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As a Senior Software Engineer in the Central AI team, you will build and maintain the core infrastructure to allow machine learning engineers and data scientists to develop, train, evaluate, deploy, and operate Machine Learning models and pipelines.
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Key Qualifications Experience in machine learning, deep learning, information retrieval, natural language processing or data mining Experience of prompt engineering, fine-tuning, evaluating, and developing data collection/annotation/management tooling for LLMs. Proficiency in one of following languages: Python, Go, Java, C.
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Experience in one or more of the following areas: NLP, Ranking, Ads, search engine, recommender system, distributed system, and machine learning. Responsible for the research and application of the company's large-scale models, exploring new applications and solutions for related technologies in the fields of search, recommendation, advertising, content creation, and customer service, to meet the growing demand for intelligent interactions from users and comprehensively improve their way of life and communication in the future world.
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Experience with large scale distributed systems, data processing pipelines and machine learning training and serving infrastructure. Prior experience building machine learning systems in production such as enabling data analytics at scale.
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Working knowledge in one of the following areas: machine learning, deep learning, backend, large-scale systems, data science, full-stack Experience with Big Data Frameworks, such as hadoop, spark, flink, etc.
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PhD or advanced degree in computer science, machine learning, cognitive science, psychology, economics, or similar (preferred). Grid Dynamics provides digital transformation consulting and implementation services in omnichannel customer experience, big data analytics, search, artificial intelligence, cloud migration, and application modernization.
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From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.
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Experience in building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization, or search, etc. Coursework or thesis in machine learning, data mining, information retrieval, statistics or natural language processing.
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Or Python, winners of competitions such as ACM/ICPC, NOI/IOI, Top Coder, Kaggle, etc. Familiar with NLP, CV-related algorithms, technologies, and familiar with large-scale model training and RL algorithms are preferred.
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Experience in building large-scale machine learning infrastructure for online recommendation, ads ranking, personalization, search, NLP, etc. Preferred to have knowledge of machine learning, data mining, information retrieval, statistics or natural language processing.
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Whether you join our Statistics, Optimization, or Machine Learning teams, you’ll be challenged to harness Target’s impressive data breadth to build the algorithms that power solutions our partners in Marketing, Supply Chain Optimization, Search and Personalization rely.
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Whether you join our Statistics, Optimization, or Machine Learning teams, you’ll be challenged to harness Target’s impressive data breadth to build the algorithmsthat power solutions our partners in Marketing, Supply Chain Optimization, Search and Personalization rely on.
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Drive the team to leverage the latest techniques in data engineering, machine learning, and data visualization to build a suite of both user-facing and internal-facing capabilities that enable fast resolution of customer issues to keep the customers focused on getting the most value out of the Snowflake platform.
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Snowflake has a vast amount of diagnostic and machine data that the team will use to create large scale batch and streaming data pipelines, visualizations, and applications to extract insights and intelligence from to get customers past roadblocks and back to consuming the Snowflake platform.
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They engineered Snowflake to power the Data Cloud, where thousands of organizations unlock the value of their data with near-unlimited scale, concurrency, and performance. Own systems that include diagnostics visualization, intelligent search, predictive issue detection, and workforce management systems.
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