AI Founding Engineer – RF Machine Learning, SIGINT

Jobtailor; Hamburg; Vor Ort; EUR 90.000 - 130.000; Vollzeit

Zusammenfassung

Datacept in Hamburg, Germany, seeks a founder-minded ML engineer to design and deploy RF- and signal-like data models. You will drive self-supervised learning, shape data pipelines, and guide architectural decisions for a scalable RFML foundation.

You will collaborate closely with the founders, build the team as the company grows, and engage in field deployments at protected sites, balancing innovative work with rigorous validation.

Qualifikationen

• Experience building and shipping ML systems on signal-like data (audio, time series, RF, etc.).
• Strong understanding of self-supervised learning and explainability.
• Solid mathematical fundamentals and ability to formulate problems.
• Ability to go from idea to prototype to deployed model independently.
• Comfort with changing priorities and few fixed structures.
• Willingness for founding-role intensity and long hours.
• Interest in Europe's technological sovereignty.

Aufgaben

• Design and train models from raw I/Q recordings for detection, classification, and related tasks.
• Research and implement ML methods 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 with founders and build the team as the company grows.
• Stay connected to hardware, founders, and customers at protected sites.

Kenntnisse

ML Systems Development

Self-Supervised Learning

Signal Processing

Architectural Decision-Making

Prototype to Deployed Model Transition

Tools

Edge Hardware

Data Platform

SDR

Embedded Systems

Jobbeschreibung

• 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