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Demonstrated professional or academic experience with deep learning frameworks such as PyTorch or Tensorflow to optimize convolutional neural networks (CNN) such as ResNet or U-Net for object detection or segmentation tasks using satellite imagery.
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Familiar with PySpark and data processing libraries, machine learning frameworks (like Tensorflow, Keras or PyTorch), and other machine learning libraries. + Familiar with Machine Learning life cycle, with knowledge of feature stores, MLflow, model registries, model deployment, model serving, and model monitoring.
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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.
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Deep Learning: Understanding neural networks, backpropagation, architectures like CNNs, RNNs, transformers, and tools like TensorFlow, PyTorch, or Keras. Support GDIT's FedCiv business areas by developing solutions to deliver capabilities, including Data Science and Machine Learning.
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As a Senior Machine Learning Research Scientist, you will apply your technical skills to a variety of exciting programs and datasets, primarily focused on remote sensing applications (acoustic, RF, and other.
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Experience with advanced machine learning techniques and packages (TensorFlow, PyTorch, Keras, Scikit-Learn, Pandas, NumPy, Seaborn, Orange3, SciPy, Matplotlib, Theano) Drive the development, ongoing maintenance, and governance of advanced machine learning models and AI pipelines in a growing AI environment.
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Experience with machine learning frameworks such as TensorFlow or Pytorch, Lightning, Mosaic ML etc. San Francisco, California (Hybrid On-Site): $248,700 - $283,800 for Sr. Lead Machine Learning Engineer.
$234,700 a yearRemoteExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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New York City (Hybrid On-Site): $234,700 - $267,900 for Sr. Lead Machine Learning EngineerSan Francisco, California (Hybrid On-Site): $248,700 - $283,800 for Sr. Lead Machine Learning EngineerRemote (Regardless of Location): $198,900 - $227,000 for Sr. Lead Machine Learning Engineer.
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San Francisco, California (Hybrid On-Site): $213,400 - $243,500 for Lead Machine Learning Engineer. New York City (Hybrid On-Site): $201,400 - $229,900 for Lead Machine Learning Engineer.
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Preferred skills: NLP, Text mining, Tableau, PowerBI, Databricks, Tensorflow. We want Data Science/Machine learning/Data Analyst and Java Full stack candidates. Currently, We are looking for entry-level software programmers, Java Full stack developers, Python/Java developers, Data analysts/ Data Scientists, Machine Learning engineers for full time positions with clients.
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Desired Skills are:Knowledge of machine learning frameworks such as TensorFlow, Pytorch, or others. Understanding of machine learning concepts, model evaluation strategies, and lifecycle (feature generation, model training, model development, bath and real time scoring) and engineering considerations.
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Experience with deep learning tech such as PyTorch, TensorFlow, etc. Knowledge of deep learning algorithms for image science, such as YOLO, SSD, DetectNet, etc. We need an experienced data scientist to work on all stages of the data science pipeline, to include applying existing machine learning software across large hyperparameter trade spaces, developing new machine learning algorithms and evaluating opportunities within that AI/ML industry that can enhance our workflow.
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Experience with modern machine learning development frameworks (e.g. Keras, TensorFlow, PyTorch) Experience modeling waveform and signal processing algorithms (e.g. Matlab) Additional Desired Skills: Experience with real-time software development Experience with containers and orchestration solutions (e.g. Docker, Kubernetes) Proficiency in prototyping, simulating, and testing systems in MATLAB is a must.
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Experience with machine learning architecture and neural network design and implementation, utilizing tools such as Tensorflow and/or Pytorch. Utilize machine learning (ML) algorithms in order to tackle challenging IRCM & MWR problems.
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Experience with data science techniques, classical machine learning, and deep learning for anomaly detection. Experience with Jupyter, Spark, and either Tensorflow or PyTorch. Provide technical leadership in developing capabilities to detect insider threats among large user communities, leveraging combination of statistical, classical machine learning, and deep learning methods.
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