AI Engineer, GenAI Enterprise Accelerator - San Francisco (Hybrid)Weights & Biases Published 28 Sep 2024
Share this job San Francisco, CA, USA
Remote
Full Time
Role Highlights Python
S3
Generative AI
CICD
Product Development
Problem Solving
Customer Success
Control Systems
Automated Testing
VCS
Cloud Environments
foundation model
Operations
LLMs
Deployment
Transformers
Research
Google Analytics
Tools, Libraries and Frameworks OpenAI
AWS
GCP
Unix
Git
PyTorch
NumPy
Description 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 SkillsDisclaimer: 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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