Tasks & Responsibilities:
* 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.
Must Haves:
* 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
* Professional proficiency in English
* Strong analytical and problem-solving skills, and collaboration abilities.
Nice to Haves:
* 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.
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