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Specialized Knowledge: Expertise in specific AI domains such as natural language processing, computer vision, or deep learning. Julia, MATLAB, Scala, LISP, Prolog, and JavaScript for various AI/ML applications, including data analysis, high-performance computing, enterprise-level applications, and web-based MLExperience with AI and machine learning frameworks (, TensorFlow, PyTorch, Scikit-learn.
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Audible Content Data Science team partners with business, technology, and product leaders to solve problems related to Content Understanding, Content Evaluation, and Recommendations, relying on Natural Language Processing (NLP), ML, Deep Learning, LLMs/GenAI. We operate in an agile environment in which we own the life cycle of research, design, model development and, often, deployment.
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Deep understanding of predictive modelling, machine-learning, clustering and classification techniques, and algorithms. Analyze raw data: assessing quality, cleansing, and structuring for downstream processing.
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Knowledge of AI concepts, such as machine learning, deep learning, natural language processing, computer vision, or reinforcement learning. Firm grasp of data structures, algorithms with fluency in programming languages like Java, Kotlin, Go.
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From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.
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Minimum of 5 years of experience in machine learning or deep learning engineering and demonstrated proficiency in computer vision related algorithms and models, ability to process both video and images at required latency and accuracy for use cases.
$131,100 - $336,900 a yearFull-timeExpandApply NowActive JobUpdated 7 days ago - UpvoteDownvoteShare Job
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The Team: S&P is a leader in risk management solutions leveraging automation and AI/ML. This role is a unique opportunity for an experienced ML scientist and hands-on NLP/Gen AI/ LLM senior scientist to grow into the next step in their career journey and apply her or his domain expertise in NLP, deep learning, GenAI, and LLMs to drive business value for multiple stakeholders while mentoring and growing a ML Data Science team.
$180,000 - $225,000 a yearFull-timeExpandApply NowActive JobUpdated 7 days ago - UpvoteDownvoteShare Job
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Knowledge of computer vision, statistical pattern recognition, machine learning, artificial intelligence, or natural language processing. Experience with developing deep learning algorithms using structured and unstructured data sources and frameworks, including Tensorflow or Pytorch.
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You must be familiar with image processing, machine vision, and data engineering, and even better if you are interested in 3D rendering, deep learning, and computer graphics.
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Or MS degree in Computer Science, Machine Learning, Data Mining, Statistics, or related technical field with 5+ years of relevant experience. Investigate and harness cutting-edge machine learning methodologies, including deep learning, large language models (LLMs), and graph neural networks, to address diverse challenges throughout the company.
$180,265 - $212,000 a yearFull-timeExpandApply NowActive JobUpdated 7 days ago - UpvoteDownvoteShare Job
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Focus on Deep Learning, Reinforcement Learning, Natural Language Processing, Speech/Voice Analytics, Time Series, Computer Vision, Cryptography, and Interpretability, and Ethics and Fairness of AI.
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The ideal candidate will have a solid background in computer vision, deep learning, image processing, and machine learning techniques. Stay updated on the latest advancements in computer vision, image processing, and machine learning research.
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PhD student in Computer Sciences, Electrical Engineering, or Mathematics with specialization in machine learning, deep learning, or natural language processing.
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Knowledge of natural language processing, computer vision, and deep learning techniques. Experience with machine learning libraries and frameworks such as Keras TensorFlow, PyTorch, and Scikit-Learn.
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DE designing and implementing large scale data science projects, Artificial Intelligence (AI) and Machine Learning (ML) models and interfaces using Spark, Tensorflow, Python data science libraries, and Deep Learning (DL) frameworks on Amazon Web Services (AWS) and On-premise computing environments; developing cutting-edge investment tools and strategies using Natural Language Processing (NLP), Large Language Models (LLM), Neural Networks, and Supervised and Unsupervised Learning algorithms.
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