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Experience in building, deploying, maintaining and sunsetting large-scale machine learning models and systems in production environments using TensorFlow, Keras, PyTorch, Scikit-Learn, AWS, Azure, Google Cloud.
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Python ecosystem preferred, R will be acceptable, machine learning libraries & frameworks (e.g. TensorFlow, PyTorch, scikit-learn) and familiar with data processing and visualization tools (e.g., SQL, Tableau, Power BI.
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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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Familiarity with one or more machine learning libraries or frameworks such as: PyTorch, Tensorflow, scikit-learn. Experience using cloud technologies such as AWS with services such as S3, Lambda, Athena, API Gateway, SageMaker, Glue, ECR, ECS, EKS, Quicksight, Step Functions, VPC, Cloudwatch, IAM, Kinesis, and Bedrock.
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Experience in statistical learning methods for GTM attribution and building from the ground up end-to-end data driven attribution systems which may include marketing mix models (MMM) and algorithmic multi-touch attribution (MTA.
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Familiar with one machine learning development framework such as PyTorch, TensorFlow, MXNet. You will work with a team of machine learning researchers to build AI software systems, learn about deep learning algorithms, and use your technical skills to advance autonomous driving.
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Conducting experiments on state-of-the-art machine learning models using Tensorflow for real world, large scale problems. Conducting experiments on state-of-the-art machine learning models using Tensorflow for real world, large scale problems.
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Proficiency with a deep learning framework such as TensorFlow or Keras. The Machine Learning Engineer III will be a member of the Learning and Active Perception (LEAP) group in AV’s MacCready Works division and develop a variety of innovative computer vision (detection, classification, localization, and tracking) and other analytics solutions for defense and commercial applications.
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Leverage data science frameworks like TensorFlow, PyTorch, and Spark ML to build robust and scalable machine learning solutions. Join the Anheuser-Busch NAZ Tech Data and Analytics Team as a Machine Learning Engineer.
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Hands-on experience with deep learning frameworks (e.g. PyTorch, TensorFlow, JAX) and a solid understanding of neural network architectures for tasks like image recognition, segmentation, and generative modeling.
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Awareness of data science platforms (like Databricks or SageMaker), distributed engines (like Spark and AWS cloud), and CI/CD pipelines and containerization are preferred. Familiarity with ETL, ML, or analytics technologies such as Scikit-learn, Pandas, OpenCV, NumPy, TensorFlow, or similar platforms and frameworks.
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Experience with ML systems and deep learning frameworks (e.g. PyTorch, TensorFlow, ONNX) You will have the opportunity to work on large-scale deployment of machine learning models for real-time inference.
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Strong knowledge, experience, and fluency in a wide variety of tools including Python with data science and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch), Spark, SQL; familiarity with Alteryx and Tableau preferred.
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We are looking for a senior Machine Learning Engineer with deep expertise in attribution space (just like you!) Additionally, we are dedicated to crafting exceptional data infrastructure and innovative solutions in the realms of data science and machine learning.
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Two years applied machine learning experience in a professional setting, familiar with at least one mainstream deep learning framework, such as TensorFlow or PyTorch. The Senior machine Learning Engineer will be working on different aspects of recommendation and user growth new initiatives, and the product will greatly impact Weee.
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