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Research experience in machine learning, deep learning, and/or natural language processing. We are looking for strong engineers who have a background in generative AI and NLP, with experience in areas like language model evaluation; data processing for pre-training and fine-tuning; responsible LLMs; LLM alignment; reinforcement learning for language model tuning; efficient training and inference; and/or multilingual and multimodal modeling.
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The core aspect of this role is writing the annotation guidelines, annotation of clinical notes - in order to make it easier for software, analytics & data science teams to train natural language processing models.
$50 - $70 an hourExpandApply NowActive JobUpdated 6 days ago - UpvoteDownvoteShare Job
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Experience working with Research Scientists, reading technical research papers, and implementing state-of-the-art methods in Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Knowledge Representation and Management, etc.
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Demonstrated expertise in at least one of the following areas: computer vision, machine learning, autonomous systems, natural language processing and translation, knowledge representation, and reasoning with evidence.
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The Department of Information Sciences and Technology (IST) has 45 full-time faculty members with diverse areas of research focus including cybersecurity, data mining and machine learning, natural language processing, human-centered computing, ubiquitous and mobile computing, cyber-physical systems, and engineering education.
Full-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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Must demonstrate understanding of AI, natural language processing (NLP), and machine learning concepts. Experience evaluating and testing Natural Language Processing (NLP) systems or Large Language Models (LLMs.
$100,000 - $115,000 a yearFull-timeWork from homeExpandUpdated 21 days ago - UpvoteDownvoteShare Job
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Experienced with traditional as well as modern machine learning and statistical techniques, including A/B testing, Causal Inference, Regression, Classification, Ensemble Methods, Deep Learning, Natural Language Processing, and Reinforcement Learning.
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2 large projects related to Client & Natural language processing (NLP)1+ yrs of model deployment, model packaging and model lifecycle management experience as part of BYOM framework.
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As a highly experienced and seasoned science leader, you will apply state of the art natural language processing and computer vision research to video centric digital media, while also responsible for creating and maintaining the best environment for applied science in order to recruit, retain and develop top talent.
ExpandApply NowActive JobUpdated 8 days ago - UpvoteDownvoteShare Job
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Meta is seeking a Postdoctoral Researcher to join Fundamental AI Research (FAIR), a research organization focused on making significant progress in AI. Individuals in this role are expected to be recognized experts in identified research areas such as Natural Language Processing, Multimodality, Artificial Intelligence and Machine Learning.
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As a Data Analytics Engineering Intern, you will be responsible for designing, developing, and implementing natural language processing (NLP) and AI-based solutions to enhance our products and services.
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Harness the power of transformer architecture, a cutting-edge deep learning model widely employed in natural language processing and computer vision, to optimize the language model's performance and efficiency.
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Your contributions will be instrumental in shaping cutting-edge architectures, frameworks, and methodologies, pushing the boundaries of natural language processing (NLP) and machine learning.
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Collaborates with Machine Learning/Natural Language Processing (ML/NLP) engineers and data scientists to provide expert insights into real-world clinical decisions, coding, and interpretation of cases and findings.
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Quality Assurance (QA), Test and Evaluation (T&E), privacy requirements, cybersecurity, assessment of opportunities to incorporate Artificial Intelligence ("AI") and/or Machine Learning ("ML") into ODRR Test and Evaluation which may include, Predictive Analytics, Natural Language Processing (NLP) and Large Language Models (LLM), Generative AI, Training models, navigating access, compliance, privacy, and security concerns across all applicable platforms, data types, and use cases.
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