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5+ years experience in designing, developing and deploying production-grade machine learning solutions in NLP (NLTK, Spark NLP, spaCy, HuggingFace, Flair, NLTK, etc) for real-world business problems.
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Design, develop, and optimize data processing workflows using Apache Spark for large-scale data analytics and machine learning applications. Minimum of 6 years of experience in data engineering roles, with a focus on Apache Spark and big data technologies.
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You will need strong data engineering capability particularly with Python, SQL, AWS tools including Glue, Lambda and Apache Spark. · Deep knowledge of Python, SQL, Glue, Lambda and Apache Spark.
$70 - $85 an hourExpandApply NowActive JobUpdated 2 months ago - UpvoteDownvoteShare Job
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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.
$124,800 - $220,800 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Will work closely with Machine Learning Engineer and Quantitative Researcher. Knowledge of distributed computing (e.g. Apache Spark, Dask) The Data and AI team primarily uses Python to manage data engineering, machine learning, and quantitative tasks.
$175,000 - $250,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Hands-on experience using Databricks or Apache Spark (i.e., PySpark, SparkR, or Scala) and understanding best practices. Prior experience as a Machine Learning Engineer, Data Engineer, Software Engineer, or similar role.
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Practical cloud native experience in AWS (EC2, S2, Glue, AWS Lambda, Athena, RDS, SNS), proficiency with Python, PySpark, Machine Learning disciplines. Strong experience with distributed computing frameworks such as Apache Spark, specifically PySpark and event driven architecture using Kafka.
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Our technology stack includes Java and Python as well as a wide range of internal tools built on top of Apache Spark, TensorFlow, and other Big Data technologies. As a Lead Engineer on the AI Ops team, you will be responsible for building the next-generation routing engine for the Internet using Data Science, Machine Learning (ML), and Artificial Intelligence (AI.
$164,200 - $241,700Full-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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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.
$157,700 - $213,800 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Familiar with Spark, MLLib, Databricks MLFlow, Apache Airflow and similar related technologies. Extensive experience with scientific libraries in Python (numba, pandas) and machine learning tools and frameworks (scikit-learn, tensorflow, torch, etc.
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You Have: 8+ years of experience in batch and streaming ETL using Spark, Python, Scala, Snowflake or Databricks for Data Engineering or Machine Learning workloads. 5+ years orchestrating and implementing pipelines with workflow tools like Databricks Workflows, Apache Airflow, or Luigi 3+ years of experience prepping structured and unstructured data for data science models.
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Apache Spark AWS AI Services AWS S3 AWS SageMaker Azure Cognitive Services Azure DevOps Azure Machine Learning Dataiku GCP Vertex AI Vertex AI. Machine Learning Engineer responsibilities include creating machine learning models and retraining systems To do this job successfully you need exceptional skills in statistics and programming If you also have knowledge of data science and software engineering we’d like to meet you your goal will be to shape and build efficient self learning applications.
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Desired certifications to include: Certified Analytics Professional (CAP) Data Science Council of America (DASCA) Principle Data Scientist (PDS) Microsoft Certified Data Scientist or AI Fundamentals Tensorflow Developer SAS Certified Professional in AI and Machine Learning.
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We are looking to add a Machine Learning Engineer to our team. Technologies: Apache Spark. Publications / patents in the field of machine learning. The work we do at Cherre touches multiple aspects of ML - the ideal candidate should be familiar with such diverse topics as deep learning, graph algorithms, and named entity resolution.
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Experience with building data pipelines in getting the data required to build and evaluate ML models, using tools like Apache Spark or other distributed data processing frameworks. We are currently seeking a Machine Learning Engineer for our client in the Financial Services domain.
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