Software Engineer - Autonomous Air System V&V

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 programme 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

Our Autonomous Air System V&V team builds the simulation and validation environments that enable rigorous testing of autonomous air systems before they ever leave the ground. We develop Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) environments that allow engineers to integrate, test, and assure AI-driven capabilities against virtualised and representative hardware.

We build software that is subject to significant and unorthodox technical constraints, must operate in a wide variety of constantly-changing environments, and has to support an ever-growing list of challenging use-cases. What we build ultimately ends up in the hands of real people in high-risk, high-stress situations – from mission planning to command and control (C2) and mission debrief – so must be both reliable and frictionless.

Taken together, this brings a whole host of interesting backend engineering challenges across a wide variety of fields that you'll help us solve. To give some examples:

• Simulation frameworks (e.g., high-fidelity SiL/HiL environments, real-time and deterministic simulation)

• Test automation (e.g., fault injection, automated test coverage, regression testing at scale)

• Hardware-software integration (e.g., testing against representative hardware, sensor and actuator interfaces)

• Real-time and high-performance computing (e.g., concurrency, low-latency execution, profiling and optimization)

• Distributed systems (e.g., orchestrating complex multi-component test environments)

• Deployment infrastructure (e.g., air-gapped devices and heterogeneous test rigs)

• Machine learning (e.g., systems for testing and validating AI/ML model behaviour)

In several of these areas,