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Quantum-Systems GmbH

Senior Machine Learning Engineer

Gilching

SeniorAI & DataML / AI EngineerAI-first team

Senior Machine Learning Engineer bei Quantum-Systems GmbH in Gilching. Entwicklung, Training und Deployment von ML-Modellen für autonome UAV-, UGV- und USV-Plattformen.

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Job description

As an ML Engineer (m/f/d), you will be a key contributor within our development team. Your responsibilities include preparing and selecting data, training and validating models, as well as deploying them on real embedded UAVs, UGVs, and USVs to enhance their capabilities.

You will work within a world-class team to help push the boundaries of autonomous drone technology.

In this role, you collaborate closely with stakeholders from hardware development, testing, production, and customer operations, driving continuous improvements in the autonomy of our UxV platforms.

By advancing intelligent autonomy, you will actively support Quantum Systems’ mission to set the benchmark for next-generation UxV solutions.

Within this advertisment we are open to hiring people with the following profiles:

ML Engineer (Generalist)

  • Proven track record designing and training deep learning models for production use
  • Experience with model optimization for resource-constrained environments (quantization, pruning, distillation)
  • Deep expertise in computer vision architectures or VLAs Architecture

ML Platform Engineer (MLOps)

  • Experience building and maintaining ML training and inference pipelines
  • Hands-on experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)
  • Experience with GPU cluster management and high-performance computing environments

Data Engineer (Geospatial Data & Sensor Fusion)

  • Experience managing large-scale datasets, including geospatial, image, or sensor data
  • Familiarity with drone-generated data types (imagery, LiDAR, telemetry, flight logs)
  • Experience with data quality assurance, annotation workflows, and labelling platforms
  • Experience with generation of syntetic data and simulation