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3+ years of shown experience in working with AI technologies and tools, such as natural language processing (NLP), AI generation, machine learning algorithms, deep learning, etc.
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Strong foundation in data infrastructure tooling (e.g., Spark, Airflow, AWS, Databricks) and machine learning infrastructure. Oversee the development and maintenance of scalable data and machine learning infrastructure utilizing tools such as Spark, Airflow, AWS, and Databricks.
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Deep Learning, Red Hat OpenShift, Tensorflow. 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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Kelly Services is seeking an FEA Engineer / Software Engineer / Machine Learning Engineer for one of our top clients. FEA Engineer / Software Engineer / Machine Learning Engineer, Cupertino, CA (Hybrid.
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Expertise in internals of deep-learning frameworks like PyTorch, JAX, TensorFlow, etc. Expertise in building deep-learning models in PyTorch, JAX, or TensorFlow. Optiver is a seeking a Machine Learning Research Engineer to join our team, focusing on a pivotal AI initiative.
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Experience developing, implementing and applying advanced statistical or machine learning models and algorithms using modern software libraries such as PyTorch, TensorFlow, or similar as evidence through medium to large scale deep learning models and experiments.
$11,040 a monthFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Deliver simple solutions to complex problems as a Machine Learning Engineer at GDIT. Here, you'll tailor cutting-edge solutions to the unique requirements of our clients. Our work depends on TS/SCI cleared Machine Learning Engineer joining our team to support our intelligence customer in Springfield, VA.
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Argonne’s Leadership Computing Facility (ALCF) and Mathematics and Computer Science Division (MCS) is looking for a Postdoctoral appointee working at the intersection of scientific machine learning SciML and large-scale simulation codes.
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You will apply statistical, programming, data wrangling, AI machine learning, data visualization and other techniques for analyzing data. Takeda’s Analytics Leadership Development Program (ALDP) blends real-world, hands-on experience with an extensive pharmaceutical industry overview.
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As a Senior Data Scientist for our team, you'll leverage your expertise in machine learning, deep learning and reinforcement learning to create large-scale, personalized recommendation models for our omni-channel retail platform, with a focus on exploring and implementing cutting-edge technologies like Large Language Models (LLM's.
$234,000 a yearFull-timeExpandApply NowActive JobUpdated 18 days ago - UpvoteDownvoteShare Job
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We are a growing subsidiary of a large public company that is hiring a talented Lead ML Engineer / Senior Machine Learning Engineer! 100% REMOTE Senior ML Engineer / Lead Machine Learning Engineer Needed for Growing Subsidiary of a Large Public Company.
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Experience with frameworks and libraries for machine learning & AI such as scikit-learn, HuggingFace, PyTorch, Tensorflow/Keras, MLlib, etc. 5+ years of practical experience in building, evaluating, scaling, and deploying machine learning pipelines with Python, preferably within the AWS ecosystem.
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Ability to design, train, and evaluate machine learning and AI models while adhering to best practices including model selection, validation, bias/variance tuning, performance assessment, sensitivity analysis, dimensionality reduction, etc.
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Experience in assessing and implementing new data tools to enhance the machine learning stack. Familiarity with Snowflake, Monte Carlo, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, EMR, Sagemaker, DataDog, PagerDuty, Atlan, Data Observability tools and Data Governance tools.
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Expertise in data strategy, data architecture, data engineering, advanced analytics, data science, data mining, and machine learning. Has experience managing on-shore and off-shore teams and knows how to optimize operating models to ensure data entry, data operations, and data thought leadership is being performed by the right talent base with scale and speed.
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