<h3>Who we are</h3>
Helsing is a defence AI company. Our mission is to protect our democracies. We aim to achieve technological leadership, so that open societies can continue to make sovereign decisions and control their ethical standards.

AI Research Engineer - Reinforcement Learning

Imagine an ancient oracle, a cavernous chamber where any question posed is met with a perfect, instantaneous, and utterly truthful answer. For centuries, supplicants have journeyed to its steps, seeking solutions to their most vexing problems. Yet, history does not remember the oracle for the answers it gave. It remembers the few who, through the sheer quality of their inquiry, received a truth so profound it reshaped their world. The rest received only facts, correct yet sterile, leaving them no wiser than before.
Today, we have built this oracle. We call it Artificial Intelligence. It is an infinite archive, a boundless repository of answers waiting for a summons. In our haste to celebrate this achievement, we have overlooked the timeless lesson of the cavern: the power lies not in the oracle, but in the one who asks. In an age of ubiquitous answers, the art of asking a beautiful question has become the most critical, and most undervalued, of all human skills.
<h3>The Anatomy of a Beautiful Question</h3>
What, then, separates a mundane query from a beautiful one? It is not a matter of complexity, but of character. A beautiful question cannot be answered with a simple fact. It is a key, crafted to unlock a door into a new room of understanding.
<ul>
<li>It Challenges Hidden Assumptions. A beautiful question does not operate within the existing frame; it questions the frame itself. While a common question asks, "How can we build a better mousetrap?" a beautiful question asks, "Why are we so certain we have a mouse problem?" It forces us to confront the bedrock of our beliefs, the invisible scripts that govern our thinking.</li>
<li>It Creates New Territory. A beautiful question is generative. It does not seek a destination on a known map; it suggests that a new map is possible. When Einstein asked, "What would I see if I rode on a beam of light?" he was not asking for a fact. He was opening a portal to an entirely new universe of physics. Such questions are the seeds of paradigm shifts.</li>
<li>It is Humble and Brave. To ask a beautiful question requires a profound intellectual humility—an admission that our current view is incomplete. It is an act of vulnerability that requires immense courage. It says, "I am willing to be wrong. I am willing to be lost for a time, in the service of a deeper truth." It is this quality that separates the true innovator from the mere technician.</li>
</ul>
<h3>Application: Putting the Oracle to Work</h3>
This is not an abstract philosophical exercise; it is a practical tool for improving your life. By shifting from seeking answers to crafting questions, you can directly enhance your Health, Wealth, and Relationships.
<ul>
<li>Health &amp; Mental Resilience: We often torment ourselves with unhelpful questions. We ask, "Why am I so stressed?" and receive a laundry list of our anxieties. A more beautiful question is, "What is the hidden assumption in how I've structured my day that is creating this friction?" This shifts the focus from a state of being (stressed) to a system that can be redesigned, giving you agency over your own well-being.
</li>
<li>Wealth &amp; Career Moat: The common career question is, "What skill should I learn next?" This is a race you will lose to AI. A more beautiful, strategic question is, "What is a valuable problem in my industry that no one is asking the right questions about?" The answer to this question is where your true, defensible value lies. It moves you from a replaceable technician to an indispensable strategist.
</li>
<li>Relationships &amp; Deeper Connection: In our interactions, we default to superficial queries: "How was your day?" The response is almost always "Fine." A beautiful question creates an opportunity for genuine connection. Try asking, "What was a moment today that challenged your perspective?" or "What was the most meaningful interaction you had today?" These questions invite introspection and vulnerability, the twin pillars of any strong relationship.
</li>
</ul>
<h3>The Final Reflection: Your Turn to Ask</h3>
The AI oracle is here to stay. Its voice will grow louder, its answers more comprehensive. We can choose to be its passive supplicants, endlessly asking for trivial facts and receiving trivial truths in return. Or we can choose to become its master, to be the thoughtful inquirer who, through the elegance and wisdom of our questions, elicits a response that elevates us all.
The future does not belong to those who know. It belongs to those who ask. The quality of your life, your business, and your civilization will depend not on the power of our machines to answer, but on our courage and creativity to ask the truly beautiful questions.
So, here is your first exercise. For one week, listen for the questions you ask yourself and others. Are they designed to confirm what you already know, or to explore what you don't? At the end of the week, ask yourself the most important question of all: What one beautiful question, if I pursued it, would change everything?

Echoes in the Oracle: The Timeless Art of Asking the Right Question

For centuries, the great explorers and cartographers did not just wander into the unknown; they meticulously mapped it. They gathered data from distant shores, analyzed the patterns of stars, and synthesized this information into charts that transformed terrifying voids into navigable routes. Their maps did not predict the future, but they created the conditions for successful journeys.
Today, we all stand at the edge of a vast, uncharted professional landscape. The terrain of our careers is being reshaped by forces as powerful as any tectonic shift, chief among them Artificial Intelligence. The old maps—the linear career paths and stable job descriptions of the 20th century—are now dangerously obsolete. To navigate this new world, we must stop acting like passengers and become the cartographers of our own careers.
This isn't a metaphor; it's a mental model. Career Cartography is the discipline of using modern tools and timeless strategic principles to proactively map your professional future. It’s a shift from reacting to the job market to making the job market react to you.
<h3>The Framework: The Three Stages of Career Cartography</h3>
Just as a mapmaker cannot chart a continent in a single step, we cannot design our careers in one sitting. The process is iterative and built on three core practices.
1. Terrain Analysis: From Job Trends to Skill Trajectories
A traditional map shows you where things are now. A cartographer’s map shows you the underlying terrain—the mountains, rivers, and currents that dictate why things are where they are. In career terms, this means moving beyond lagging indicators like popular job titles and focusing on leading indicators: skill trajectories.
<ul>
<li>The Action: Use AI-powered tools and platforms not just to search for jobs, but to analyze the underlying skills requested in those jobs. Look for patterns. Which skills are consistently bundled together? Which ones are appearing in senior roles now that were in junior roles 18 months ago? This is the data that reveals the “high ground” of the future job market. You are no longer asking, “What jobs are available?” but rather, “What skills will create valuable jobs in three to five years?”</li>
</ul>
2. Route Planning: Your Personal Learning Roadmap
Once you understand the terrain, you can plot your route. A reactive professional learns what their company tells them to learn. A career cartographer designs a personalized learning roadmap based on their analysis of the terrain and their own unique destination.
<ul>
<li>The Action: Based on your Terrain Analysis, identify the one or two key “choke points” or “mountain passes” on the map—the skills that, if acquired, will unlock multiple future paths. This is not about collecting dozens of random certifications. It is about making a few, highly leveraged bets on skills that will form the core of your future value. Your roadmap should be a living document, reviewed quarterly, that guides your projects, your reading, and your professional development.</li>
</ul>
3. Identifying Harbors: Engineering Your Discovery
Ancient explorers knew that a successful voyage required safe harbors. In career terms, these harbors are the organizations and teams where your unique skills will be most valued. The crucial insight is that these organizations are also building maps—they are using proactive AI to find you before you find them.
<ul>
<li>The Action: You must make your skills legible to these systems. This means translating your experience into the language of the machine. Document your projects not just by their outcomes, but by the specific, in-demand skills you applied. Contribute to open-source projects, publish analyses, and build a body of work that acts as a beacon. You are not just applying for jobs; you are creating a data trail that leads the best opportunities directly to your door.</li>
</ul>
<h3>Application: Charting a Course for Health and Wealth</h3>
This framework is not just about career advancement; it is a strategy for a more resilient and fulfilling life.
<ul>
<li>Wealth: By focusing on future skill trajectories, you are building a defensible career moat. You are investing in assets that will appreciate in value, insulating yourself from the commoditizing force of automation and securing your long-term financial health.</li>
<li>Health: The greatest source of professional anxiety is uncertainty. A map does not eliminate the storms of a changing world, but it provides a sense of direction and control. Career Cartography replaces frantic, reactive scrambling with calm, proactive planning, significantly improving your mental and psychological well-being.</li>
</ul>
<h3>Your First Expedition</h3>
Stop looking at job boards as a catalog of what is. Start looking at them as a dataset of what will be. This week, take one hour. Don’t search for a job. Search for a pattern. Pick a role you aspire to and analyze the skills listed in ten different postings. What is the hidden architecture of your industry’s future? What is the first point you will plot on your own map?

Career Cartography: Mapping Your Professional Future in the Age of AI

For the last two decades, you have been given a single playbook for career success: accumulation. You were told to build a portfolio of skills, a checklist of competencies stacked one on top of the other. Python, data analysis, project management, digital marketing—each a brick in the wall of your professional fortress.
This playbook is now obsolete.
Artificial intelligence is not just another tool; it is a force of devaluation. It can acquire technical skills with near-instantaneous speed, rendering entire categories of human expertise into commoditized, low-value assets. The career model based on a static portfolio of accumulated skills is not just fragile; it is a strategic liability.
It is time for a new playbook. It is time to stop thinking about the skills you have and start focusing on the value you can create that AI cannot. This requires a radical shift in perspective, one inspired by the venture capitalists who obsess over their "anti-portfolio"—the list of transformative companies they failed to invest in.
Your career anti-portfolio is the collection of high-value, uniquely human problems you are choosing not to solve every time you focus on a task that can be automated. It is the opportunity cost of clinging to the old playbook.
Building a resilient career in the age of AI is no longer about the skills you list on your resume. It is about consciously designing and defending a professional "moat"—a defensible space built around a class of problems that are structurally resistant to automation. This moat is not built with the bricks of individual skills, but with three powerful, interconnected pillars: Creative Synthesis, Deep Empathy, and Strategic Unlearning.
<h3>Pillar 1: Creative Synthesis</h3>
A logistics firm was struggling with crippling inefficiencies in its urban delivery network. Their team of data scientists had optimized routes, modeled traffic patterns, and squeezed every ounce of performance from the data. Yet, the breakthrough came not from a data scientist, but from a manager with a background in biology. She looked at the delivery network not as a series of nodes and vectors, but as an ecosystem. She asked, "How do ant colonies solve this? How do mycelial networks transport nutrients?" By applying principles from ecology—redundancy, decentralized decision-making, and swarm behavior—she introduced a new logic that was less efficient on paper but infinitely more resilient in reality. That is Creative Synthesis: the ability to connect disparate fields and import powerful analogies to solve novel problems. AI can optimize within a known domain; it cannot make the intuitive leap to a new one. This practice of borrowing and reapplying mental models is a core tenet of <a href="/articles/the-polymaths-secret-analogical-thinking">the polymath's secret</a>.
<h3>Pillar 2: Deep Empathy</h3>
In a high-stakes negotiation between two companies, the data was clear. The AI-powered models had run every scenario, analyzed every financial statement, and produced a single, optimal deal structure. It was efficient, logical, and mathematically sound. Yet, the deal was on the verge of collapse. The lead negotiator, an experienced veteran, paused the discussion. She walked over to an empty chair at the table and said, "Let's consider the person who isn't in this room: the junior engineer who will have to implement this solution. What is their reality? What are their frustrations?" In that moment, she did something no algorithm could. She introduced a variable that wasn't in the dataset: the unspoken, human reality of implementation. She bridged the empathy gap. The conversation shifted from optimizing financial outcomes to designing a solution that people could actually believe in. This is Deep Empathy: the work that remains when the spreadsheets have been perfected.
<h3>Pillar 3: Strategic Unlearning</h3>
A legendary software architect, known for building elegant, indestructible systems, was tasked with leading a new rapid-prototyping team. Her instincts, honed over decades, were to plan, structure, and build for permanence. But the team was failing, bogged down in her meticulous processes. After a week of frustration, she called a halt. "Everything I know about building perfect software is wrong for this project," she announced. "Our goal is not to build a cathedral; it is to build a series of tents. Speed and feedback are our new architecture." She had to consciously and deliberately unlearn the habits that had made her successful to adapt to a new context. This is Strategic Unlearning: the ability to recognize when your own expertise is a liability and have the humility to abandon it. AI can learn, but it cannot unlearn. It cannot choose to forget its programming.
<h3>Application: Building Your Moat for Life</h3>
<ul>
<li>Wealth: Your moat is your economic security. By focusing on problems of synthesis, empathy, and unlearning, you move from being a commodity (a skilled technician) to a strategic asset (a trusted problem-solver), making your value durable and defensible.</li>
<li>Health: The anxiety of modern work comes from the endless race to keep up. Building a moat frees you from this. It provides a deep sense of professional security and reduces the stress that comes from feeling replaceable, directly contributing to your mental and psychological health.</li>
<li>Relationships: Trust is the currency of all valuable relationships. The ability to understand a colleague’s unspoken frustration (empathy) or to connect ideas in a way that clarifies a complex problem for your team (synthesis) builds immense professional capital and influence.</li>
</ul>
<h3>Your First Step</h3>
Stop auditing your skills. Start auditing your problems. Ask yourself: What percentage of my work in the last month could have been done by a sophisticated AI? Of the remaining work, how much falls into Synthesis, Empathy, or Unlearning? What high-value, moat-protected problems am I not solving because I am busy with automatable tasks?
This week, take one concrete step to shift 10% of my focus from my "skill portfolio" to my "professional moat." The age of collecting bricks is over. It is time to start digging.

AI Research Engineer - Reinforcement Learning

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