Kitware - Natural Language Processing Researcher
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Requirements
• PhD in Computer Science or related field. • Strong publication history in NLP (ACL, EMNLP, NAACL) conferences, or other AI venues incorporating NLP (AAAI, ICML, ICLR, NeurIPS). Candidates should include a detailed list of publications as part of their resume/CV. • Experience with natural language text generation, foundation models, large language models (LLMs), using one or more of GPT, Claude, BERT, or similar. • Highly innovative and demonstrated track record for solving difficult technical challenges. • Experience collaborating successfully with others and thriving in a fast-paced and dynamic work environment. • Due to contractual restrictions, only US Citizens will be considered for this position. • If not already cleared TS/SCI, willingness and ability to apply for, use, and maintain a TS/SCI security clearance. • Experience with multiple large multimodal AI models including image, text, and speech modalities. • Past work on adapting NLP and LLMs to different domains and applications, including decision-making, reasoning, and inference. • $125,000 - $170,000 a year
Responsibilities
• Develop and implement natural language processing algorithms to analyze textual content for sentiment analysis, entity recognition, and other relevant tasks within the scope of computer vision projects. • Collaborate with cross-functional teams including data scientists, software engineers, and domain experts to integrate NLP components into existing or new systems/projects at Kitware. • Stay current with industry trends in natural language processing research by attending conferences, workshops, webinars, reading relevant publications, and engaging in professional networks like LinkedIn groups focused on computer vision and AI. • Conduct experiments to evaluate the performance of NLP models using standard datasets or custom data sets created for specific project needs; analyze results and iterate based on findings. • Document research methodologies, experimental setups, model architectures, code snippets, and other relevant information in a clear and concise manner suitable for sharing with team members and stakeholders at Kitware.
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