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Significant experience and advanced knowledge of developing and applying algorithms in one or more of the following machine learning areas/tasks: deep learning, unsupervised feature learning, zero- or few-shot learning, active learning, transformer-based language modeling, multimodal learning, ensemble methods, scalable online estimation, and probabilistic graphical models.
$159,324 - $245,544 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Programming skills and familiarity with GPU based technologies like CUDA, CuDNN and TensorR and background with deploying machine learning models on data center, cloud, and embedded systems.
$144,000 - $270,250 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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In this role, the Machine Learning Engineer will work with a variety of data-driven technologies, including traditional and deep learning paradigms. The Machine Learning Engineer is responsible for proposing, planning, executing, and analyzing research and development machine learning projects related to the field of advanced manufacturing artificial intelligence.
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Apply computational tools and machine learning/deep learning techniques to analyze and interpret complex biological data relevant to drug discovery. Strong proficiency in programming (e.g., SQL, Python, R, MATLAB), database technologies (Oracle, mySQL, relational databases; graph databases are a plus), machine learning/deep learning (network architectures are a plus), dimensionality reduction techniques (e.g., PCA), and possible cheminformatics software suites Demonstrated experience in the analysis and visualization of large datasets.
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Design and implement deep-learning (DL) and machine-learning (ML) models to extract valuable insights from large repositories of time-series/biosensor data. This position is with the Signal Processing team at WHOOP. As a Signal Processing Engineer, focused on Deep Learning, you will be part of a cross-functional team composed of Signal Processing, WHOOP Labs, Firmware, and Data Science.
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Overview Are you a a data expert with broad understanding and experience in solving business problems by integrating statistical analysis, machine learning/artificial intelligence, deep learning, Natural Language Processing (NLP) and Large Language Models (LLMs.
$130,000 - $170,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Strong grasp of object-oriented programming (e.g. SOLID Principles)Experience using cloud computing for machine learning and deep-learning frameworks such as TensorFlow, PyTorch, KerasExperience in source management, code reviews, unit testing, and related modern software development methodologiesA distinguished publication record in NLP, Machine Learning, Computer Vision or related areas preferredAdvanced skills in data sourcing, cleaning, manipulating, analyzing, visualizing and modeling.
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Utilize expertise in computational biology, machine learning, and bioinformatics to develop and optimize algorithms and models for the analysis of high-dimensional biological data.
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The ideal candidate will have a strong background in computer vision, machine learning, and deep learning, as well as experience working with neural radiance fields. Strong background in computer vision, machine learning, and deep learning.
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Must have a strong background in one or more of the following: Mathematical, Statistics, Probability, Deep Learning, Machine Learning, Natural Language Processing, Computer Vision, Recommendation Systems, Pattern Recognition, Large Scale Data Mining or Artificial Intelligence.
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Have experience applying signal processing, image processing, pattern recognition, machine learning, deep learning, or related techniques with respect to signal extraction from sensors.
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The candidate should have subject matter expertise in designing and implementing machine learning pipelines including a deep understanding of core ML frameworks such as PyTorch, TensorFlow or Scikit-learn and 3rd party ML platforms from Palantir, Dataiku, DataRobot, Alteryx and other vendors.
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At least one of the following: GCP Professional Machine Learning Engineer, AWS Certified Machine Learning Engineer. In addition, the candidate should have a strong understanding and experience with the integrated machine learning environments provided in Azure, AWS and GCP, along with emerging experience with Generative AI ML technical architectures that include pre-trained LLM and diffusion models such as LLaMA, ChatGPT, Gemini, Stability AI and Claude as well as Vector Databases and orchestration tools such as LangChain.
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Certified Machine Learning - Specialty, Microsoft Certified: Azure AI Fundamentals. They must be comfortable engaging with non-technical ends users to understand objectives and leverage (the candidates) understanding of available machine learning designs to blueprint solutions and oversee technical implementations, done by others.
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Self-driven, challenge-loving, detail oriented, teamwork spirit, excellent communication skills, ability to multitask and manage expectationsNice to have:Experience in any of: machine learning, analytics, data mining, or data mart and warehouseExperience with Deep Learning platforms (Tensorflow/Keras/Spark MLlib) and SQL/Unix/ShellExperience with machine learning algorithms, NLP, and/or statistical methods a big plusYahoo is proud to be an equal opportunity workplace.
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machine learning deep jobs Title: data engineer Company: Visa
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