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This role requires a strong foundation in machine learning algorithms, hands-on experience in model development and deployment, and a passion for delivering measurable results through data-driven approaches.
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A world-class, tight-knit team of good-hearted people across software, machine learning, computational chemistry, medicinal chemistry, and biology. Genesis Therapeutics is building a world-class software team to solve problems in drug discovery through machine learning, biophysical simulation, and computational chemistry.
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LLM Lead with Deployment experience and NLP experience related to Data Science- Deep Learning. Proven experience as a Data Scientist, with a focus on machine learning, LLM, and Deep Learning.
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Design, build, test and maintain the Machine Learning platform supporting Data Science initiatives. Experience with the Azure Ecosystem (Azure Data Lake, Azure Data Factory, Azure Databricks, Azure Machine Learning (AML), Azure Cognitive Services, Azure Storage.
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Transform your career to the next level with GDIT as a Solutions Architect where you can elevate your skills in Data Science, Machine Learning, or leading AI techniques and technology expertise into solutions in support of our Government customers.
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Experience with Machine Learning cloud technologies such as Azure Machine Learning (AML), AWS SageMaker or Spark ML. Experience with end-to-end Machine Learning Engineering lifecycle.
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As a Machine Learning Engineer, you will lead the development and implementation of advanced data engineering solutions to support the deployment and optimization of AI models.
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As a Machine Learning Engineer Co-Op, you will work closely with cross-functional teams, including Data Science, Software Engineering, and Product Management, to develop machine learning models, implement solutions, and ensure the successful deployment and maintenance of these models in production.
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Join our team as a Machine Learning Engineer, where you will play a pivotal role in leveraging data-driven solutions to drive innovation and business impact. Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP) and experience deploying machine learning models using containerization technologies (e.g., Docker, Kubernetes.
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As a Senior ML Ops Engineer at SiriusXM, you will be a key player in our Data Platform Team. Your role will be pivotal in deploying, managing, and optimizing machine learning (ML) models, leveraging advanced tools like Databricks and MLFlow.
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OSARO is a San Francisco-based startup company building machine learning software for industrial automation, to power robots in logistics and material handling centers. You will collaborate with Operations, Software, and Machine Learning teams to research, design, integrate, test and deploy new robotic hardware including: robot cell designs, end effectors, cameras, lighting, HW control devices (PLCs, I/O couplers, sensors, etc), pneumatics, actuators, custom mechanisms.
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Extensive experience in at least one cloud platform (e.g. AWS, GCP, Azure) and associated machine learning services, e.g. Amazon SageMaker, Azure ML, Databricks. Support AAG leadership in extending and growing our machine learning, engineering and analytics capabilities.
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4+ years of experience developing machine learning solutions. Experience with data streaming infrastructure deployment (e.g., Spark Streaming, Kafka, Azure EventHub, or EventBridge.
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Working with Sagemaker, Tensorflow, Pytorch, Triton, Spark, or equivalent large-scale distributed Machine Learning technologies on a modern containerized deployment stack using Kubernetes, Spinnaker, and other technologies.
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Evaluate and recommend new tools, technologies, and best practices for machine learning development and deployment. 10 + years of professional experience as a data scientist or machine learning engineer with proven track record of delivering functional product oriented ML solutions.
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deployment machine learning jobs Title: platform engineer Company: Thermo Fisher Scientific
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