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We are looking for an experienced Machine Learning Engineer to help us in our journey to scale end-to-end neural networks for autonomous driving. Developing and implementing machine learning models and robotics systems to enhance the capabilities of our Autonomous Vehicle stack.
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You will be responsible for developing, validating, and implementing cutting-edge machine learning algorithms, including use of Large Language Models (LLM) applied to diverse healthcare data sources, e.g. electronic medical records and to generate medical reports.
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Staff Machine Learning Engineer, Model Optimization Mountain View, California, United States. These efforts have resulted in making machine learning more accessible to teams at Waymo, including Perception, Planner, Research and Simulation, ensuring greater degrees of consistency and repeatability, and addressing the "last mile" of getting models into production and managing them once they are in place.
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The project involves the development and application of machine learning and deep learning approaches for spatial transcriptomics, spatial proteomics, and their integration with H&E histopathology data.
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We're looking for an experienced Senior Product Manager to lead and build highly scalable machine learning products that determine what ads billions of TikTok users see and engage with.
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Strong experience with machine learning, particularly in the context of behavior prediction for autonomous driving. Train advanced behavior prediction models, including LSTM, Transformer, and other relevant machine learning models.
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You are an individual with a deep understanding of computational genomics, bioinformatics, machine learning, statistics, next-generation sequencing, and single-cell multi-omics. Develop novel machine learning algorithms to get biological insights from the multifaceted genomic datasets.
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This team invents and applies machine learning, data mining, and informational retrieval algorithms to understand, identify, and improve web content discovery. RoleWe are looking for a hands on Engineering Leader to spearhead the Data Science and Machine learning charter.
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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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Proficient with ML frameworks such as Tensorflow, Keras, JAX.Experience with machine learning and geophysics algorithms or applications such as: deep learning, computer vision, optimization, denoising, signal processing or time-series analysis.
$105,000 - $134,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Our team has extensive experience in GNSS system architecture, multipath, signal processing, ASIC design and AI/machine learning, and has collectively filed over 200 career GNSS patents.
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Senior Machine Learning Engineer. Bachelor’s Degree, or higher-level degree, in Computer Science, Engineering, or related quantitative field that demonstrates knowledge of AI, machine learning, optimization, random processes, sensor signal processing, or mathematical modeling.
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Machine Learning Engineer. Extensive exp in Computer Vision, Machine Learning, Deep Learning, or other relevant areas of Artificial Intelligence (e.g., as evidenced by industry experience, publication record.
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Excellent debugging, analytical and problem-solving skillsA deep understanding of machine learning foundations and can develop technical solutions for new problemsNice to have:Experience working with cloud data processing technologies (Apache Spark, ElasticSearch, Presto, SQL, etc.
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Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as natural language processing (NLP), speech recognition and analytics, time-series predictions or recommendation systems.
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machine learning lead jobs Title: data engineer Company: Amazon in Stanford, CA
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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.