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Design and implement cutting-edge machine learning algorithms using deep learning frameworks and distributed data processing frameworks such as Spark. Our ML team is responsible for the development of machine learning algorithms and systems for recommendations at Tinder.
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Data science, machine learning, optimization models, Master's degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
$136,000 - $200,000 a yearFull-timeExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
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We are looking for an experienced Machine Learning Engineer to help us in our journey to scale end-to-end neural networks for autonomous driving. Challenges you will own Technical leadership of at least one company wide programDeveloping and implementing machine learning models and robotics systems to enhance the capabilities of our Autonomous Vehicle stack.
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Ideal candidates will also have experience in statistical signal processing and its application to sensor data or experience leveraging the latest techniques in deep learning for computer vision.
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You will learn practical modeling skills, as well as software skills to be a successful machine learning engineer. You will identify an applied machine learning problem in the enterprise search and question answering ecosystem.
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SAIC takes on grand scientific and engineering challenges in machine intelligence and actively contributes to the international research community through scientific publications and presentations in major conferences and journals in research areas of Computer Vision, HCI, Contextual/multi-modal modeling, NLP, Speech Recognition, Dialogue, and Machine and Deep Learning.
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Advanced Degree: Master's or PhD in Computer Science, AI, Linguistics, or related fields, with a focus on machine learning and natural language processing. Research and Development: Stay abreast of the latest advancements in machine learning, particularly in LLMs, LLM agents, and large-scale neural network training.
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2+ years working in an Economist / Data Scientist / Applied Scientist role building science models for production environments. MSc/PhD in Economics, Statistics, Machine learning, or equivalent industry/academic experience.
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Design, execute, and own analyses of large, complex genomics datasets using Artificial Intelligence, machine learning, and statistical methods to generate biological insights. Lead end-to-end analyses that includes data gathering, processing, analysis, iteration with stakeholders, and presentation of results.
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The Sr. Instructional Designer performs needs analysis, develops roadmaps and schedules, liaises with subject matter experts and stakeholders, autonomously oversees assigned projects, crafts appropriate learning solutions across various modalities (such as web-based, instructor-led, virtual training, blended approaches, among others), prepares content for deployment in a learning management system, and establishes feedback mechanisms to assess the effectiveness of developed solutions.
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Solid understanding of HIPAA compliance and its implications for data processing and machine learning in healthcare is a plus. Foster a culture of innovation, continuous learning, and adherence to best practices in machine learning and data security.
$180,000 - $225,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Design and develop cutting-edge speech algorithms and systems using Digital Signal Processing, deep learning and big data. Experience designing and implementing real-time Machine Learning (ML) based voice processing algorithms for embedded systems or mobile platforms.
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Moloco is a machine learning company empowering organizations of all sizes to grow and unlock the full value of their unique first-party data, elevating the traditional path to performance advertising.
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PhD in Electrical Engineering, Computer Science, Robotics, or a related field, with a focus on sensor fusion, computer vision, signal processing, and/or machine learning. Experience with deep learning optimization for edge deployment, including quantization, pruning, and knowledge distillation.
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Experience in two or more applicable data science disciplines: statistical modeling, machine learning, data mining, time series data analysis, or data engineering. Some example statistical and machine learning methods we use on the data science team include Regression Analysis, Naive Bayes, Random Forests, PCA/LDA/IDA, and Neural Networks and related architectures (CNN, RNN, Transformers, LSTMs.
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learning job Title: sr data scientist in Stanford, CA
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