AI Engineer, GenAI Enterprise Accelerator - San Francisco (Hybrid)

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 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