FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and deploying machine learning systems, particularly in RF and signal-centric domains, with a strong foundation in self-supervised learning and mathematical problem formulation. Capable of leading architectural decisions and integrating diverse data sources while adapting to dynamic work environments.
Highest-signal resume keywords
Machine Learning System DevelopmentSelf-Supervised LearningSignal Processing FundamentalsArchitectural Decision-MakingData Pipeline Integration
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningModel TrainingData Pipeline DevelopmentMathematical Problem FormulationPrototype DevelopmentSignal ProcessingI/Q RepresentationSpectrum SensingModulation RecognitionEmbedded Systems
Soft Skills
AdaptabilityTeam BuildingCommunicationProblem-SolvingIndependence
Tools & Technologies
Edge HardwareData PlatformsField TrialsReal-World TestingFeedback Loops
Industry Keywords
Electronic WarfareCognitive CoreRF Machine LearningSignal-Centric MLTechnological Sovereignty
About the role
Key responsibilities & impact- Serve as founding engineer for RF machine learning at Datacept
- Take responsibility for the cognitive core of electronic warfare systems
- Design and train models from raw I/Q recordings for detection, classification, and emitter understanding
- Research and implement machine-learning methods from other domains to improve data pipelines
- Define evaluation approaches, including test sets, metrics, and real-world scenarios involving domain shift and interference
- 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 and make architectural decisions
- Work directly with the founders and build the team as the company grows
- Stay connected to hardware, founders, customers, field sites, and deployments
Requirements
What you’ll need- Experience building and shipping ML systems that people depend on, preferably on signal-like data: 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 take work 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
- Desire to contribute to Europe's technological sovereignty
- Helpful but not required: prior work in RF or signal-centric ML, including spectrum sensing, modulation recognition, SIGINT, or EW
- Helpful but not required: self-built software or hardware projects
- Helpful but not required: signal processing basics, including sampling, spectral analysis, I/Q representation, SDR, communications engineering, or embedded systems
- Helpful but not required: publications, open-source work, or production architectures
Benefits
Comp & perks- Possibility to contribute to strengthening Europe's sovereignty
- Virtual shares (VSOP) as part of the founding engineer package
- A development and compensation roadmap that we define together and that grows with the company
- Remote work with full mobile equipment
- A workplace at both locations: Hamburg HafenCity (HQ) and Horneburg (R&D)
- Access to real RF data, real hardware, and real deployments