A Natural Language Processing Engineer specializes in developing and implementing algorithms and models tailored for understanding, processing, and generating natural language text. They utilize methodologies such as tokenization, parsing, named entity recognition, part-of-speech tagging, and other NLP techniques to perform tasks including text classification, chatbot development, and other applications where the primary input or output is natural language text.
Savannah River National Laboratory (SRNL) is seeking a highly motivated and self-starting AI and machine learning researcher to join our team in creating and maintaining large-language model research tools, especially for cybersecurity data. The successful candidate will have strong experience in Python, AI, and cybersecurity, with a focus on developing and maintaining high-quality code using unit testing, continuous integration, and deep learning models and libraries. The ideal candidate will be a solid researcher (PhD preferred), an independent worker, a good communicator, and a team player with a strong ability to write and document his or her work.
Minimum Qualifications:
Bachelor's degree in Computer Science, Cybersecurity, or related field and 4-6 years of experience in software development, preferably in a research environment For ability to obtain and maintain a security clearance, US Citizenship is Legally Required Strong experience in Python programming, including experience with AI and machine learning libraries (e.g. Pytorch, TensorFlow, scikit-learn) Experience with deep learning models and libraries, particularly Huggingface, Pytorch, etc. Strong understanding of cybersecurity concepts and threats Experience with unit testing and continuous integration (e.g. Jenkins, GitHub, or others) Excellent communication and teamwork skills Ability to write and document technical work Experience with version control systems (e.g. Git) Familiarity with Agile development methodologies Self-motivated and able to work independently Experience with Red Hat Enterprise Linux (RHEL) or similar Linux distributions Experience with batch processing tools such as PBS or SLURM Familiarity with data engineering and curation principles and practices Experience with data visualization tools such as Plotly/Dash, Kibana, or similar tools
Preferred Qualifications:
Experience with machine learning primitives and ability to choose the right approach for a given problem (e.g. decision trees, random forests, deep learning) Experience with natural language processing (NLP) techniques and libraries (e.g. NLTK, spaCy) Familiarity with containerization (e.g. Docker) Experience with cloud-based platforms (e.g. AWS, Azure) Certification in cybersecurity or a related field (e.g. CompTIA Security+, CISSP) Experience with proposal writing and research funding opportunities Develop and maintain large-language model research tools for cybersecurity data using Python, Huggingface models, and Pytorch libraries or other equivalent state-of-the-art technology Design and implement unit tests and continuous integration pipelines to ensure high-quality code Collaborate with team members to develop and maintain research tools and software applications Write and maintain technical documentation for research tools and software applications Participate in code reviews and contribute to the improvement of the overall codebase Develop and maintain strong understanding of cybersecurity concepts and threats Collaborate in writing proposals for external sponsors, Laboratory Directed Research and Development (LDRD) projects, and other funding opportunities Stay up-to-date with the latest developments in AI, cybersecurity, and large-language models
