Software Engineer - Autonomous Air Systems

Who we are

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. 

As democracies, we believe we have a special responsibility to be thoughtful about the development and deployment of powerful technologies like AI. We take this responsibility seriously. 

We are an ambitious and committed team of engineers, AI specialists and customer-facing program managers. We are looking for mission-driven people to join our European teams – and apply their skills to solve the most complex and impactful problems. We embrace an open and transparent culture that welcomes healthy debates on the use of technology in defence, its benefits, and its ethical implications. 

The role

You’ll build the autonomy brain for a cutting-edge autonomous aerial platform that will actually take flight. At Helsing, you won't just be developing software; you'll be integrating state-of-the-art reinforcement learning agents into the operational systems of our own Unmanned Combat Aerial Vehicle (UCAV), the CA-1 Europa, part of the groundbreaking Centaur project. This is a unique opportunity to directly contribute to a novel autonomous system designed from the ground up.

Working at the intersection of machine learning and systems engineering, you'll integrate reinforcement learning agents into high-performance runtime systems, enabling real-time autonomous decision-making in flight. This isn't theoretical; your code will enable the CA-1 Europa to perceive, reason, and act autonomously in the most demanding environments.

What we build ultimately ends up in the hands of real people in high-risk, high-stress situations, so it must be both reliable and frictionless. To give some examples:

• Autonomous Decision-Making Systems — reliable pipelines from sensor data to RL inference to tactical execution, including edge-case and failure-mode handling.

• Reinforcement Learning Integration — bridging Python-based RL agents with Rust runtime systems for low-latency, reproducible inference.

• Distributed Systems & Communications — handling intermittent connectivity and bespoke hardware protocols.

• Training Infrastructure — distributed training, evaluation pipelines, and large-scale runs on custom simulators.

In some areas, we're working at the state-of-the-art—actively implementing research papers and pushing further. In others, we're applying proven techniques to real-world situations they've never encountered before. Both require skill, diligence, and deep technical understanding.

Our software operates under significant constraints, in constantly-changing environments, for users in high-risk situations. It must be reliable and frictionless. T