San Rosenau
AI Engineer, GenAI Enterprise Accelerator - San Francisco (Hybrid)
San Rosenau, San Francisco, California, United States, 94199
AI Engineer, GenAI Enterprise Accelerator - San Francisco (Hybrid)
Weights & Biases
Published 28 Sep 2024Share this job
San Francisco, CA, USARemoteFull TimeRole Highlights
PythonS3Generative AICICDProduct DevelopmentProblem SolvingCustomer SuccessControl SystemsAutomated TestingVCSCloud Environmentsfoundation modelOperationsLLMsDeploymentTransformersResearchGoogle AnalyticsTools, Libraries and Frameworks
OpenAIAWSGCPUnixGitPyTorchNumPyDescription
Weights & Biases aims to provide the best tools for AI developers and has developed a comprehensive AI developer platform tailored for organizations focused on deep learning and generative AI. The AI Engineer role involves collaborating with leading enterprises to integrate and scale GenAI technologies effectively, driving substantial business results. This position focuses on practical implementation of large-scale GenAI solutions and optimizing GenAI pipelines for enterprise clients. The engineer will work directly with advanced software and machine learning teams to address real-world challenges and enhance AI capabilities.Required Qualifications and Skills
Disclaimer: Job and company description information and some of the data fields may have been generated via GPT-4 summarisation and could contain inaccuracies. The full external job listing link should always be relied on for authoritative information.Weights & Biases is a developer-first MLOps platform. Track everything you need to make your models reproducible with Weights & Biases from hyperparameters and code to model weights and dataset versions. Weights & Biases helps your ML team unlock their productivity by optimizing, visualizing, collaborating on, and standardizing their model and data pipelines regardless of framework, environment, or workflow. Used by ML engineers at OpenAI, Lyft, Pfizer, Qualcomm, NVIDIA, Toyota, GitHub, and MILA, W&B is part of the new standard of best practices for machine learning. W&B is free for personal use and academic projects, and it's easy to get started. Run your first experiment in 30 seconds with this quick hosted notebook: \[bit.ly/intro-wb\](http://wandb.me/intro)
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Weights & Biases
Published 28 Sep 2024Share this job
San Francisco, CA, USARemoteFull TimeRole Highlights
PythonS3Generative AICICDProduct DevelopmentProblem SolvingCustomer SuccessControl SystemsAutomated TestingVCSCloud Environmentsfoundation modelOperationsLLMsDeploymentTransformersResearchGoogle AnalyticsTools, Libraries and Frameworks
OpenAIAWSGCPUnixGitPyTorchNumPyDescription
Weights & Biases aims to provide the best tools for AI developers and has developed a comprehensive AI developer platform tailored for organizations focused on deep learning and generative AI. The AI Engineer role involves collaborating with leading enterprises to integrate and scale GenAI technologies effectively, driving substantial business results. This position focuses on practical implementation of large-scale GenAI solutions and optimizing GenAI pipelines for enterprise clients. The engineer will work directly with advanced software and machine learning teams to address real-world challenges and enhance AI capabilities.Required Qualifications and Skills
Disclaimer: Job and company description information and some of the data fields may have been generated via GPT-4 summarisation and could contain inaccuracies. The full external job listing link should always be relied on for authoritative information.Weights & Biases is a developer-first MLOps platform. Track everything you need to make your models reproducible with Weights & Biases from hyperparameters and code to model weights and dataset versions. Weights & Biases helps your ML team unlock their productivity by optimizing, visualizing, collaborating on, and standardizing their model and data pipelines regardless of framework, environment, or workflow. Used by ML engineers at OpenAI, Lyft, Pfizer, Qualcomm, NVIDIA, Toyota, GitHub, and MILA, W&B is part of the new standard of best practices for machine learning. W&B is free for personal use and academic projects, and it's easy to get started. Run your first experiment in 30 seconds with this quick hosted notebook: \[bit.ly/intro-wb\](http://wandb.me/intro)
#J-18808-Ljbffr