Forward Deployed AI Engineer

<h3><span>Company Description</span></h3><p><span>NestAI is a European AI lab for defence delivering the adaptive operating system for modern battlefield operations. We develop adaptive intelligence for unmanned and command systems: AI that continuously learns from operational data and adapts to changing conditions.</span></p><p><span>NestOS is our open, modular and interoperable platform that enables this at scale and keeps capability evolution under sovereign European control. Founded in 2025 by Peter Sarlin, whose previous company Silo AI became Europe's largest AI acquisition, NestAI brings together over 150 engineers and scientists and partners with leading European defence forces and industry.</span></p><h3><span>Role Description</span></h3><p><span>We are looking for a Forward Deployed AI Engineer to join NestAI. In this role, you will work embedded directly in our customers' environments, sitting at the intersection of advanced machine learning and real-world execution. You will shorten the path from prototype to fielded capability: validating our technology against operational reality, adapting it on the fly to the constraints and data of each deployment, and feeding what you learn back into the core product.</span></p><p><span>This is as much a client-facing role as it is a technical one. You will navigate shifting environments, build trust with operators and customer engineering teams, and take ownership of problems that do not come with a clean spec. You will need to be comfortable being the technical voice in the room with a customer, translating between what they need and what is actually possible, while still being hands-on enough to build, debug, and ship the solution yourself.</span></p><p><span>On the technical side, we're looking for strong AI/ML engineers with real breadth in machine learning - deep learning, decision-making, and increasingly LLM-driven or agentic workflows - who can adapt to whatever a deployment demands. Solid grounding in signal processing, whether from classical methods or modern learned/deep approaches, is particularly valuable given the sensor-heavy, real-time nature of our operating environments, but we don't expect mastery of both AI and signal processing in one person. Squads are built so that complementary backgrounds cover the full stack.</span></p><h3><span>Day-to-day Responsibilities:</span></h3><ul><li><p>Working directly with customer teams to understand operational requirements, constraints, and failure modes, and translating those into concrete technical problems.</p></li><li><p>Designing and implementing algorithms for sequential decision-making, autonomous behavior, or specialized application workflows - drawing on whichever combination of ML, LLMs, control, or signal processing the deployment calls for, often alongside teammates with complementary backgrounds.</p></li><li><p>Adapting and fine-tuning models to perform reliably against real operational data, which is often noisier and more constrained than anything seen in the lab.</p></li><li><p>Rapidly building and iterating on prototypes in the field or in customer development environments, then hardening the ones that prove out for production.</p></li><li><p>Collaborating with our internal platform, embedded, and perception teams to bring field learnings back into NestOS and ensure deployed solutions are supportable long-term.</p></li><li><p>Building lightweight simulations, test harnesses, or data pipelines on-site to validate algorithms before they scale. Adapting state-of-the-art research in deep learning, signal processing, reinforcement learning, or NLP/LLMs into working, production-ready code under real deployment pressure.</p></li></ul><h3><span>Qualifications:</span></h3><ul><li><p><span>Note: we don't expect deep expertise in both AI and signal processing. One strong discipline plus a willingness to grow into the other is enough.</span></p></li><li><p><span>Working knowledge of signal processing: either a strong background in classical DSP (filtering, spectral analysis, estimation) or in modern learned/deep approaches to signal data, with genuine curiosity about the other side.</span></p></li><li><p><span>Strong working knowledge of machine learning theory and common deep learning architectures, with the ability to pick up new subfields (decision-making, LLMs, or signal processing) as the deployment requires.</span></p></li><li><p><span>Proficiency in Python and hands-on exper