PA
PartSpaceMachine Learning Engineer – m/f/d
Machine Learning Engineer responsible for optimizing AI pipelines at Easy2Parts. Join a growing team to revolutionize component sourcing with AI technology.
About the role
- Design and build and optimize state-of-the art deep learning models for 2D/3D perception tasks: object detection, segmentation, tracking, sensor fusion (camera, radar, LiDAR, IMU or equivalents).
- Lead development of scalable ML pipelines for large-scale data ingestion, preprocessing, active learning, and annotation workflows.
- Architect & Develop end-to-end ML systems — from dataset management and training infrastructure to model deployment in simulation or production environments.
- Collaborate closely with engineers, data scientists, and tooling experts to scale ML platforms across massive sensor datasets.
- Deliver ML solutions optimized for accuracy, latency, and robustness in real-world, high-stakes settings.
Requirements
- Built and deployed AI/ML systems in demanding real-world scenarios — robotics, medical imaging, surveillance, drone systems, geospatial analysis, or similar.
- Proficient with deep learning frameworks like PyTorch or TensorFlow.
- Strong Python skills and ability to contribute to performance-critical code (bonus points if you know C++, CUDA or ONNX for model optimization).
- Experienced with ML infrastructure: data pipelines, CI/CD for ML, distributed training, annotation tooling, and versioning (MLflow, DVC, Weights & Biases, Airflow, Kubeflow, or Metaflow).
- Familiar with cloud computing platforms (Azure, GCP) and GPU clusters (SLURM, Kubernetes, Docker).
- MSc or PhD in computer science, applied math, physics, or a related technical field.
Benefits
- Flexible working: hybrid in Gothenburg or Munich, or fully remote within the EU.
- Join a flat, inclusive culture that values learning and psychological safety.
Job title
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NO
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