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The company uses computer vision and machine learning technology to analyze entire buildings: from full HVAC systems to solar panels to pavement. Machine Learning | AI | TensorFlow | Deep Learning | AWS | PyTorch | Django | MLOps | Computer Vision | Imaging | Python | GIS | Location Data | Aerial Data.
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Prepare R&D machine learning constructs for deployment in production cloud-based systems. 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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Job Description: The artificial intelligence platform architect will lead the development of autonomous driving software solutions with modular design and integrated platform to handle machine learning and deep learning simulation systems.
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Have 3 or more years of experience applying signal processing, pattern recognition, machine learning, deep learning, or related techniques with respect to information extraction from electromagnetic systems.
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A healthcare client of ours in the Philadelphia area is looking for a Machine Learning Engineer to join our Data Science team and help us build, optimize, deploy, and maintain machine learning systems and pipelines in Google Cloud Platform (GCP) production environments for Generative AI and more traditional Machine Learning projects.
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Our work depends on TS/SCI cleared Machine Learning Engineer joining our team to support our intelligence customer in Springfield, VA or St. Louis, MO. Education: Bachelor or Master' Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or equivalent experience in lieu of degree.
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Familiarity with machine control systems including sensing, communication, and system level controls for electric plants, machinery, and electromechanical systems. The Physics-based Simulation and Machine Learning Engineer will work both individually and in concert with other engineers, subject-matter experts, and program management to develop suitable modeling strategies that can be used for advanced process control and decision making.
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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. As a Machine Learning Engineer you will help ensure today is safe and tomorrow is smarter.
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Expert level of understanding of NLP, NLU and Machine learning/Deep learning methods. Do applied research on a wide array of text analytics and machine learning projects, file patents and publish the papers.
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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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You will develop robust and scalable data pipelines, ML model hosting, and backend services for enterprise line of business applications in Python, VertexAI, Airflow, dbt, Flask, PostgreSQL, BigQuery, Docker, Kubernetes, and more.
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The essentials Seven or more years of experience designing, configuring, implementing, and managing machine and deep learning algorithms and HR analytics. Strong knowledge of statistical and machine learning algorithms and their applications, including regression, classification, clustering, NLP, deep learning, and optimization.
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Committees may include topics such as AI/machine learning, ethics, natural language processing, data definitions, data governance, genomic/pathology/radiology and other specialized clinical data systems, Epic, internal and external data reuse, learning health systems and translational science.
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Expected Qualifications: Demonstrated knowledge and teaching experience in at least one of the following areas: Statistics, Data Analytics, Data Visualization with different tools, Python, Data Management & SQL, Data Engineering, R Programming, and/or Machine Learning.
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If applicable)Years of experience: At least 10 years of experience in business intelligence, data integration, and AI/machine learning, with substantial experience in leadership and cross-functional strategic initiatives.
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