AI-native company. Our core products, Mercury (intelligence platform) and Agent Karla (AI-powered analyst), run on AI. We expect marketing to run on it too. If your relationship with AI stops at "I've tried ChatGPT," this isn't the right fit. If you've spent years fine-tuning marketing in an established machine, this isn't the right role. If you want to architect one from zero inside a European cybersecurity company with real traction and a regulatory tailwind (NIS2, DORA), keep reading. What You'll Own 1. Demand generation engine Build the engine for a mid-market-to-enterprise sales motion, selling to CISOs and buying committees in regulated industries. Outbound sequences, inbound programs, account-based campaigns: you decide what runs and what gets cut. 2. Intelligence platform as owned media Build a multilingual content and intelligence platform that becomes the go-to resource for risk intelligence professionals across Europe. Think Bloomberg TV for cybersecurity: curated threat briefings, regulatory analysis, original research, and video content published through an owned channel that drives top-of-funnel awareness and positions QuoIntelligence as the European category authority. The platform feeds brand, demand gen, and product adoption simultaneously. You'll define what this looks like, build the first version, and measure whether it earns the audience's attention. 3. Brand, positioning, and website Define how the market sees QuoIntelligence. ICPs, buyer personas (CISO as the champion, CEO/CFO as the economic buyer), and messaging that positions the company as the European category leader in Unified Risk Intelligence. Lead a full brand and website overhaul with compliance-aware positioning that speaks to regulated buyers. 4. Marketing operations (HubSpot) Architect marketing operations in HubSpot (Marketing Pro + Sales Enterprise). Lifecycle workflows, multi-threaded sequences for long sales cycles, lead scoring, and attribution modeling that give the CRO transparent pipeline contribution data. 5. Partner marketing function Channel partners today are transactional resellers. Your job: enablement assets, co-marketing campaigns, and referral tracking that turn them into co-sellers. 6. Field events and field marketing Plan and run the events that put QuoIntelligence in front of CISOs and security leaders across Europe. Industry conferences (it-sa, Cybertech Europe, RSA), regional roundtables, executive dinners, partner-hosted events. You decide where we show up, what we say when we're there, and how every event converts to pipeline. In a market where trust sells, and buyers want to meet the analysts behind the product, field marketing is a primary demand gen channel, not a brand exercise. 7. Multi-regional coordination Coordinate execution across DACH, UK, and Central Europe with internal and external SDR teams and agencies. Messaging consistency and pipeline velocity across markets. 8. Team building and scaling Hire and scale the marketing team as the function matures. Your first hire is already budgeted. You'll define the org structure, roles, and growth plan from there. What You'll Bring Must-haves: - AI fluency. You actively use AI tools in your marketing work (content, analytics, automation, research) and can evaluate new ones critically. You understand what AI products do well and where they fall short, because you've marketed or worked alongside them. - You've built and scaled B2B demand generation in a company selling to regulated enterprises (cybersecurity, fintech, defense tech, Governance, Risk, and Compliance, or similar): CISOs, buying committees, and long sales cycles. You know what it takes to turn marketing from a cost center into a pipeline contributor. - Marketing automation depth: you can architect lifecycle workflows, lead scoring, and attribution from scratch, not just operate someone else's setup. We run HubSpot (Marketing Pro + Sales Enterprise). - Product marketing instinct. You've taken a complex, technical product and framed it as a business and compliance solution for buyers who aren't engineers. Agent Karla and Mercury are AI-driven intelligence products; marketing them credibly requires genuine technical fluency. You should be able to explain what makes our AI architecture different in a conversation with a CISO. Threat Intelligence, Governance, Risk, Compliance, or adjacent domains. - Multi-regional execution across European markets. You've coordinated agencies, SDR teams, and campaigns across borders without losing messaging coherence. Nice-to-haves: - Direct cybersecurity marketing experience. Not required if you've marketed to regulated buyers in adjacent sectors, but it accelerates ramp-up. - Partner marketing background: you've built co-marketing programs and enabled channel partners to sell, not just resell. AI-First in Marketing AI is an operating principle here. Every workflow you build should reflect that. AI as an operating system. You'll use AI tools to generate content, analyze pipeline data, build campaigns, and scale what a lean team can produce. We expect you to evaluate, adopt, and integrate AI into marketing workflows as a core competency, not a side experiment. Evaluate, Integrate, Repeat. You don't wait for someone to hand you a tool. You test new AI capabilities against your actual marketing problems, adopt what delivers, and kill what doesn't. You have opinions about which tools work and why, grounded in your own usage. Define Success First. Attribution, pipeline contribution, and CAC are how we decide what keeps running and what gets cut. Every program has success criteria before it launches. AI helps you measure deeper and faster, but the discipline of defining success comes first. Responsible AI. We sell to regulated industries. Our AI claims are accurate, our compliance positioning is defensible, and our content doesn't promise what the product can't deliver. In a market full of AI hype, our credibility is a competitive advantage. You protect it. What Good Looks Like in 90 Days Day 1-30: Audit and Quick Wins - Full audit of HubSpot setup, website, SEO, LinkedIn Ads, and outbound sequences - ICPs, buyer personas, and messaging framework defined and aligned with CRO - KPI and reporting templates established with sales leadership - HubSpot hygiene: lifecycle stages, lead routing, basic intent-triggered workflows - SDR messaging and sequence templates standardized across agencies Day 31-60: Demand Generation Rebuild - Redesigned outbound sequences and nurture workflows targeting CISOs and economic buyers - LinkedIn Ads optimized with account-based targeting and compliance-focused creatives - Website rebuild brief delivered: structure, messaging, conversion strategy - Partner enablement pack v1: co-marketing assets and referral tracking in HubSpot - Weekly pipeline review cadence running with sales and SDR teams Day 61-90: Scale and Optimize - Website rebuild or major refresh executed with CRO and product input - Advanced intent scoring and multi-channel triggers live in HubSpot - Pilot partner co-marketing campaign launched and measured - Attribution, ROI, and CAC reporting framework delivering transparent pipeline data - Quarterly marketing operating cadence established: planning, budget, performance reviews What We Offer - Build a marketing function in a company with product-market fit, a growing client base, and a €1.4M ENISA contract as a credibility anchor. - Executive seat at the table. Direct CRO partnership, revenue leadership team membership, and influence over commercial strategy. Your decisions shape how the company goes to market. - A ~40-person team where your work is visible and consequential. This is pre-Series A: early enough to shape the company, stable enough to build on. - Remote-first, European footprint. A culture built on six values that actually drive behavior: Trust, Sleek, Curiosity, Lean Strength, Impact, and Playful Teamwork. - Meaningful product. Mercury and Agent Karla are real AI-native intelligence products, not dashboards with a chatbot bolted on. Clients include Financial Services, Defense, Energy, and Government. Frequently Asked Questions - Is this a player-coach role? Yes, for the first 6-12 months. You'll execute hands-on (HubSpot workflows, campaign launches, LinkedIn Ads) while building the strategy. After that, you shift toward leadership, strategy, and cross-functional coordination. - What marketing budget is available? You'll have a budget for tools, agencies, LinkedIn Ads, and events. The exact number is set during planning with the CRO. Expect to justify spending with pipeline data, not slide decks. We operate with a lean-strength mindset: enterprise-grade results on a focused budget. - How does marketing work with sales today? The CRO leads the revenue org (sales, SDR, RevOps). Marketing today is still in its early stages. You'll define the operating model: pipeline review cadences, lead handoff protocols, and messaging alignment. The SDR teams (internal and external across DACH, UK, and Central Europe) are already in place. - Do I need cybersecurity experience? It helps, but it's not required. What matters is experience marketing complex, technical products to regulated buyers. If you've sold compliance-driven solutions in fintech, defense tech, or Governance, Risk, and Compliance, you'll translate quickly. - What is the recruitment process going to be like? We envision the following fast but detailed process: - Recruiter Screen - CRO Interview (60 min). Strategic depth, demand gen experience, and go-to-market thinking. - AI-Readiness and Case Discussion (45 min). How you use AI in marketing, case walkthrough of a demand gen challenge. - CEO 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.

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. - Python: It's the team's language. - Open-source mindset. You've worked with Hugging Face, spaCy, OpenNMT, or similar. If your entire career has been inside proprietary ecosystems with no exposure to open-source equivalents, that's 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 you're 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. You'll 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 EU's cybersecurity agency) partner. We're upfront about what our AI can and can't do, and our compliance record backs it up. In a market full of AI hype, that credibility is a real advantage. Growth trajectory. Pre-Series A, revenue growing fast, Series A planning underway. You'd 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 team's work. You can use AI tools, but you'll 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.

Role Description Mercury 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 (60% of your time). - Build and maintain basic frontend features in React and TypeScript. - Write and maintain tests at every level: unit, integration, end-to-end (Playwright or similar). - Own CI/CD pipelines, code quality tooling, and static analysis. - Work with data: SQL queries, data modeling, domain modeling, and visualization. - Collaborate across teams, asking for help when stuck and proactively helping others. AI-First in Engineering We 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. - Use AI to turn engineering leverage, shorten delivery cycles, and focus on high-value problems. - Strong judgment in using AI, understanding risks, and applying pragmatic safeguards. - Help re-engineer team workflows, build repeatable standards, and better tooling. - Coach teammates less familiar with agentic coding and drive AI adoption. What You'll Bring Must-haves: - AI-assisted development: actively use AI tools (e.g. Cursor) and evaluate their output critically. - Strong Python backend experience: designed and shipped production APIs, web services, and data processing systems. - Working familiarity in React and TypeScript: can build and maintain basic frontend features. - Solid testing discipline: unit, integration, and e2e testing are part of your work. - CI/CD and code quality tooling experience: set up or maintained pipelines. - Data proficiency: SQL, data modeling, and comfort with data analysis and visualization. - Clean code habits: produce readable, maintainable, and extendable code. Nice-to-haves: - Go or additional programming languages. - UX and design principles knowledge. Your First 90 Days - Month 1: By day 30, have a working mental model of Mercury's architecture. - Month 2: Own a significant feature end-to-end and start reshaping something. - Month 3: An engineering practice or system you built is now part of the team's workflow. What We Offer - Full ownership from day one. - Interesting domain: Unified Risk Intelligence solution with an AI-agentic product. - Remote with autonomy: small team, low bureaucracy, high trust. - Shape the engineering culture as the team grows. - AI-native workflow: Cursor is the standard tool. FAQ - How small is the engineering team? Four engineers (including the engineering lead). - Do I need cybersecurity experience? No, strong engineering fundamentals are what matter. - What is the tech stack? Python (backend), React/TypeScript (frontend), Playwright (e2e testing). The Process - Recruiter Interview - AI Fluency Interview - Team Interview - Live Coding - Offer

Role Description QuoIntelligence turns millions of raw signals into finished Cyber Threat Intelligence (CTI) that security teams across Europe act on every day. 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'll Do - Improve the production ML stack. - Own model evaluation end-to-end. - Ship something the stack can't do today. - Expand agent capabilities. - Work directly with the IntelOps team. AI-First in Engineering - You build with AI-assisted tools daily, but you also know when AI-generated code introduces risk. - You evaluate new AI tools critically, adopting what works and dropping what doesn't. - Every model and pipeline has a clear definition of success before it ships. Qualifications - Production ML deployment. - NLP and LLM grounding. - Comfort with messy data. - Python: It's the team's language. - Open-source mindset. - AI fluency. Nice-to-haves - Experience with agent frameworks (LangGraph, LangChain, or similar). - 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. What We Offer - A small team where your work hits customers. - Ownership, not just tickets. - Constraints that force creativity. - Ethical red lines. - Growth trajectory. The Process - Recruiter Screen. - AI fluency screen. - Take-home task: a real evaluation problem from the team's work. - Hiring manager interview. - 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.

Role Description QuoIntelligence has the product, the credibility, and a growing base of enterprise clients across Financial Services, Manufacturing, and Critical National Infrastructure. What's missing: a marketing engine. The marketing department today is nascent, not established. You'll take what exists and build a full function around it. - Strategy, execution, infrastructure, partner programs, and the team to run it all. - From the first HubSpot workflow to the first partner co-marketing campaign, you own the function end-to-end. - You'll sit alongside the CRO on the revenue leadership team with a clear mandate: turn marketing into a measurable revenue driver. - This is an AI-native company. Our core products, Mercury (intelligence platform) and Agent Karla (AI-powered analyst), run on AI. - If your relationship with AI stops at "I've tried ChatGPT," this isn't the right fit. - If you've spent years fine-tuning marketing in an established machine, this isn't the right role. If you want to architect one from zero inside a European cybersecurity company with real traction and a regulatory tailwind (NIS2, DORA), keep reading. What You'll Own - Demand generation engine: Build the engine for a mid-market-to-enterprise sales motion, selling to CISOs and buying committees in regulated industries. - Intelligence platform as owned media: Build a multilingual content and intelligence platform that becomes the go-to resource for risk intelligence professionals across Europe. - Brand, positioning, and website: Define how the market sees QuoIntelligence and lead a full brand and website overhaul. - Marketing operations (HubSpot): Architect marketing operations in HubSpot (Marketing Pro + Sales Enterprise). - Partner marketing function: Enablement assets, co-marketing campaigns, and referral tracking that turn transactional resellers into co-sellers. - Field events and field marketing: Plan and run events that put QuoIntelligence in front of CISOs and security leaders across Europe. - Multi-regional coordination: Coordinate execution across DACH, UK, and Central Europe with internal and external SDR teams and agencies. - Team building and scaling: Hire and scale the marketing team as the function matures. Qualifications - AI fluency: Actively use AI tools in your marketing work and can evaluate new ones critically. - Experience in building and scaling B2B demand generation in a company selling to regulated enterprises. - Marketing automation depth: Ability to architect lifecycle workflows, lead scoring, and attribution from scratch. - Product marketing instinct: Ability to frame complex, technical products as business and compliance solutions. - Multi-regional execution across European markets. Requirements - Direct cybersecurity marketing experience (not required but accelerates ramp-up). - Partner marketing background: Experience in building co-marketing programs and enabling channel partners. What Good Looks Like in 90 Days - Day 1-30: Full audit of HubSpot setup, website, SEO, LinkedIn Ads, and outbound sequences. - Day 31-60: Redesigned outbound sequences and nurture workflows targeting CISOs and economic buyers. - Day 61-90: Website rebuild or major refresh executed with CRO and product input. Benefits - Build a marketing function in a company with product-market fit and a growing client base. - Executive seat at the table with direct CRO partnership and influence over commercial strategy. - A ~40-person team where your work is visible and consequential. - Remote-first, European footprint with a culture built on six values: Trust, Sleek, Curiosity, Lean Strength, Impact, and Playful Teamwork. - Meaningful product: Mercury and Agent Karla are real AI-native intelligence products.