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Well-versed with ML frameworks such as PyTorch or TensorFlow. Experience working with streaming solutions (Kafka, Samza, etc.) Being accompanied by a service dog. The Ads Ranking team is responsible for AI models and infrastructure in the Marketing Solutions business ($5.5B+) at LinkedIn. The team works on diverse infrastructure problems across deep-learning inference with tight SLAs, end-to-end signal latency for content embeddings, large scale (1B+ param) model training, near-real-time signal gathering and low-latency systems for model inference.
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Proficiency in core programming languages and data science packaging: Python, Keras, Tensorflow, PyTorch, Pandas, Jupyter, etc. Experience in industry tooling (AWS, Kafka, K8s, Python Notebooks, ML Orchestration, etc.
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Proficiency in mainstream ML libraries (e.g., TensorFlow, PyTorch, Spark ML, etc.) Experience with mainstream big data tools (e.g., MapReduce, Spark, Flink, Kafka, etc.) Introduce and enforce best engineering practices, uphold engineering quality standards, and streamline machine learning development and deployment processes.
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Establish a clear vision and executable strategies for different machine learning component teams. Solid theoretical background in machine learning and/or data mining. Experience with the advertising industry, real-time bidding (RTB) ecosystem, demand-side platform (DSP), and Ad Manager.
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Proficiency in statistical programming languages such as Python, R, or Scala and experience with ML frameworks like TensorFlow or PyTorch. Experience with real-time systems and large-scale data processing technologies like Spark, Hadoop, or Kafka.
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Proficiency in Python, SQL, PyTorch, or TensorFlow. Experience with Spark, Kafka, and Kubernetes is also desirable. Proficiency in Python, SQL, PyTorch, or TensorFlow. As a Machine Learning Engineer in our team, your primary focus will be on the implementation and evaluation of machine learning algorithms through rigorous experimentation and testing methodologies.
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Hands-on Experience in Big Data and cloud technologies tools, i.e. Hadoop, Hive, Iceberg, Kafka, Spark, Presto, Airflow, Elastic Search, Docker and Kubernetes. Experience with data science packages such as skit-learn, SciPy etc., and Machine Learning frameworks such as TensorFlow, Keras and Torch or LLMs.
Full-timeExpandApply NowActive JobUpdated 3 days ago - UpvoteDownvoteShare Job
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Familiarity with CI/CD, ETL, big data tools, and mainstream ML libraries (e.g., MapReduce, Spark, Flink, Kafka, Docker, Kubernetes, TensorFlow, PyTorch, Spark ML, etc. Familiarity with CI/CD, ETL, big data tools, and mainstream ML libraries (e.g., MapReduce, Spark, Flink, Kafka, Docker, Kubernetes, TensorFlow, PyTorch, Spark ML, etc.
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Golang, Python, gRPC, Kafka, Tensorflow, SQL, NoSQL, AWS, AI, Docker, Kubernetes, Terraform, GitHub Actions, CQRS, DDD, Agile Scrum. Golang, Python, gRPC, Kafka, Tensorflow, SQL, NoSQL, AWS, AI, Docker, Kubernetes, Terraform, GitHub Actions, CQRS, DDD, Agile Scrum.
$200,000 - $220,000 a yearFull-timeExpandApply NowActive JobUpdated 7 days ago - UpvoteDownvoteShare Job
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Experience with big data pipeline technologies such as BigQuery, SnowFlake, Spark, Kafka. Familiarity with machine learning frameworks and libraries (e.g., scikit-learn, Keras, TensorFlow, PyTorch.
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And model serving platforms (TorchServe, TensorFlow Serving, NVIDIA Triton inference server, etc.) You will be building products using technologies such as AWS SageMaker, Tensforflow, Pytorch, LLM, Elastic Search, REST web services, SQS/Kafka, Vector Database, HBase, Machine Learning, and more.
Full-timeExpandApply NowActive JobUpdated 26 days ago - UpvoteDownvoteShare Job
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Proficiency in mainstream ML libraries (e.g., TensorFlow, PyTorch, Spark ML) Experience with mainstream big data tools (e.g., MapReduce, Spark, Flink, Kafka) Proficiency in mainstream ML libraries (e.g., TensorFlow, PyTorch, Spark ML.
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A tech lead role to lead and deliver production-grade machine learning products from end to end to make Samsung Ads a key player in the mobile performance ads market. As a machine learning model engineer of the Samsung Ads Platform Intelligence (PI) team, you will have access to unique Samsung proprietary data to develop and deploy a wide spectrum of large-scale machine learning products with real-world impact.
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Hands-on experience with machine learning-related frameworks (e.g., TensorFlow, PyTorch, model registry, OpenMetadata, ML feature store). Having extensive industry experience with Infrastructure such as Code (Terraform), orchestration tools ( Airflow, AWS Step/Lambda), building CI/CD pipelines using GitHub Actions, and real-time monitoring/alerting frameworks such as Prometheus and Grafana.
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Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, pytorch, Tensorflow) Data Engineering technologies (Ex: Spark, Hadoop, Kafka) 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.
$139,700 - $247,300 a yearFull-timeExpandApply NowActive JobUpdated 4 months ago
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