AI Research Engineer - ML & Signal Processing

Who We Are

Helsing is a defence AI company with the mission is to protect our liberal democracies. We aim to achieve technological leadership, so that open societies can continue to make sovereign decisions and control their ethical standards. We believe we have a responsibility to be thoughtful about the development and deployment of powerful technologies like AI and take this responsibility seriously.

We are an ambitious and committed team of software and deployment 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 today.

Benefits

, and its ethical implications. The role At Helsing you will work at the intersection of machine learning and signal processing on RF data, helping build the AI capabilities inside our electronic warfare systems: models that detect, classify, and reason about RF signals in congested, contested spectrum. The data is messy, irregular, and adversarial.

Output runs on real platforms, under hard latency and compute budgets. What you build has to survive contact with the deployment target. You will own problems end-to-end: read the literature, prototype on real recorded data, push to the edge, watch it run.

You will work in a small research team alongside RF and hardware engineers and the operators using the system. Current focus areas include self-supervised representation learning on signal data, deinterleaving and emitter identification, mode recognition, and anomaly detection on embedded hardware. You should apply if you Hold a PhD (or have an equivalent research track record) in ML, signal processing, physics, electrical engineering, or a related field, with publications at NeurIPS, ICLR, ICML, ICASSP, or similar venues.

Have shipped non-trivial ML into a real product - not just notebooks. You know what breaks between research and deployment. Are comfortable on irregular, noisy sequence data: temporal models, self-supervised or contrastive methods, calibration under distribution shift.

Write clean Python, and are willing to pick up Rust where the deployment target demands it. Read papers and reimplement them. You can tell which results will hold up and which won't.

Communicate research clearly to ML peers, RF engineers, and end users. We don't expect you to arrive with deep RF expertise. A strong ML or physics background and willingness to learn the domain alongside RF colleagues is enough.

Note: We operate at an intersection where women, as well as other minority groups, are systematically under-represented.

Qualifications

; ability and impact cannot be summarised in a few bullet points.

Nice To Have

Experience with EW, SIGINT, ESM/ELINT, RWR, or radar concepts. Familiarity with RF signal theory at a level sufficient to talk to RF engineers (modulation, propagation, antenna patterns — see modulation-schemes). Experience deploying ML to edge or embedded hardware (ONNX, TensorRT, quantisation, profiling on constrained GPUs).

Join Helsing and work with world-leading experts in their fields Helsing’s work is important. You’ll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns The work is unique.

Requirements

and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure.