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Our team develops, implements, and maintains recommendation and personalization algorithms for Disney Streaming's suite of streaming video apps, notably Disney+ and Hulu. As a member of this team you will collaborate across Engineering, Product, and Data teams to apply machine learning methods to meet strategic product personalization goals, explore innovative, cutting edge techniques that can be applied to recommendations, and constantly seek ways to optimize operational processes.
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7+ years of experience developing machine learning models, performing large-scale data analysis, and/or data engineering experience. In-depth understanding of modern machine learning (e.g. deep learning methods), models, and their mathematical underpinnings.
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Algorithm Development and Maintenance: Utilize cutting edge machine learning methods to develop and implement in production algorithms for personalization, recommendation, and other predictive systems; maintain algorithms deployed to production and be the point person in explaining methodologies to technical and non-technical teams.
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Ability to gauge the complexity of machine learning problems and a willingness to execute simple approaches for quick, effective solutions as appropriate. Experience building and deploying full stack ML pipelines: data extraction, data mining, model training, feature development, testing, and deployment.
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Development Best Practices: Maintain existing and establish new algorithm development, testing, and deployment standards. 5+ years of experience developing algorithms for deployment to production systems.
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Experience deploying and maintaining pipelines (AWS, Docker, Airflow) and in engineering big-data solutions using technologies like Databricks, S3, and Spark. Feature Engineering and Optimization: Develop and maintain ETL pipelines using orchestration tools such as Airflow and Jenkins; deploy scalable streaming and batch data pipelines to support petabyte scale datasets.
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Production experience with developing content recommendation algorithms at scale and familiar with metadata management, data lineage, and principles of data governance. Building streaming data pipelines using Kafka, Spark, or Flink.
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Collaborate with product and business stakeholders: Identify and define new personalization opportunities and work with other data teams to improve how we do data collection, experimentation and analysis.
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Engineer will work closely with Business domain experts and Data Scientists to solve real-world oil and gas midstream problems using advanced analytics, machine learning, and artificial intelligence.
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We integrate off the shelf and new development efforts to sustain and enhance the NMEC architecture and by leveraging cloud-based computing, artificial intelligence (Al), machine learning (ML) and cross-domain transfer systems to provide cutting edge data exploitation, enrichment, triage, and analytics capabilities to Defense and Intelligence Community members.
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The Leidos has an exciting opening for you, as a Cloud Engineer, to play a critical role with design, engineering, development, deployment, and use of software for the DOMEX Technology Platform (DTP) where we support our client’s mission to centralize and standardize Tasking, Collection, Processing, Exploitation and Dissemination (TCPED) of Open Source Intelligence (OSINT) across the DoD and IC enterprise.
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Build and facilitate machine learning across large scale systems and campaigns and ensure deployment and updates in partnership with data engineering. 7+ Years of experience in Data Engineering, Analytics and Machine Learning Systems.
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Kafka, HDFS, Hive, Cloud computing, machine learning, LLMs, NLP & Web development experience is a plus. The Role: Principal Data Engineer. Experience with Continuous integration and deployment.
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We are a multidisciplinary algorithmic trading team with expertise in machine learning, numerical optimization, software engineering, distributed systems, electricity markets, and trading.
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Familiarity with AI (Artificial Intelligence) subsets such as Machine Learning, Large Language Models (LLM) and Natural Language Processing (NLP). Familiarity with AI (Artificial Intelligence) subsets such as Machine Learning, Large Language Models (LLM) and Natural Language Processing (NLP.
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deployment machine learning engineer jobs Title: data engineer Company: Somatus
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