About the job

Senior ML/AI EngineerMunich | On-siteAI & Defence Technology Start-Up

About the RoleOur client is a well-funded Defence-Tech scale-up who are seeking a Senior Machine Learning Engineer to help scale its proprietary Large Acoustic Model (LAM) and edge-intelligence systems.

You will bridge classical signal processing with cutting-edge deep learning to turn non-speech audio into real-time intelligence for defense and security applications.

Core ResponsibilitiesModel Training: Design and train deep learning models for acoustic event detection, classification, and localization.
Signal Processing: Integrate advanced digital signal processing (DSP) with machine learning for robust audio feature extraction.
Edge Optimization: Quantize, optimize, and deploy models onto ultra-low-power edge hardware for real-time inference.
Collaboration: Work alongside hardware and physics teams to align software performance with embedded device constraints.

Key RequirementsProven experience as an ML Engineer focusing on audio, time-series, or signal processing.
Strong proficiency in Python, PyTorch/TensorFlow, and modern ML frameworks.
Practical background in acoustics and DSP.
Experience with edge AI deployment (model quantization, pruning, and hardware constraints).
Degree in Computer Science, Electrical Engineering, Signal Processing, or a related field.

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Responsibilities

About the job

Senior ML/AI EngineerMunich | On-siteAI & Defence Technology Start-Up

You will bridge classical signal processing with cutting-edge deep learning to turn non-speech audio into real-time intelligence for defense and security applications.

Core ResponsibilitiesModel Training: Design and train deep learning models for acoustic event detection, classification, and localization.
Signal Processing: Integrate advanced digital signal processing (DSP) with machine learning for robust audio feature extraction.
Edge Optimization: Quantize, optimize, and deploy models onto ultra-low-power edge hardware for real-time inference.
Collaboration: Work alongside hardware and physics teams to align software performance with embedded device constraints.

Key RequirementsProven experience as an ML Engineer focusing on audio, time-series, or signal processing.
Strong proficiency in Python, PyTorch/TensorFlow, and modern ML frameworks.
Practical background in acoustics and DSP.
Experience with edge AI deployment (model quantization, pruning, and hardware constraints).
Degree in Computer Science, Electrical Engineering, Signal Processing, or a related field.