Project specification
Project expertise
Description
- Extend and optimize our deep learning frameworks (based on PyTorch) to improve efficiency and ease of use across different use cases
- Scale the distributed training infrastructure and tooling for faster and larger model training operations
- Design and implement data strategies for large-scale datasets, optimize storage and GPU utilization
- Debug and solve performance bottlenecks in production ML pipelines at large scale
- Bring technical expertise to our engineering culture and share insights on ML optimization and systems design
Requirements
- MSc or PhD in Computer Science or a STEM field with a focus on Machine Learning and Deep Learning
- Strong software engineering skills in Python; confidence with modern DL frameworks (PyTorch, JAX, or TensorFlow)
- Proven experience implementing custom layers, loss functions, and distributed training loops – not just library imports
- Hands-on experience training models on large-scale GPU clusters and implementing advanced parallelism strategies (NCCL, MPI)
- Familiarity with workload orchestrators (Slurm, Kubernetes, Ray) for managing complex training jobs
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Frequently asked questions
The project preferably starts in September 2026 and is planned to end in February 2027 (5 months).
The project offers a daily rate of 720 - 960€ which breaks down to an hourly rate of 90 - 120€/h.
The project requires German (Advanced) as essential language. Additionally, English (Advanced) is desirable.
The project is related to the following industry: Information Technology.
The project covers the following business areas: Information Technology, Product Development, and Research and Development.
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