We’re working with a well-funded deep-tech company building next-generation AI sensing systems for deployment in complex real-world environments. They’re looking for a Machine Learning Engineer / Applied ML Scientist to help develop and deploy advanced ML models for real-time inference on edge devices.

The company operates at the intersection of AI, sensing, embedded systems, and intelligent autonomy, with applications spanning industrial, robotics, environmental, and next-generation sensor platforms.

This is a highly technical role suited to someone who enjoys taking ideas from research through to deployment in production systems.

The Role

You’ll work across the full ML lifecycle - from data processing and model development through to optimisation and deployment on constrained hardware.

Typical projects may include:
  • Developing deep learning models for real-world sensor data
  • Building low-latency inference pipelines for edge devices
  • Optimising models for deployment (quantisation, pruning, compression, distillation)
  • Designing ML systems for noisy or dynamic environments
  • Working closely with hardware, embedded, and systems engineers
  • Prototyping and evaluating new ML architectures and approaches
  • Contributing to production ML infrastructure and deployment workflows
What They’re Looking For
  • Strong experience with Python and modern ML frameworks (PyTorch and/or TensorFlow)
  • Experience developing and deploying ML models in production environments
  • Background in one or more of:
    • Edge AI
    • Embedded ML
    • Real-time inference
    • Audio / signal processing
    • Sensor fusion
    • Computer vision
    • Time-series modelling
  • Understanding of performance optimisation for constrained systems
  • Familiarity with Docker, Linux, cloud infrastructure, or MLOps tooling
  • Strong mathematical and problem-solving skills
Nice to Have
  • Experience with on-device AI or ultra-low-power inference
  • Background in robotics, autonomous systems, defence, healthcare, or industrial AI
  • Knowledge of quantisation / pruning / TinyML techniques
  • Exposure to C , embedded systems, or hardware-aware ML optimisation
  • Research background (PhD/MSc) in ML, signal processing, robotics, physics, or related fields
Why Join?
  • Work on genuinely cutting-edge AI systems with real-world deployment
  • Small, highly technical engineering team
  • Significant ownership and influence over technical direction
  • Strong funding and long-term product vision
  • Opportunity to work closely with leadership on next-generation AI products
Location: Munich - Onsite
Compensation: Highly competitive equity potential depending on experience