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Experience in Google Cloud Platform, AWS, or Azure. Founded in 2014, Freenome has ~500 employees and more than $1.1B in funding from key investors, such as the American Cancer Society, Andreessen Horowitz, Anthem Blue Cross, Bain Capital, Colorectal Cancer Alliance, DCVC, Fidelity, Google Ventures, Kaiser Permanente, Novartis, Perceptive Advisors, RA Capital, Roche, Sands Capital, T. Rowe Price, and Verily.
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Strong technical background and knowledge of network and systems security, system and network configuration, and application security as well as working with server technologies, cloud-based SaaS applications, and IaaS platforms such as AWS, Azure, and Google Cloud.
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Proficient with one of the following cloud platforms AWS/GCP/Azure. Active contributor to one or more PAAS platforms software such as, Snowflake, Databricks, Confluent, Google BigQuery, Spanner, Redshift, Aurora, Cosmos DB, Redis, MongoDB, ElasticCloud, CockroachDB, Yugabyte, Hashicorp, etc.
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Experience with developing applications for the cloud (AWS or GCP) Twenty/Twenty Therapeutics was formed in August 2020 as a joint venture between Verily (the life sciences arm of Google/Alphabet) and Santen (a leading global ophthalmic company.
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What makes you a strong fit: Experience working with data processing pipelines for training in the cloud (AWS, Azure, Google Cloud, etc.) About the role We are looking for a talented ML Infrastructure engineer to join the Autonomy team and help build state-of-the-art model training, evaluation, and deployment pipelines to aid in the development of the perception modules of our self-driving vehicles.
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Experience with serverless computing technologies such as AWS Lambda and Google Cloud Functions/Google Cloud Run. 4+ years of experience building and maintaining large-scale cloud-native infrastructure (AWS and/or GCP.
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11 or newer) and PythonDemonstrated experience in profiling CPU/GPU code Experience with developing, running, and managing container orchestration systems like Kubernetes Ability to thrive in a fast-moving, collaborative, small team environment with little supervision What makes you a strong fit: Experience working with data processing pipelines for training in the cloud (AWS, Azure, Google Cloud, etc.
$160,000 - $220,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Familiarity with Microsoft Azure, AWS, or Google Cloud/Vertex AI will be a bonus. Familiarity with big data processing and cloud computing will be critical to succeed in this environment.
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In-depth knowledge of cloud platforms, such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). SLAC is a U.S. Department of Energy (DOE) National Laboratory operated by Stanford University and based in Menlo Park, CA. Your specific responsibilities will include:Implement and maintain container-based microservices and serverless applications using Kubernetes, Swarm in IaaS cloud or hybrid on-premise.
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Certification in relevant technologies such as AWS Certified DevOps Engineer, or Google Cloud Professional DevOps Engineer. Strong understanding of cloud platforms such as AWS, or GCP, and hands-on experience with provisioning and managing infrastructure in the cloud.
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Comfortable working in AWS and/or Google Cloud computing environments. MS or PhD with an educational background in chemistry or computational chemistry required. Comfortable working in AWS and/or Google Cloud computing environments.
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FDB runs on multiple cloud providers including Amazon Web Services, Microsoft Azure and Google Cloud. Knowledge and experience in cloud environments, such as AWS, Azure, and GCP.
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Experience with Google Cloud Platform (GCP), Amazon Web Service (AWS) or Azure. You have extensive experience deploying and operating microservices in a cloud environment.
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Familiarity with cloud platforms such as AWS, Azure, or Google Cloud. - Strong understanding of serverless technologies like AWS Lambda and Serverless Framework. Experience with configuration management tools such as Ansible, Puppet, or Chef.
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Experience building scalable SaaS products for large enterprises with cloud platforms, such as AWS, Azure, or Google Cloud Platform, and familiarity with IAM services and features.
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