Jupyter Notebook, Keras software library, Machine learning techniques, matplotlib, Object-Oriented programming (OO languages), Python Programming, PyTorch, SaaS, Software Development, TensorFlow, Unix Operating system
At Weights & Biases, our mission is to build the best developer tools for machine learning. Weights & Biases is a series C company with $250 million in funding and a rapidly growing user base. Our platform is an essential piece of the daily work for machine learning engineers, from academic research institutions like FAIR and UC Berkeley to massive enterprise teams including iRobot, OpenAI, Toyota Research Institute, Samsung, NVIDIA, Salesforce, Blue Cross Blue Shield, Lyft, and more.
ML engineers love using our tools day to day as a core part of their workflows, and our user base is growing quickly. We're hiring a Customer Support Engineer to help maintain world-class support as we grow.
Support is a deep part of our culture here at W&B. We believe that the best way to build a great product is to listen to real users and follow their lead to shape our priorities and roadmap. In this role, you will work on a diverse set of problems in a fast-moving environment, collaborate cross-functionally with our team to support our users, and help shape our product.
Your main goal will be to quickly get answers to all user messages. Customers write in with highly technical questions, suggestions, or bug reports, and we need to give them timely responses to fulfill SLAs. In this role, you will act as the face of the company and the first line of support in EMEA time zones. Ideally, we're looking for someone with technical skills who would like to support our customer base. If you've found yourself wanting to be more external but still build programming/machine learning skills, this is the role for you.
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Requirements:
Weights & Biases helps machine learning teams build better models faster. With a few lines of code, practitioners can instantly debug, compare and reproduce their models — architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions — and collaborate with their teammates.
San Francisco, CA, USA
2-4 year