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Knowledge of data science, machine learning, and artificial intelligence is required. Knowledge of data science, artificial intelligence, and/or machine learning is required.
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You will deploy and deliver technical solutions at the intersection of computational chemistry and machine learning, supporting research directions in molecular design across broader gRED and Roche.
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Programming may be in areas such as DeFi, machine learning, data science, ESG, coding, on the technical side. MFE students are pursuing highly technical careers in areas such as quant trading, quant research, data science, machine learning, risk management among others.
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Work with the embedded Machine Learning Engineers on the team and ML platform services to deploy models to the production environment and monitor ongoing performance. Our machine learning systems monitor and surface suspicious activity (money laundering, illegal activity and terms of service violations) for agent review.
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The incumbent will apply advanced data analysis skills and machine learning in collaboration with a group of computational and biological scientists to perform translational oncology research around multimodal translational data sets for programs in clinical development.
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Experience with cloud platforms such as AWS, Azure, or Google Cloud, and proficiency in using relevant tools and services for deep learning development and deployment. Model Deployment: Architect scalable and reliable deep learning pipelines for deployment in production environments, collaborating with DevOps engineers to ensure seamless integration and monitoring.
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Apply mathematical (geometry, linear algebra, numerical methods, error analysis) and machine learning methods to prototype and develop algorithms for converting raw depth sensor data into point clouds, 2D and 3D tracking like SLAM, 3D point cloud reconstruction, registration, and classification as well as texture projection and completion.
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Work cross-functionally with product, platform, security, and data engineering teams to unlock new machine learning applications. Passion about continuously learning new technologies, frameworks, and services.
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Growth and learning opportunities from a tech environment include working closely with an international team of scientists, engineers, platform architects, programmers, executive staff and professionals.
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Candidates should be familiar with some of the following: subsurface imaging with passive seismic data, geomechanics, earthquake characterization, seismicity forecasting, seismic wave physics, big data processing, machine-learning-based processing, distributed fiber optic sensing, seismic sensors and deployment, hydraulic fracturing, and/or geothermal exploration.
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As a Machine Learning engineer, you will help support all aspects of the machine learning lifecycle, including the research platform, training and deployment pipelines, labeling and serving infrastructure.
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Familiarity with cloud platforms (e.g., Azure, Dell Boomi) and deployment techniques. As a Senior Full Stack Developer, you will be crucial in designing, developing, and maintaining our e-commerce platform.
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Proficient with the core concepts of at least one programming language such as Python, R, and SQL and adept with machine learning frameworks such as TensorFlow and PySpark. Accredited Artificial Intelligence (AI) / Machine Learning (ML) certifications (, AWS, Google Data Analytics, Microsoft Azure) Information for applicants with a need for accommodation: The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.
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Preferred Qualifications Familiarity with biological data formats, concepts, and computational models is a plus The salary range for Redwood City, CA: Staff Machine Learning Engineer: $232,900 - $315,100 The salary range for San Diego, CA: Staff Machine Learning Engineer: $221,000 - $299,000 Exact compensation may vary based on skills, experience, and location.
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Engineer and implement operating system deployment, software and patch distribution management such as RIS/WDS, WPE, MECM/SCCM/OSD, Intune, WSUS.Evaluate and recommend workstation hardware, such as desktop, laptop, tablet computers, peripherals and accessories.
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deployment machine learning jobs Title: platform engineer Company: Baird in San Francisco, CA
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