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Proficiency in using Google Cloud Platform (GCP) tools and services for machine learning, such as Vertex AI, BigQuery, and TensorFlow. As a Machine Learning Engineer, you will play a key role in developing and deploying machine learning models on Google Cloud, with a focus on generative AI and large language models, primarily text-based.
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Understanding of machine learning concepts and experience with ML platforms like Azure Machine Learning or TensorFlow. Familiarity with distributed computing frameworks, such as Apache Hadoop and Apache Spark.
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Good foundation in Machine Learning (ML), Deep Learning, Large Language Models (LLM) and Natural Language Processing (NLP). Experience with scientific libraries in Python (numba, pandas) and machine learning tools and frameworks (scikit-learn, tensorflow, torch, etc.
$144,000 - $169,000 a yearFull-timeExpandApply NowActive JobUpdated 21 days ago - UpvoteDownvoteShare Job
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Experience with cloud-based machine learning platforms such as Amazon SageMaker or Apache Spark-based solutions like Databricks is advantageous. Cytokinetics is dedicated to advancing the frontiers of technology through the strategic application of artificial intelligence, machine learning, image analysis, and computational biology and chemistry.
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The adjacent areas of major focus are Machine Learning Infrastructure and workflow, Experimentation Platform, Knowledge Graphs and various Data Science and Analytics related tooling. Apache Kafka, Apache Flink, Apache Spark, Trino (Presto), Apache Airflow/Dagster, Apache Superset, AWS S3, Snowflake, Amplitude, CDP (e.g. Segment.com.
Full-timeExpandApply NowActive JobUpdated 30 days ago - UpvoteDownvoteShare Job
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In our Data science track we prepare you to get job as one of the following: Python developer, a data analyst, data visualization developer, a statistician, a machine learning engineer or a data scientist.
Full-timeExpandApply NowActive JobUpdated 10 days ago - UpvoteDownvoteShare Job
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Dexian is seeking a Machine Learning Engineer/AI Engineer for an opportunity with a client located in San Francisco, CA. Data Science / Machine Learning => 5/10 experience.
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Key Responsibilities:- Design, develop, and deploy advanced machine learning models and algorithms, leveraging deep learning frameworks such as PyTorch and TensorFlow. machine learning We are actively seeking a Senior Machine Learning Engineer to contribute to innovative projects for a prestigious Global Financial Services organization.
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We are building state-of-the-art probabilistic models and engineering marvels to serve our customer needs by leveraging cutting edge machine learning, deep learning, reinforcement learning, natural language processing, entity-relationship extraction and knowledge representation.
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Proven experience as a Data Scientist, Data Engineer, Machine Learning Engineer, Data Principal, Customer Intelligence Analyst, CX Analytics Analyst, Quantitative Analyst, etc., with a strong portfolio demontstrating successful outcomes and the ability to influence corporate strategy through story-telling.
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Strong background in artificial intelligence and machine learning, including experience with deep learning frameworks like TensorFlow or PyTorch, for evaluating advanced document processing algorithms and models.
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The Adobe Experience Platform handles petabytes of data on behalf of organizations allowing them to have a centralized and standardized data platform applying data science and machine learning (ML) to spectacularly improve the design and delivery of rich, personalized experiences.
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We are currently sourcing for a Lead Software Engineer, Machine Learning to work at Fidelity Investments. As a Machine Learning Engineer, build and maintain large scale ML Infrastructure and ML pipelines.
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As a AWS Data Architect,you will beresponsible for leading a team of data engineers in the development andmaintenance of scalable data pipelines and implementing solutions in graphdatabases and machine learning.
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Proficiency in Apache Airflow, ECS/EKS,Kafka, Docker, Terraform, and disaster recovery strategies. Oversee the implementation and use ofdata storage solutions, including PostgreSQL, MS SQL, Amazon RDS/Aurora,Databricks, and Snowflake.
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