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Machine Learning Engineer, Security AI
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- Problem-Specific Model Customization : Experience adapting neural network architectures like GANs (Generative Adversarial Networks), Autoencoders, Attention Mechanisms, and Transformers or developing novel architectures to address unique challenges in different domains (e.g., NLP, computer vision, time series analysis).
- Proficiency in NLP and LLM: Including experience with BERT, GPT, or similar, to effectively derive insights from structured and/or unstructured text data.
- Building custom real-world production NLP models for tasks like text generation or text summarization is a strong plus.
- Innovative Solution Development : Ability to apply deep learning techniques innovatively to solve complex problems, often combining domain knowledge (from cybersecurity or other domains) and out-of-the-box thinking.
- 3+ years’ experience in programming languages such as Python or R, and experience with machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).
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