AI Founding Engineer: RF Machine Learning (SIGINT) (f/m/d)

Job description

Modern conflict runs on the electromagnetic spectrum. Every drone, every radio, every radar leaves a trace in it. Datacept builds cognitive electronic warfare systems that listen to that spectrum, understand what is in it, and act on it. At the centre of this sits one capability: models that learn directly from raw I/Q data. Not from decoded protocols, not from hand-built feature extractors, but from the signal as it arrives at the antenna. This is how we handle emitters no system has seen before, in environments where new frequencies, new protocols, and new platforms appear faster than any rulebook can follow. We are looking for engineers who have this capability. Tasks Your role You are the founding engineer for RF machine learning at Datacept. You take responsibility for the cognitive core of our systems. The layer that turns received I/Q data into detection, classification, and understanding of emitters for SIGINT and COMINT missions. We build the foundation model in a space where no foundation models exist. The domain of RF machine learning is just in its starting phase. Methods that work in audio and computer vision like self-supervised pretraining, learned representations, large pretrained backbones carry over to RF only if you understand why they work. Second, the models have to run in the field. Our systems are deployed at ports, stadiums, critical infrastructure, and military sites, on hardware with real limits on compute and power. Our cloud platform enables scalable processing of the largest RF datasets, here we can deploy the full power of the foundation model. We maintain one of the largest RF datasets in the world which is constantly growing. Besides being the integral part of the engineering team you will directly work with the founders. You set the direction for RFML at Datacept, you make the architectural decisions, and as we grow you build the team around you. What you will do Design and train models that learn from raw I/Q recordings: detection, classification, and more Research and implement ML methods from other domains to improve our data pipelines Define how we evaluate: test sets, metrics, and scenarios that reflect real conditions such as domain shift between sensors, sites, and interference environments Make models fit the hardware: Besides the foundation model we need expert models that are also compact and deployable on edge Con…