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Machine Learning Engineering - Dallas, TX (Hybrid) Optimize machine learning algorithms and infrastructure for performance, scalability, and cost-efficiency. Deploy machine learning models into production environments, ensuring scalability, reliability, and real-time performance.
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Implement and develop machine learning-based protein design solutions for structure prediction, paratope/epitope analysis, affinity maturation, developability assessment, scaffold optimization, library design, immunogenicity and other protein engineering challenges.
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Drive cross-functional collaboration across our data science, machine learning, lab informatics, and medicinal chemistry teams to advance our computational infrastructure for our drug discovery and development mission.
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The Generative Unconstrained Intelligent Drug Engineering (GUIDE) project has an opening for a Senior bioengineer who is responsible for the efficient, smooth, and effective management of a team and simulation- and machine-learning-driven computational pipeline to redesign antibodies.
$191,220 - $245,544 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Design and implement deep-learning (DL) and machine-learning (ML) models to extract valuable insights from large repositories of time-series/biosensor data. As a Signal Processing Engineer, focused on Deep Learning, you will be part of a cross-functional team composed of Signal Processing, WHOOP Labs, Firmware, and Data Science.
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Prior intern/research lab experience using one of the Machine Learning Engineering / Data Engineering tools – Databricks, Snowflake, Airflow · Extensive Visualization Experience –Tableau/PowerBI work portfolio · Prior intern/research lab experience with ChatBot AI – NLP, Deep Learning, LLM.
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At 1910 Genetics, we put computation at the heart of drugdiscovery, blending expertise in computational chemistry, structuralbiology, pharmacology, genetics, data science, and softwareengineering to develop drugs for previously undruggable targets.
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Research and apply emerging forms of statistical, machine learning (neural networks / deep networks, ensemble methods, natural language processing, computer vision) and engineering methods to Royal Caribbean Cruise Lines, data science and AI ecosystem.
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5+ years of experience in data analysis or data science, with 3+ years focusing on machine learning problems, ideally in a relevant space (KYC, sanctions detection, anti-fraud detection, treasury management, crypto/blockchain data science.
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FM Global is seeking a Machine Learning Operations Data Engineer II to join our AI/ML team to support Machine Learning Engineering, working very closely with Data Science, Data Engineering, Subject Matter Experts and Solution Architecture teams.
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MS, or above in Machine Learning, Computer Science, Engineering, or a related technical discipline or equivalent experience What we offer you The chance to be part of a truly mission driven organisation and an opportunity to shape the future of autonomous driving.
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Comprehensive technical expertise and a research track record in one or more of the following domains: artificial intelligence/machine learning; advanced cybersecurity methods and technologies; computer science and engineering; software engineering; modeling and simulation; model-based systems engineering; and/or computational science and engineering.
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PSC advances science across a wide spectrum of fields including artificial intelligence / machine learning, medical imaging, weather modeling, cell biology, and genomics. Master’s or PhD degree required, with preferred education/experience in Biology, Biomedical Informatics, Biomedical Engineering, Computational Biology, Computer Science, Data Science, AI/ML, or related fields.
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As a Machine Learning Engineer Co-Op, you will work closely with cross-functional teams, including Data Science, Software Engineering, and Product Management, to develop machine learning models, implement solutions, and ensure the successful deployment and maintenance of these models in production.
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TheMachine Learning Engineerrole requires experience in Azure OpenAI, Large Language Models (LLMs), Python, and proficiency in developing applications using Streamlit, LangChain, Databricks, and Python frameworks like Flask and FastAPI. The ideal candidate will be adept at building scalable machine learning models and deploying them to production environments, and also being part of customer success teams.
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machine learning science engineering jobs Title: engineering Company: University Of California Santa Cruz in Anoka, Minnesota
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