Jobtailor

• Design and train models from raw I/Q recordings for detection, classification, and related tasks

• Research and implement machine-learning methods from other domains to improve data pipelines

• Define evaluation test sets, metrics, and scenarios reflecting domain shifts between sensors, sites, and interference environments

• Develop compact, deployable expert models for edge hardware

• Continuously test pipelines in real-world conditions and use feedback to improve them

• Integrate new data sources into the data platform

• Set the direction for RF machine learning at Datacept

• Make architectural decisions for the RFML foundation

• Work directly with the founders and build the team as the company grows

• Stay connected to hardware, founders, and customers at protected sites

Requirements

• Experience building and shipping ML systems that people depend on, preferably on signal-like data such as audio, time series, sensor streams, images, video, or RF

• Strong understanding of self-supervised learning and ability to explain why methods work

• Strong mathematical fundamentals and comfort formulating problems before solving them

• Ability to go from idea to prototype to deployed model independently

• Ability to work with few fixed structures and changing priorities

• Preparedness for the intensity of a founding role, including long and unconventional working hours, field trials, and deployments

• Willingness to contribute to Europe's technological sovereignty

• Helpful but not required: prior RF or signal-centric ML experience, including spectrum sensing, modulation recognition, SIGINT, or EW

• Helpful but not required: self-built software/hardware projects

• Helpful but not required: signal processing basics, sampling, spectral analysis, I/Q representation, SDR, communications engineering, or embedded background

• Helpful but not required: publications, open-source work, or production architectures

Core Competencies

Demonstrates expertise in designing and deploying machine learning models for RF and signal-like data, with a strong foundation in self-supervised learning and mathematical problem formulation. Capable of integrating new data sources and making architectural decisions to enhance data pipelines and model performance.

Highest-signal resume keywords

• Machine Learning Systems Development

• Self-Supervised Learning

• Signal Processing

• Architectural Decision-Making

• Prototype to Deployed Model Transition

Hard Skills

• Machine Learning

• Model Training

• Data Pipeline Improvement

• Mathematical Fundamentals

• Signal Processing Basics

• I/Q Representation

• Spectrum Sensing

• Modulation Recognition

• Field Trials

• Deployment

Soft Skills

• Adaptability

• Team Building

• Communication

• Problem Solving

• Independence

Industry Keywords

• RF Machine Learning

• Technological Sovereignty

• Signal-Centric ML

• Field Deployments

• Unconventional Working Hours

Tools & Technologies

• Edge Hardware

• Data Platform

• SDR

• Embedded Systems