Natural language processing expert (m/f/d)

Background

The ideal candidate is an expert AI/NLP consultant with hands-on experience fine-tuning Transformer models and strong proficiency in PyTorch. They should have demonstrable experience with multi-task and multi-label classification, expertise in handling severe class imbalance in text data, and proficiency in deploying ML models as REST APIs. Strong software engineering fundamentals are also essential.

• Start date: asap
• Latest Start Date: 01.11.25
• Planned duration: 31.12.25
• Extension: possible
• Workplace: Basel
• Workload: 50- 100%
• Travel: no

• Review and optimize a transformer architecture and training pipeline
• Implement and experiment with advanced techniques to improve performance on fields with severe class imbalance
• Conduct in-depth error analysis to identify patterns in misclassifications and propose data-driven improvements
• Refine and validate the data processing, label mapping, and stratified data splitting procedures to ensure maximum reliability
• Collaborate with our software architect to integrate the final, optimized model into a production-ready API for inference
• Document the final model architecture, training procedures, performance benchmarks, and best practices for future development

• Educational background: advanced degree in AI/NLP or related field
• Min. 3 years hands-on experience fine-tuning Transformer models
• Demonstrable experience with multi-task and multi-label classification problems, expertise in handling severe class imbalance in text data
• Proficiency in deploying machine learning models as REST APIs
• Strong proficiency in PyTorch, including creating custom model architectures (e.g., multi-head classifiers) and custom loss functions
• Strong software engineering fundamentals and the ability to write clean, modular, and well-documented Python code, experience with Docker
• Strong analytical and problem-solving skills, and collaboration abilities

• Direct experience working with biomedical, scientific, or other technical document formats
• Familiarity with advanced data splitting techniques for multi-label datasets
• Experience with MLOps principles and tools