Company

NestAI builds the adaptive operating system for modern battlefield operations. Most AI systems are built once, validated at deployment, and expected to stay sufficient after that. That assumption holds until conditions change, and in defence, they always do. Our team develops adaptive intelligence for unmanned and command systems: AI that continuously learns from operational data and adapts to changing conditions, deployed in environments where there is no internet, no GPS, and failure is not an option. NestOS, our open, modular, and interoperable platform, 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 has grown to over 200 engineers and scientists and partners with leading European defence forces and industry. We're ambitious about what we're building and honest with each other. The bar is high, and so are the standards we hold ourselves to. But this is also a place built on humility, trust, and looking after each other. If that sounds like the kind of work you want to do, keep reading.

Role Description

NestAI builds the software and intelligence layer for autonomous defense systems: technology deployed in live operational environments where "it worked in the demo" isn't good enough. We're looking for an AI Engineer (Forward Deployment Engineering) to sit at the center of that mission: embedded directly in customer environments, at the intersection of advanced machine learning and real-world execution.

This is a high-agency role with no clean spec. You'll shorten the distance between prototype and 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. Most of what you encounter here won't have a known answer yet; you'll work it out alongside a small, senior team that has full ownership of the problems in front of it, and a product organization that takes field feedback seriously enough to ship it back into the platform in days, not quarters.

We're looking for strong AI/ML engineers with real breadth across machine learning, deep learning, decision-making, and increasingly LLM-driven or agentic workflows — people 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 — you'll regularly be the strongest voice in the room on one half of a problem and learning from a teammate on the other.

Day-to-Day Responsibilities:

Working directly with customer teams to understand operational requirements, constraints, and failure modes, and translating those into concrete technical problems.
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.
Adapting and fine-tuning models to perform reliably against real operational data, which is often noisier and more constrained than anything you've seen in the lab.
Rapidly building and iterating on prototypes in the field or in customer development environments, then hardening the ones that prove out for production.
Collaborating with our internal platform, embedded, and perception teams to bring field learnings back into NestOS and ensure deployed solutions are supportable long-term.
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.

Qualifications:

Working knowledge of signal processing: either a strong background in classical Digital Signal Processing (filtering, spectral analysis, estimation) or in modern learned/deep approaches to signal data, with genuine curiosity about the other side.
Strong working knowledge of machine learning theory and common deep learning architectures, with the ability to pick up new subfields (decision-making, reinforcement learning, LLMs, or signal processing) as a deployment requires.
Proficiency in Python and hands-on experience with frameworks such as PyTorch, TensorFlow, and modern LLM tooling (e.g., Ollama, LangChain).
Strong communication skills and genuine comfort engaging with customer or non-ML stakeholders; a consultative mindset is a plus but can be developed on the job with support from the team.
Understanding of data processing pipelines and model deployment basics (predominantly edge-based).
Experience with version control (Git) and Linux-based environments.
Some familiarity with cloud or edge compute environments (e.g., GCP, AWS, Azure, or hardware accelerators).
Willingness to travel to and work in customer environments as needed.

Preferred, not required:

Prior experience in a forward-deployed, solutions engineering, or field applications role.
Previous experience working within or for DefSec (Defense & Security) companies.
Experience building applications with LLMs, agentic workflows, or handling large-scale cloud data infrastructure.
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.

If your background doesn't match every requirement but the work resonates with you, we'd encourage you to apply. How you think, learn, and take ownership matters more to us than ticking every box.

Why join NestAI

At NestAI, you’ll build meaningful, real-world technology as part of a world-class team of engineers, scientists, and experienced professionals. You’ll work on systems where reliability genuinely matters, contribute to Europe’s security and resilience, and solve problems that demand depth, trust, and craftsmanship.

If you want to build with purpose - and with leading experts in the field - you’ve come to the right place.