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Data science, machine learning, optimization models, Master’s degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch)Primary Location.
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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Publications or active peer reviewer in related journals or conference, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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You will identify and address an applied machine learning problem in the enterprise co-pilot system, working with ML Engineers across the organization to adopt and productionize LLM usage.
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The Principal Data Scientist / Applied Machine Learning Scientist Computational Advertising is a critical data science/applied machine learning role that uses cutting-edge machine learning, deep learning, big data mining and optimization techniques to solve the challenging problems from ads relevance, ranking to campaign optimization.
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Solid background in the fundamentals of computer science, distributed systems, concurrency, resiliency, caching, large scale data processing, database schema design and data warehousing.
$160,000 - $203,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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ResponsibilitiesDevelop, optimize and deploy ultra-low latency Deep learning/ Machine Learning algorithms for Rivian ADAS and Autonomy use cases. Research and development experience in the following areas:model compression and neural architecture search techniquesknowledge distillation, pruning, quantization and quantization aware trainingoptimizing and deploying inference on various embedded processorsExperience defining compute architecture for efficient Deep learning inferencing.
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Experience in data science, statistics, optimization, machine learning, and/or deep learning. Experience with machine learning frameworks (e.g. PyTorch, Tensorflow, etc.
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Strong writing and analytical skills in machine learning. Experience with signal processing of medical imaging data. Create databases and reports, develop algorithms and statistical and/or computational models, and perform data analyses appropriate to data and reporting requirements.
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Programming skills and familiarity with GPU based technologies like CUDA, CuDNN and TensorR and background with deploying machine learning models on data center, cloud, and embedded systems.
$144,000 - $270,250 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Passionate about natural language understanding, information retrieval, or machine learning. This is achieved through our cutting-edge in-house LLM training and inference infrastructure, which allows us to create intelligent LLMs tailored to our customers' needs and serve the model at scale with low latency.
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Stay up-to-date with the latest advancements in data science, machine learning, and artificial intelligence, and identify opportunities for their application in the streaming industry.
$150,000 - $200,000 a yearFull-timeExpandApply NowActive JobUpdated 5 days ago - UpvoteDownvoteShare Job
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The Afero next-generation IoT PaaS accelerates time to market, provides hardened security from device to cloud and from factory to customers, simplifies onboarding to help maximize customer connections, and uses data ontology to streamline machine learning.
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Google Cloud High Performance Computing (HPC) Machine Learning (ML) Networking is responsible for innovations and optimizations of the networking stack to make Machine Learning High Performance Computing (HPC) workload performant on Google Cloud Platform (GCP) and in Google production.
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Develop novel and accurate NLP algorithms and systems, leveraging Deep Learning and Machine Learning on big data resources. Experience with the development of enterprise level AI, Machine Learning, and Deep Learning platform involving big data management and GPU compute.
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Prior experience building and deploying machine learning models in production at scaleUnderstanding of data and ML systems with the ability to think across stack layers - REST APIs, microservices, data ingestion and processing systems, and distributed systems.
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