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ML Engineer - 3D Perception (C++ / Python / Deep Learning)

Nicholson SAS

Karlsruhe · On-site 1w ago

About the role

About

I am currently hiring on behalf of a high-growth deep tech company operating at the intersection of Physical AI and real-world perception systems.

This team is building large-scale machine learning systems that interpret complex 3D environments in real time. If you enjoy solving hard perception problems and working close to production systems, this could be a strong fit.

Responsibilities

  • Improving core 3D perception tasks: object detection, tracking, segmentation, and freespace estimation
  • Designing and scaling auto-labelling pipelines to reduce manual annotation costs and increase dataset diversity
  • Implementing robust tracking-by-detection algorithms in 3D space
  • Training and optimising large ML models using substantial in-house compute infrastructure

Requirements

  • MSc or PhD in Computer Science, Robotics, or a related discipline (or equivalent industry experience)
  • Strong deep learning experience in at least one sensor modality (Camera, LiDAR, or Radar)
  • Experience with modern architectures (transformer-based models preferred)
  • Strong programming skills in C++ and Python
  • Collaborative mindset and strong problem-solving ability

Nice to have

  • Experience with TensorRT, LibTorch, or ONNX for optimised deployment
  • Background working on mid-to-large-scale production software systems

This is an opportunity to join a highly technical team solving complex real-world perception challenges with meaningful compute resources and long-term product vision.

Location: Karlsruhe | On-site Employment Type: Full-time

Requirements

  • Strong deep learning experience in at least one sensor modality (Camera, LiDAR, or Radar)
  • Experience with modern architectures (transformer-based models preferred)
  • Strong programming skills in C++ and Python
  • Collaborative mindset and strong problem-solving ability

Responsibilities

  • Improving core 3D perception tasks: object detection, tracking, segmentation, and freespace estimation
  • Designing and scaling auto-labelling pipelines to reduce manual annotation costs and increase dataset diversity
  • Implementing robust tracking-by-detection algorithms in 3D space
  • Training and optimising large ML models using substantial in-house compute infrastructure

Skills

C++LiDARPythonRadarTransformer

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