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The Machine Learning Engineer role 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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Our current backend stack is Python, Rust, TensorRT-LLM, Kubernetes, AWS. You will work on designing and implementing machine learning models that drive our recommendation systems, retrieval algorithms, and classifiers.
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Work cross-functionally with product, platform, security, and data engineering teams to unlock new machine learning applications. Be responsible for designing, building and managing the distributed services and pipelines for Underwriting & Credit Cash App.
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Advanced degree (Ph. D. preferred) in a field like Applied Linguistics, Computational Linguistics, or Data Science, with a focus on building and deploying machine learning models for text analysis.
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Proven experience as a Data Scientist, with a focus on machine learning, LLM, and Deep Learning. Stay updated on the latest advancements in machine learning, particularly in the areas of LLM and Deep Learning, and apply cutting-edge techniques to solve complex problems.
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If you've used Disney+, Peacock, or other top streaming platforms, you've already benefited from Conviva's technology.
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Performs various projects requested or assigned by the Personal Lines Underwriting Manager. Communicates to the Underwriting Manager and Territory Manager any underwriting quality or integrity concerns with individual agents.
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Design, develop, and deploy advanced machine learning models and algorithms, leveraging deep learning frameworks such as PyTorch and TensorFlow. - Conduct ongoing research to stay abreast of the latest developments in machine learning, deep learning, and data science, and apply this knowledge to enhance project outcomes.
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Responsibilities Play a critical role in setting the direction and goals for one of our ML teams in terms of project impact, ML system design and ML excellence Be a go-to person for the most complex production performance and evaluation issues requiring an in-depth knowledge of how the machine learning system interacts with systems around it Lead and review technical designs for projects within your team, actively contributing to team-level strategies setting clear, achievable engineering goals.
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Deep knowledge of machine learning, both theory and application. 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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As our first Data Science hire focusing on Machine Learning at Circle, you’ll start by focusing on two critical problems: helping identify transactions and actors who might be acting illicitly and helping us better balance our reserves across a series of accounts to reduce systemic risk inside the company.
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In this role, you will collaborate with researchers to develop machine learning methods for computational biology and drive the implementation and testing of novel research ideas using cutting-edge deep learning techniques.
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While the focus of this job will be machine learning applications, there are times where we need to use data science to solve other problems and machine learning is not the only or optimal option.
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Technical Program Manager, Machine Learning page is loaded. 2+ years of experience as a developer, data scientist or machine learning engineer in addition to 2+ years of experience in a TPM role Prior development or management experience on a Machine Learning Platform in a large tech company Hands-on experience with cloud technologies and service oriented architectures Track record of using data analytics for improving SW operations and organizational efficiency Advanced degree in an analytical field.
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Machine Learning and Deep Learning: Good understanding of: ML algorithms like linear regression, logistic regression, etc., supervised, unsupervised, and reinforcement learning, AI Frameworks like TensorFlow, PyTorch, scikit-learn etc., Neural network, NLP, computer vision, and predictive analytics.
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underwriting manager machine learning jobs Title: deep learning Company: Capital One
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