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Advanced knowledge of cloud computing technologies such as: Apache Spark, Azure Data Factory, Azure DevOps, Azure Client (Machine Learning), Hadoop, Microsoft Azure, Databricks, AWS, Google Cloud.
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Subject matter expertise in one or more of the following: Artificial Intelligence (AI), Natural language Processing (NLP), Machine Learning (ML), Statistical Models, and Text Analytics on large data sets.
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Hands-on experience with technology such as sensors and signal processing, machine vision and machine learning, and internet-of-things type technology. Knowledge of autonomous sensor deployment and AI-driven machine vision.
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At least 10 years of industrial/academic experience advancing the state-of-the-art machine learning-based research through demonstrable, verifiable technical results in the area of natural language processing, human language understanding, computational linguistics.
$122,200 - $220,900 a yearFull-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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Much of our work contributes to innovative research in the fields of sensor science, signal processing, data fusion, artificial intelligence (AI), machine learning (ML), and augmented reality (AR.
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As a Senior Reliability Engineer, you will play a critical role in ensuring the robustness, availability, and performance of our cutting-edge Data Engineering and Machine Learning Platforms.
$82,000 - $185,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Other technology (e.g., natural language processing, web-scraping techniques), Tuition Reimbursement, Personal Development & Learning Opportunities. Depending on your areas of work, experiences and studies, and interests, you will have the opportunity to serve clients regarding China, in cybersecurity and cyber threat intelligence, open-source Intelligence (OSINT), supply chain risk management, anti-money laundering and fraud, financial investigations, technology and solutions innovation, and much more.
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CUDA (Compute Unified Device Architecture) /OpenCL (Open Computing Language) Programming: Develop and optimize applications using CUDA or OpenCL, harnessing the full potential of GPU hardware for parallel processing, high-performance computing, and machine learning on Linux platforms.
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Processing and approving expense reports in Unanet will be the focus of the Expense Report Processing Specialist (+ expenses) Expense Report Processing Specialist may assist with system conversion related project - reviewing old information in Solomon and inputting into Sage Intacct.
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Desired experience includes analog to digital signal processing, underwater acoustics analytics, Fast Attack Submarine Sonarman Experience, and Submarine Total Ship Monitoring System operation (TSMS.
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Deep understanding of E-Commerce search solutions, query development, relevancy tuning, and implementing Natural Language Processing (NLP) use cases. Expertise in machine learning and Artificial Intelligence (AI.
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We offer a unique and immersive online learning platform, powering corporate technical training in fields such as Artificial Intelligence, Machine Learning, Data Science, Autonomous Systems, Cloud Computing and more.
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Additional preferred candidate experience would include airborne lidar trajectory processing (Applanix PosPac MMS, Novatel Inertial Explorer), and in extracting lidar using sensor vendor specific processing suites (Riegl RiProcess, Optech LMS, Teledyne CARIS, GeoCue True View EVO.
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Experience with the SEVIS Real-Time Interactive (RTI) data processing system. Experience with data processing systems used by U.S. universities to comply with the DHS Student and Exchange Visitor Information System (SEVIS.
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Sales Territory or Remote (Regardless of Location): $140,000 - $159,800 for Senior Machine Learning Engineer. San Francisco, California (Hybrid On-Site): $174,900 - $199,700 for Senior Machine Learning Engineer.
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machine learning natural processing jobs Title: specialist Company: Deloitte in Mclean, VA
FEATURED BLOG POSTS
Recruiting in a Recession: Hard Truths That Talent Acquisition Experts Must Accept
The summer had economists from around the globe embroiled in a debate about a possible recession coming in the next few years (or months). As of October 2022, the U.S. Labor Department data put the current inflation rate at 7.7%. The recent layoffs in the tech industry are just the first of what is soon to be a string of cutbacks by companies looking to save costs. For recruiters, this means freezes in hiring and fewer openings. It will also include the uphill task of finding the best candidates for them from the coming influx of recently laid-off job seekers. Now is probably a good time to brace for tough times in the next few years in the talent acquisition industry. To survive and thrive recruiting in a recession, here are some hard truths you will need to accept.
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Talent acquisition is a multi-stage process where candidates undergo various application steps before getting hired. The unfortunate reality is that it is a labor-intense system, with the hiring manager and recruiter often handling all of the work on their own. Ask any one of them, and you will hear about the overabundance of applications and the demanding task of filtering through them to find the best candidates. The quality of talent suffers under the weight of all that work on one person's hands. It's not easy, but as many companies are starting to realize, there is a better way. The future of talent acquisition lies in collaborative recruiting!
4 Talent Acquisition Trends Going Into 2023
For better or worse, a side effect of the COVID-19 pandemic was a marked shift in talent acquisition practices worldwide. With the struggle to retain talent that began in 2020, companies have had to rethink recruitment strategies. The result has been new talent acquisition trends that are well on their way to becoming commonplace. These are the practices that are going to become even more widespread going into 2023.