Software Engineer - Ground to Air HMI

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 Ground to Air HMI enables air forces to operate autonomous systems in missions. 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 and frontend engineering challenges across a wide variety of fields that you’ll help us solve. To give some examples:

• Distributed systems (e.g., intermittent connectivity and eventual consistency)

• Human-autonomy teaming (e.g., HMIs for effective human oversight and control)

• Networking (e.g., long-range low-bandwidth radios and cross-medium routing)

• Real-time interfaces (e.g., tactical situation display and streaming sensor feeds)

• Deployment infrastructure (e.g., air-gapped devices and heterogenous fleets)

• Security (e.g., data sharing in low-trust, no-TLS networks and actually-secret data handling)

• Machine learning (e.g., systems for model development, storage, and dissemination)

In several of these areas, we are working near the state-of-the-art; we actively read (and sometimes implement) research papers, and often need to push even further to make theory work in practice. In others, we are applying well-known techniques to real-world situations (with real-world limitations) that have never been used in before, which requires skill, diligence, and honing a deep understanding of the underlying technologies.