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Experience using engines such as Apache Spark or Hadoop to analyze large datasets. The Data Scientist role on the Marketing Optimization Analytics team is responsible for building end-to-end Machine Learning based Analytic Data Products that will directly impact short-term and long-term marketing strategy.
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Big Data: Apache Spark, YARN, Livy, ZooKeeper, Ranger, Knox, Polybase. Machine Learning: SparkSQL, Java, Scala, Python, Jupyter. Big Data: Apache Spark, YARN, Livy, ZooKeeper, Ranger, Knox, Polybase.
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If you're passionate about building scalable pipelines for state of the art production machine learning models and thrive in a fast-paced, collaborative environment, we'd love to hear from you.
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Experience in operating a workload/workflow management system (e.g. Slurm, Apache Airflow/Beam/Flink) Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments.
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Data Sources: MongoDB, Cloudera, Teradata, Oracle, DB2. Containers: Docker, Kubernetes (k8), OpenShift, cloud storage, flannel. Familiarity with Linux script-based development environments and tools (Perl, Python, Ruby, PHP, etc.
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More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark, Delta Lake and MLflow.
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Our backend teams span many domains, from our core compute fabric resource management infrastructure, to service platforms, to machine learning infrastructure. Resource management infrastructure powering the big data and machine learning workloads on the Databricks platform in a scalable, secure, and cloud-agnostic way.
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Experience with Apache infra, CNCF-stack, and cloud-native development, and ideally with Rust/Golang. Spice AI is also building the Spice.ai Cloud Platform ; building blocks for data and AI-driven applications, including managed, cloud-scale Spice.ai OSS. The Cloud Platform enables developers to compose real-time and historical time-series data, high-performance SQL query, machine learning training and inferencing, in a single, interconnected AI backend-as-a-service.
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Familiarity with databases (e.g. MySQL, MongoDB), web servers (e.g. Apache) and UI/UX design. Experience managing common Azure services (Virtual Machine, Database, Storage, Virtual Network, Azure Resource Manager, etc.
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Develop and adjust benchmarking suites and methodologies for various software technologies such as Java, Golang, open-source database, Apache Kafka, and machine learning applications. Deep understanding of Linux kernel, compilers, middleware technologies (Java, Golang, TensorFlow, PyTorch), and container environments (Kubernetes, Docker.
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Familiarity with advanced statistical and machine learning techniques such as clustering, gradient boosting machines (GBM), support vector machines (SVM), neural networks (e.g., ANN, RNN, CNN), and other deep learning algorithms is a plus.
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Experience with large-scale data processing systems like Apache Spark and Flink. Senior Software Engineer - Machine Learning Platform page is loaded. You will report to the Machine Learning Platform Engineering Manager.
$180,000 - $225,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Additional expertise in application specific benchmarks for databases, event streaming, Golang, etc would be beneficial. Analyze application performance across various hardware components (CPUs, GPUs, storage and memory subsystems) in both virtual machines and bare metal in order to optimize for cost, performance and utilization.
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Experience operating large scale data processing systems such as Apache Spark, Apache Flink. We use Deep Learning (the most sophisticated machine learning) to build custom algorithms for digital marketers to help grow their customer base.
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Mosaic AI is hiring experienced machine learning platform engineers to build out our generative AI platform for the ML model development lifecycle including pre-training of large language models (LLMs), fine-tuning, evaluation, and serving.
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