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We seek machine learning engineers with a passion for sports to implement, automate, and optimize the quantitative models that power our world-class sports intelligence platforms in baseball, basketball, cricket, football (American), hockey, soccer, and tennis.
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Strong understanding in the machine learning and data science technical ecosystems (e.g., Tensorflow, Pytorch, MLflow, Ray, LangChain, Data lake house (Databricks), Snowflake, SageMaker, Scikit-learn, etc.
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Hands on experience working in an infrastructure managed services environment, supporting complex engineered solution in production with Artificial Intelligence/Machine Learning/High Performance Compute Systems and Platforms, Converged/ Hyper-Converged infrastructure along with fluency in AI/ML pipelines, Nvidia GPU optimization, InfiniBand networking, Machine Learning operating systems such as cnvrg.io, Compute Orchestration Platform such as runai etc.
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The Senior Principal Engineer- Site Reliability Engineering supporting Artificial Intelligence/Machine Learning/High Performance Compute Solutions, Service Delivery will be responsible for providing the primary management, administration, support, and ongoing maintenance of customer Platforms within a 24x7x365 datacenter environment.
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We are looking for a hands-on machine learning engineer who is passionate about crafting, developing, and deploying cutting edge AI/ML solutions which will impact millions of customers. Familiarity with one or more deep learning software frameworks such as Tensorflow, PyTorch.
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Good understanding of Machine Learning and Deep Learning technologies and experience using one or more deep learning frameworks (e.g., PyTorch, JAX, TensorFlow. We are looking for a software engineering manager to lead our development efforts for math libraries in the AI, HPC, and Quantum Computing (QC) domains.
$216,000 - $414,000 a yearFull-timeExpandApply NowActive JobUpdated 1 days ago - UpvoteDownvoteShare Job
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Job DescriptionThe Automotive High Performance Compute (HPC) team is seeking a Sr. Director of systems architecture & product management to manage the system engineering functions for automotive SOC product line of all our current and next generation HPC products, with a critical role to accelerate the development of next generation of AI, Machine Learning (ML) and automotive networking solutions in the field of ADAS/Autonomous Driving and Software-Defined-Vehicle (SDV.
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As a Computer Scientist/Engineer on the Fraud Team, you will be at the forefront of developing and executing an innovative strategy utilizing Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Large Language Models (LLM), and Multi-Agent Systems to combat a variety of fraudulent activities.
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AWS Machine Learning accelerators are at the forefront of AWS innovation and one of several AWS tools used for building Generative AI on AWS. The Inferentia chip delivers best-in-class ML inference performance at the lowest cost in cloud.
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We design and build every component of our hardware and software to come together into products that our customers use for accelerated computing through Machine Learning acceleration and FPGA acceleration.
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The combination brings together healthcare expertise, AI/machine learning technology and data-driven analytics solutions to deliver innovative solutions and value to our customers and the healthcare ecosystem.
$80,000 - $140,000 a yearFull-timeExpandApply NowActive JobUpdated 0 days ago - UpvoteDownvoteShare Job
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If you are a skilled AI Machine Learning Engineer looking for a challenging and rewarding opportunity, we encourage you to apply! As an AI Machine Learning Engineer, you will be responsible for developing, testing, and implementing machine learning algorithms and models to solve complex problems and improve business outcomes.
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You’ll gain experience as an embedded software architect with strong data science skills and have the opportunity to develop and deliver enterprise-grade Artificial Intelligence (AI) and Machine Learning (ML) solutions across the client devices portfolio.
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Experience with distributed training techniques for machine learning models, leveraging deep learning frameworks such as TensorFlow, PyTorch, or JAX to accelerate model training and handle large datasets efficiently.
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The Data Scientist will contribute to the Fraud Analytics unit’s efforts to develop and implement algorithms, artificial intelligence/machine learning (AI/ML) models, and predictive analytics to generate insights and actionable leads to identify and respond to risks of fraud, waste, and abuse.
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machine learning jobs in Austin, TX
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