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Experience in building, deploying, maintaining and sunsetting large-scale machine learning models and systems in production environments using TensorFlow, Keras, PyTorch, Scikit-Learn, AWS, Azure, Google Cloud.
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Rhizome is seeking a Software Engineer who can scale our Data Engineering capacity and contribute to Machine Learning development at the enterprise scale. Successful candidates will also have practical experience building large scale ETL pipelines on AWS or GCP for data engineering, feature extraction, statistical analysis, and correlations.
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The team develops and enhances task-specific Large-Language-Models (e.g. Text Summarization models, Multi-turn Dialogue Language models) and foundational machine learning models (e.g. Named-Entity Recognition, Sentiment modeling) for a wide range of applications in Airbnb.
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As an applied scientist on Twitch's Community team, you will use machine learning to develop data products tackling problems such as harassment, spam, and illegal content. - Collaborate with Community Health's engineering and product management team to productionize your models into flexible data pipelines and ML-based services.
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The Personalization Machine Learning (PML) team strives to create a personalized experience on Cash App and Afterpay by tailoring recommendations and search ranking results for our customers.
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We have a highly international team made up of expert machine learning practitioners and dedicated software and hardware engineers which match well with the global nature of our business.
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We are looking for a highly motivated and experienced Machine Learning Engineer to join our team and help us shape the future of machine learning at Grindr. You will collaborate with a world-class team of research scientists and engineers to develop new machine learning algorithms, systems, and applications that will bring step-function impact to the business metrics.
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You will lead efforts to build machine learning models that predict disease from whole genome sequence data. Experience with PyTorch, JAX, TensorFlow or equivalent machine learning framework.
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Deep understanding and experience building machine learning (ML), high performance computing (HPC), or graphics applications in cloud environments. Crusoe Cloud is a managed cloud services platform that brings scalable, clean, low-cost cloud computing to the world's bravest thinkers in computationally intensive fields including artificial intelligence, graphics rendering, scientific computing, and computational biology.
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Typical role includes working on a wide range of activities such as working with complex structured and unstructured datasets, developing/recommending novel machine learning tools, data visualizations, automation of analytics workflows, disease progression models, mechanistic and empirical PK/PD models, clinical trial simulations, literature meta-analysis using quantitative approaches and statistical modeling of historical and preclinical data.
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Familiarity with Artificial Intelligence/ Machine Learning, Robotics Process Automation and Blockchain and their impact on the regulatory environment. In addition, our team manages, automates, analyzes and visualizes data using data transformation and visualization tools to support process improvement initiatives.
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Knowledge of machine learning and AI methods and proficiency with scripting and executing data analytics algorithms and models with hands-on experience using a modeling and simulation software (e.g. Python, MATLAB, R, NONMEM, SAS, S-Plus, etc) is a must.
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The ideal candidate should have a strong background in machine learning, deep learning, LLMs, and data analysis, as well as experience in developing and deploying AI models on hardware/wearables.
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The Computational Biology group in the Clinical Biomarkers & Diagnostics (CBD) department at is seeking a highly motivated Sr data scientist to join our team and contribute to develop machine learning modeling and prediction pipelines using multi-modal biomarker data from clinical trials.
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We have a fast-growing team that’s distributed around the world, with offices in San Francisco and New York City. Our team has years of experience building and operating business-critical machine learning systems at leading tech companies like Uber, Google, Meta, Airbnb, Lyft, and Twitter.
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team building services machine learning jobs Title: data engineer in San Francisco, CA
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